Final Report Summary - CRANESINSPECT (Continuous Reliable Advanced Novel Efficient Structural Health Monitoring system for crane inspection applications)
Executive Summary:
European seaports handle around 18% of worldwide container throughput and approximately 15% of global seaborne trade. Ship to Shore (STS) cranes form the most valuable infrastructure in seaports and are responsible for loading and unloading containers from container ships. Most modern cranes can achieve an average of 30-50 container moves per hour and in an effort to increase the productivity of ports there is a constant trend towards increasing the number of moves per hour per crane (i.e. 80 moves per hour are theoretically possible). STS cranes are being used in high cycle applications; the weight of the containers combined with the continuous operation in extreme environmental conditions pose substantial stresses to the structural condition of cranes.
Over the years this situation has led to numerous crane accidents, with the majority being fatal, leading the Health and Safety Executive (HSE) to issue an update on crane safety in ports. According to the HSE update on crane safety, critical structural crane members should be examined periodically for fatigue cracking; these cracks are characterised by low probability of detection and can only be detected when they reach a significant, often dangerous, size.
Current methods of inspection involve visual, magnetic particle and ultrasonics; however all these methods require complete paint removal, significantly increasing the inspection cost. Hence, it is evident that the container transport maritime industry urgently seeks a Structural Health Monitoring (SHM) system in order to ensure the structural integrity of their crane infrastructure.
The CRANESInspect system is a SHM system that offers continuous monitoring of cranes in real-time, with the ultimate target to prevent catastrophic and fatal accidents. The system is characterised by reduced human intervention and operator subjectivity offering a cost-effective solution for the inspection of cranes. The system employs a hybrid Non Destructive Testing (NDT) technique that is based on the techniques of Acoustic Emission (AE) and Long Range Ultrasonics (LRU). Towards this direction, the system employs a dual capability sensor array that acts as an AE receiver, and a LRU transmitter. The transducer array has been designed to operate under harsh environmental conditions and is capable of conforming to any irregular surface that is encountered on the crane.
The CRANESInspect system has been equipped with automated defect detection and sizing software. The CRANESInspect software is capable of processing both AE and LRU signals, and the LRU method is triggered based on the information provided by the AE technique. The software has been supported with Artificial Neural Networks (ANNs) which are responsible for defect classification through analysing the AE signals. Once a defect is identified the software can provide information for the defect location through a customised trilateration technique. Immediately, the LRU sensors are activated providing information regarding the defect size.
The CRANESInspect system is capable of demonstrating wireless transmission capabilities. Specifically, a wireless communication network, based on the IEEE 802.11n wireless transmission protocol, has been developed and tested to transmit data from the sensors to the main unit.
Project Context and Objectives:
The CRANESInspect project targeted at developing a continuous, real-time and non-invasive approach for monitoring failures of cranes. Towards this direction, an advanced integrated Structural Health Monitoring (SHM) system was developed to continuously monitor Ship to Shore cranes.
During the project, a hybrid Non-Destructive Testing (NDT) technique, based on Acoustic Emission (AE) and Long Range Ultrasonics (LRU), was developed. The technique was supported by new sensors systems specifically developed to detect structural damage and cracks in the main frame caused from fatigue and corrosion.
The CRANESInspect monitoring and prevention system features important advantages, such as:
- Real time monitoring of the structure to ensure safety
- Prevention of a catastrophic and fatal accident
- Cost-effective and total solution for inspection/maintenance
- Reduction of human intervention and operator subjectivity
- Highly sensitive and accurate Acoustic Emission (AE) technology
During the project, the consortium placed effort to advance the final CRANESInspect system further than any similar system in existence toward a complete and commercially competitive solution. As a result, during the CRANESInspect project the following objectives had to be met:
• Clarification of the tasks and aim of the project, based on the expectation of the End-User and the SMEs
• Documentation of the specifications for each part of the final CRANESInspect system
• Identification of the defect types and critical parts of large cranes
• Development of a theoretical model to understand the properties of the guided wave in large complex lattice structures
• Numerical simulation of the defect detection performance of the developed transducers on critical parts of the crane
• Simulation of the interaction of defects with the LRU
• The ability of the AE/LRU techniques to improve the probability of defect detection, location and sizing
• Design the new generation of AE/LRU sensors/transducers specifically designed for the CRANESInspect CM application
• Development of new AE/LRU transducers and transducer arrays designed to withstand harsh environments and to be mounted on various crane components
• Investigation of in-service noise parameters and external or environmental acoustic interference factors using initially, measurements on acoustic emission from in-service cranes in order to identify the requirements and specifications of the signal pre-conditioning module
• Design and development of the signal pre-conditioning module
• Design and implementation of signal processing routines on selected hardware
• Preliminary research on neural network architectures has been conducted to select the optimal scheme for detecting and sizing faults from AE and LRU sources
• Development of automated fault detection and sizing software
• Development of working AE/LRU transducer prototype with signal pre-conditioning capabilities
• The aforementioned prototype should be able to transmit data through a wired (or wireless) protocol
• Integration of the CRANESInspect prototype system
• Prototype testing and validation
• Training of the SMEs on the use of the CRANESInspect system
• Promote the developed technique and maintain a technology transfer program that ensures successful implementation and provides a marketing platform for the consortium SMEs
• Ensure a widespread use and awareness of the developed SHEM technology this will contribute to the increased crane safety
• To monitor and record progress and delivery of work packages
• To inform the Commission of progress made
• To optimize the power of the project team
• To disseminate the key project results to maximize the benefit to Europe
• To ensure best use is made of project resources; develop and maintain the project’s website
• To resolve any problems and disputes that may arise during the project and cannot be solved at individual work package level
Initially, the CRANESInspect consortium identified and formed the key technical requirements for the continuous structural health monitoring of cranes in respect to the wider field of port equipment as determined by the End-User of the project, i.e. Cargotec. All the priorities, constraints and key crane operating requirements were defined, and formed the basis of the research and technological development of the project; these items were continuously updated during the project and according to the needs that evolved.
A Finite Element Analysis was performed in order to identify the most critical components of a typical Ship-to-Shore crane structure. The results indicated that the reinforcing plates were the most prone to defects areas of the crane. Specifically, it was shown that the reinforcing plates were characterised by 91% probability of damage occurrence in contrast to the main I-beam (3%), the square cross section lattice (6%), and the cylindrical boom (1%). These results were in good agreement with the maintenance record of the crane operator, according to which the reinforcing plates were the most defected crane components.
A Semi Analytical Finite Element Method (SAFEM) was developed to generate ultrasonic guided waves in the previous structures, which are characterised by complex cross-section geometries. The analysis utilised the obtained phase and group velocity dispersion curves for the propagation of the guided waves in an I-Beam. The method of transient analysis that presents the propagation of the LRU (Long Range Ultrasonics) in an I-Beam with arbitrary cross section was described. In addition to dispersion curves, SAFEM was carried out for a complete modelling of the modal propagation in complex cross section structures. Modelling analysis of LRU based on the SAFEM method was performed. The analysis focused on the use of LRU in order to inspect the identified critical crane components, which involve:
- The main supporting reinforced I-Beam,
- The square beam forming the lattice,
- The cylindrical boom,
- The joining plate
According to the End-User, the welded areas of the joining plates to the main supporting I-beam are extremely prone to corrosion. The main supporting reinforced I-beam is a complex cross section structure, where the guided waves can propagate however, they were highly attenuated because of the number of junctions and welded supports that are encountered on the direction of the wave propagation. In order to decrease this effect, low frequency of operation was considered. Relatively short square cross section beams (4.6m) are forming the lattice of the crane. These are joining the main I-beams and the spacing between two junctions on the main I-beam is 3.5m. The modelling results showed efficient propagation along this type of structure. Additionally, the cylindrical boom is equivalent to a hollow cylinder; this is recognized to be a very efficient support of LRU and it was proved that Longitudinal and Torsional LRU can propagate with very low dispersion effect. The analysis of Dispersion Curves (DC) gave the phase velocity for the propagation of LRU in the selected structures. Dispersion curves enabled the selection of less dispersive modes facilitating an efficient defect investigation. In order to investigate these DC, the simulation work considered 3 main parts: (1) Modelling of the DC, (2) Analysis of the DC, and (3) Selection and optimisation of the operating conditions.
The design selection procedure of the piezoelectric AE sensor element was provided along with the design selection criteria. The preliminary experimental testing of the initial sensor element was described, and various experimental designs were proposed for the transducers, in order to facilitate the array development. Additionally, the design and optimisation of the transducer array was performed with respect to defect detection performance. The developed transducer array employs dual capability AE/LRU sensors that act as AE receptors and LRU transmitters. The optimisation of the design of the transducer array was performed through the theoretical models that were developed during the first period of the project. It was shown that enhanced surface wave can travel along the plate edge and defect boundary; hence producing a strong signal correlating to the presence of defects. Additionally, it was shown that the Probability of Detection (PoD) of the 6-sensors array is lower than the PoD of the 3-sensors array. Two different focusing techniques were developed and tested; a) Time Delay Focussing (TDF) and (b) Time Reversal Focussing (TRF). Theoretical and experimental results indicated that TRF technique is characterised by improved Signal to Noise (S/N) ratio, increasing the PoD. The optimised transducer array was developed into a mountable form. Particularly, two transducer array designs were implemented; (a) a transducer array able to be mounted to the reinforcing plates, and (b) a transducer array able to mounted to any crane component. The design drawings of both transducer array prototypes were provided along with instructions related to their deployment on the crane components. The experimental validation of the characteristics of the transducer’s array was performed through; (a) Experimental testing through leak break AE waveforms in steel plates, and (b) Experimental testing of LRU focussing capability.
The experimental design and test specifications of the CRANESInspect system were analytically presented. With significant help from the End-User of the project, i.e. Cargotec, and its’ customer, Rotterdam Shortsea Terminals (RST), the test-bed crane was specified and its’ technical characteristics (components geometries, dimensions and technical drawings) were described. RST provided their crane maintenance record, along with photos from their maintenance activities, confirming that the reinforcing plates and their welded areas are the most prone to defects structures. The CRANESInspect system was implemented on the working crane in Rotterdam. The sensor installation, data acquisition and logging, live and post data processing were reported. Preliminary measurements on AE from the operating crane were used to test the influence of the in-service parameters to the overall system operation. Moreover, the requirements of the signal pre-conditioning module based on info from previous related work packages were investigated.
The second field trial on the crane revealed that the CRANESInspect system was successfully integrated to the monitored crane, since two of the most important crane-operating parameters, i.e. torque and motor speed, were successfully recorded along with the AE signals. The correlation and relationship between several features of Acoustic Emission (AE) signals and the operating parameters of the monitored crane was established. Various features of the AE signals were extracted and a number of different correlations of the parameters were plotted. Additionally, the filtering of the acoustic noise from measured AE signals took place. Specifically, high-pass Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters were designed and implemented on real AE signals in an effort to filter out acoustic noise with frequency content lower than 50 Hz. The analysis indicated that FIR filters presented better filtering behaviour than the IIR filters.
It should be noted that a significant amount of AE data were recorded during the field trials, which were utilised towards the training of the Artificial Neural Networks (ANNs). In the CRANESInspect system defect identification is achieved through the employment of ANNs which were specifically designed according to the nature and morphology of the AE signals. Initially, an extensive literature research was conducted against the structure of the fault detection software. It was decided that the fault detection software should be divided in three discrete modules; (a) the signal denoising/preconditioning module, (b) the feature extraction / selection module and (c) the defect recognition module. Then, as part of the pattern recognition software a literature research was performed aiming to identify the use of neural networks as defect recognition components in LRU and/or Acoustic Emission applications. The results indicated that Error Back Propagation ANNs were the most commonly employed structures, and the implementation of this architecture was investigated in the CRANESInspect project. The ANNs were trained with defected and non-defected signals; specifically the AE signals from the crane monitoring field trials were used as indicative of non-defected samples, whereas AE signals acquired during the pencil lead brake experiments on the reinforcing plate were used as indicative of the existence of crack propagation. Defect localisation was achieved through the widely employed trilateration technique. The theoretical background and the software block diagram of the defect location feature were provided. The defect location software was verified against numerous experiments that presented a good repeatability factor. Additionally, the theoretical background and software block diagram of the focussing feature were described. The capabilities of the focussing feature were verified against numerous focussing experiments. Particularly, it was shown that acceptable Probabiltiy of Detection values (PoD) were obtained proving that defects of 1mm to 5mm can be detected by the CRANESInspect system.
Additionally, the development and testing of the wireless transmission protocol was performed. The wireless transmission protocol was specifically developed according to the requirements of the CRANESInspect project. Two protocols were selected: (a) IEEE 802.11n was selected for the communication between the sensor and the control center, i.e. CRANESInspect hardware box, and (b) IEEE 802.11ac was selected for the communication between the control center and the user. Based on the employed protocols the hardware components were defined and a prototype wireless module was developed. Additionally, the wireless software was designed and developed to operate both in Windows and Linux. The performance and capabilities of the wireless system were verified through carefully designed experiments both in indoors and outdoors environments. It was observed that under bad environmental conditions the calculated signal strength loss, approximately 24% for a distance of 100m, allowed the wireless communication between the sensor and the main unit (control center). Finally, the packaging of the wireless module took place according to the Ingress Protection (IP) ratings, in order to protect the module against the environmental conditions encountered on the crane.
The integration of each component into the final CRANESInspect system took place. The testing of the CRANESInspect system was performed both in the laboratory and on the crane. Additionally, detailed instructions on the correct use of the system were formulated, accompanied by safety precautions that should be taken into account during the installation, assembly and operation of the prototype CRANESInspect system.
Project Results:
The Research and Development work in the CRANESInspect project is reflected in the following project results:
1) AE/LRU sensors
2) Dual capability transducers and transducer arrays
3) CRANESInspect hardware
4) Wireless module
5) Fault detection software
6) Complete CRANESInspect Structural Health Monitoring (SHM) system
In this section, information and characteristics of each component are presented.
1) AE/LRU sensors
The design of the AE sensor is presented in Figure 1. A number of PZT (piezoelectric) materials were investigated and the analysis identified the Lead Zirconate Titanate PZT-5H (d33=5.93e-10 C/N, density=7500 Kg/m3) as the best fit to the CRANESInspect application. Electrode layers were attached to the PZT element and connected to the main potential input. The electrodes were made of Nickel (E=200 GPA, density=8900 kg/m3). In addition, a cover plate was employed in order to protect the PZT element and the electrode plates. The cover plate material was made of Alumina (E=300 GPA, density=390 Kg/m3).
The effect of different components on the behaviour of the sensor in the considered frequency bandwidth was investigated through modelling. The following study will focus on the frequency range up to 1 MHz. The employed excitation potential was a tone burst of 1 cycle at 100 kHz. A wideband frequency range was excited (up to 0.5 MHz). Figures 2 and 3 of Table 1 present the movement of the cover plate as a result of the applied voltage. It is evident that most of the resulting deformation is taking place in the Z direction however minor X and Y strain are obtained. Moreover, the waveforms show that there is a high frequency displacement, this displacement is reflected in the X and Y directions (Figure 4).
The selected sensor design was experimentally tested on steel plate structures using pencil lead brake tests of 0.5mm diameter. The results indicated that the sensor elements are delivering correct and coherent output level comparable to commercially available AE sensors from Vallen, which were taken as a reference (Figure 5).
The design and the prototype of the dual purpose sensor are presented in Figure 6. The sensor consists of two sides, one dedicated to reception and the other to transmission. A thin layer of cork is used to isolate both sides ensuring that the transmitter doesn’t resonate in the backing mass of the receiver.
2) Dual capability transducers and transducer arrays
In the CRANESInpsect two different transducer array designs were implemented; (a) a transducer array for reinforcing plates and (b) a transducer array for any flat surface. The design and prototype of the mountable AE/LRU transducer array for the reinforcing plate is presented in Figure 7. It is important to note that the array was designed in such a way so that the number and position of sensors can be easily modified. Hence, according to the application the number of the AE/LRU sensors can be adjusted. In Figure 8, two pictures of the transducer array mounted on a typical reinforcing plate encountered on the crane under investigation are shown.
The transducer array design for any flat surface is presented in Figure 9. Similarly to the transducer array for the reinforcing plate, the number and position of sensors can be easily modified.
Figure 10 below presents the mounted transducer prototypes on the main I-beam of the monitored crane. The sensors can be easily mounted and stay attached on the crane via magnetic force. Additionally, the necessary preparation for mounting the sensors has been kept at minimum levels. Specifically, sanding with a fine sand paper is required for a basic cleaning of the structure followed by the application of liquid coupling gel which acts as a coupling medium between the sensors and the surface under investigation. For the sensor installation on the main I-beam, presented in Figure 10, the trolley of the crane has been used in order to facilitate the installation procedure as well as for safety reasons.
Once the sensors are mounted on the crane, a cable packaging procedure is followed in order to minimise the effect on the normal operation of the crane, maximising in this way the durability of the sensors and hence the efficiency of the condition monitoring application. The mounted sensors on their final form with the packaged cables are presented in Figure 11.
3) CRANESInspect hardware
The Condition Monitoring system developed for this project is based on several devices from National Instruments embedded in a bespoke enclosure. It includes specific components for monitoring such as AE and LRU equipment. Figure 12 shows a basic disposition of the system’s components and connections. Following on from the general hardware diagram, a brief description of the components used to assembly the system is listed.
• Chassis PXIe-1082: The NI PXIe-1082 eight-slot chassis features a high-bandwidth backplane to meet a wide range of high-performance test and measurement application needs. It accepts PXI Express modules in every slot and supports standard PXI hybrid-compatible modules in up to four slots. The chassis also incorporates all the features of the latest PXI specification including support for both PXI and PXI Express modules and built-in timing and synchronisation features.
• Controller PXIe-8135 RT: The microprocessor and Arithmetic/Logic Unit (ALU) are embedded in the device which will manage both the operation of different modules attached to the chassis and the chassis itself. This is the most important device in the system and the one which will have the most demanding capabilities. The system will be operated by LabVIEW RT which is a real time operative system that enhances the reliability and real time operation of the different software processes running in the system.
• Data acquisition card PXIe-6368 (AE): This data acquisition card was selected in order to acquire signals for AE. It is capable to run 16 simultaneous analog inputs at 2MS/s which fits with the requirements for AE measuring.
• Data acquisition card PXI 6220: This data acquisition card was chosen to acquire parametric signals. It is a low cost card capable to run 16 analog inputs at 250KS/s.
• Decoupling circuits: The function of these circuits is to power the AE sensors preamplifiers and to decouple the DC-voltage from the AE signal. The dimensions are 35x60x65mm (HxWxL not including BNC connectors) and the weight is 250g. Finally, the temperature range is from -5°C to 85°C.
• Power amplifier: In order to excite the LRU transducers, a power amplifier was required. This device increases the maximum output range from the PXIe-6368 card from ±10V to ±150V. With this wider range, the excitation is possible and then the signal can propagate and be recorded by the receiver sensors. The MK4 Teletest® from PI power amplifier was modified and reassembled in order to be able to communicate and work with the National Instruments equipment. Some modifications were done as suppression of the D/A converter and modification of the switching circuitry. Figures 13 and 14 present an exploded and a render view of the CRANESInspect hardware. Finally, in Figure 15 a photograph of the prototype with connections is presented.
4) Wireless module
The hardware components of the developed wireless system includes; (a) a processing unit and (b) a wireless transmitter. The processing unit was specifically selected to reduce the processing load of the Control Centre and temporarily store the data acquired by the sensor before the wireless transmission. It is reminded that a “Wireless node-Control Centre” architecture has been adopted in the CRANESInspect system, with the Control Centre located on the crane and being responsible for the data communication to the. In this way, the quality of the data communication scheme between the sensors and the main CRANESInspect unit is significantly improved.
The wireless module is presented in Figure 16, and it has been specifically designed to host the processing unit, the wireless transmitter and the Analog to Digital Converter (ADC), and is capable of accommodating up to four (4) sensors. The Computer Aided Design (CAD) model of the wireless box is presented in Figure 16(a). It is highlighted that during the encapsulation of the wireless box special consideration has been placed towards ensuring that the box is totally protected against dust, contact and the ingress of water. Towards this direction, the choice of the suitable enclosure was performed according to the Ingress Protection (IP) and National Electrical Manufacturers Association (NEMA) ratings. Finally, the prototype of the wireless node is presented in Figure 16(b).
5) Fault detection software
The CRANESInspect prototype system has been equipped with a software responsible for the control of the system and the detection and localisation of defects The software has been developed in the LabView platform and the front panel is shown in Figure 17. It consists of three main parts, i.e. AE localisation, GW focusing and the Artificial Neural Networks (ANNs).
Artificial Neural Networks
The ANN was designed to classify the recorded AE signals in defected and non-defected; hence a pattern recognition ANN has been developed. The scope of the ANN is to classify inputs into a set of target categories; defected and non-defected. The ANN architecture that was selected has a two-layer feed-forward network with sigmoid hidden and output neurons. The ANN can achieve very good classification rate, if it is equipped with enough neurons in its hidden layer. A series of tests have been performed in order to select the best ANN hidden layer structure for the CRANESInspect application. Such a test is presented in Figure 18 which presents the ANN software interface. It can be seen that the interface consists of 3 graphs: (a) the training label graph, (b) the training error graph, and (c) the classification result graph. Specifically, Figure 18 presents the defect classification results of the network for a Testing Data array where the first 7 rows were consisted of signals originating from defected samples, whereas the next 6 rows were consisted of non-defected signals. It can be seen that the network can effectively classify the defected samples from the non-defected ones, behaviour that is also reflected from the training error graph, where training error less than 10-4 was achieved.
Defect location
In Figure 19, a screenshot of the software is shown. The interface consists of two panels and one graph. The panels are used to enter the parameters of the acquisition and localisation and the graph shows the localisation of the defect. Defect location is performed through the trilateration process which determines absolute or relative locations of points by measurements of distances, using the geometry of circles. With the trilateration method, all the parameters are included and solved in the LabView software, including the equations and the locations of the sensors. Finally, when the coordinates are found and the localisation result is represented in a graph as shown in Figure 20. In the localisation graph, the sensors location and the AE source localised are presented as dots and the distance between the different sensors and the defects are presented as lines of different colours for each sensor.
Defect Focussing
The software for focusing was developed in the LabView platform. In Figure 21, the front panel of the focusing part of the software is shown. It consists of two parts, transmission and reception; one graph and one parameter panel is displayed for each part. The transmission parameters such as amplitude, cycles per pulse and frequency can be modified within the software depending on the test specifications.
6) Complete Structural Health Monitoring CRANESInspect system
A detailed schematic of the system hardware is presented below (Figure 22). The dotted line shows the system enclosure boundary. The components surrounded by the dotted line are placed into the system enclosure boundary. The external components are shown outside this limit.
Potential Impact:
A detailed business plan for the CRANESInspect system was developed during the project; the purpose of the business plan was to ensure the successful commercialisation of the system through the early identification of the most appropriate exploitation routes for the CRANESInspect system. A SWOT analysis was initially performed to identify the Strengths, Weaknesses, Opportunities and Threats of the CRANESInspect system. This analysis is presented in Figure 23, below.
The sale price of the complete CRANESInspect system was calculated based on a monthly rate that covers the cost of the hardware and consumables in addition to the service and reporting costs. The sale price also depends on the monitoring full duration period. In Table 2 and Figure 24 the monthly costs of the CRANESInspect product are shown, depending on the number of units sold and on the full operation time. It should be noted that the cost of a single CRANESInspect system has been calculated at €21,060.
Figure 24 clearly indicates that the monthly operating costs of the CRANESInspect system are inversely proportional to the operation time of the system. For a continuous monitoring period of 2 years (24 months) the total system cost is shown in Table 3. The 20% and 30% profit and the break-even point for these percentage profits on the CRANESInspect costs are also shown. In Figure 25 the total cost and profits at 20% and 30% are shown. Additionally, it can be seen that the profit made over 24 months from the sales of 6 complete CRANESInspect systems are calculated at approximately €43,000 (thus €21,500 per year). Besides the complete CRANESInspect system, each SMEs will be in a position of gaining profit from the commercialisation of each system component. The products will include the AE/LRU sensors, the Digital Signal Processing (DSP) hardware and the operating defect detection software. Therefore, the CRANESInspect project will result in commercialised products that will be sold by the IP owners as these are displayed in Table 4.
Each of the commercial products will have a defined primary market individually (please refer to Table 5) and will also be sold as a component of the fully-integrated CRANESInspect system which will be sold by the IP owners (i.e. Tangent, Innotec and TTest) to interested crane operators, crane manufacturers and inspection services companies. Table 4 depicts the sales of the CRANESInspect commercial system five years after project ends. The overall price of the CRANESInspect system presented in Table 4 refers to the monitoring application of a STS crane with 16 reinforcing plates (as is the case with the test-bed of the CRANESInspect project). In accordance with the Deliverables 3.1 and 3.2 it is assumed that 3 AE/LRU sensors are needed for monitoring each plate and the price of each dual sensor is taken equal to €100. In order to calculate the costs of the CRANESInspect hardware the following price estimations were assumed: price of the PXIe data transmission and acquisition system equals to €2,500 per unit; price of the power amplification unit for signal transmission equals to €600 per unit; price of the decoupling units assumed at €60 each; price of the pre-amplification units for signal transmission assumed at €60 each. As a result, €240 will be need for the pre-amplifier and decoupling circuit per plate, resulting in €3840 for the monitoring application of 16 plates. Also, the DSP CRANESInspect hardware includes the wireless communication module which cost €230 and is capable of accommodating up to two plates; thus the cost of the wireless box for the specific application was calculated at €1,840. Additionally, the price of the CRANESInspect software was considered at €10,000.
It should be noted that the consortium SMEs have arrived at the prices for the CRANESInspect products by analysing the prices of state of the art systems and techniques, their own cost of systems and estimation of the manufacturing cost of the CRANESInspect system and its components. Moreover, as the products are innovative, and in accordance with Figure 25, the profit was set at approximately 30%.
Table 4 presents the calculated economic benefits for each SME participant of the CRANESInspect consortium. Specifically, Tangent will receive the IP rights on the AE/LRU transducers which they will be developing after project ends. The financial profits for Tangent from the transducers sales are calculated at approximately €176,000. Innotec through their participation in the project will receive the IP rights on the DSP system hardware which they will commercialise after project ends. The financial profits from the hardware sales for Innotec, five years after project ends, are calculated at approximately €310,000. TTest through their participation in the CRANESInspect project will have the opportunity to commercialise the developed fault detection software. The economic benefits for TTest from the software sales five years after the end of the project are calculated at approximately €196,000. In addition, Tangent, Innotec and TTest will have the opportunity to exploit the fully integrated CRANESInspect system. Agilis through their participation in the project will receive the IP ownership of the complete CRANESInspect system. As it was shown, the profits from the sales of the complete system were calculated at €21,500 per year. As a result, five years after project ends the economic benefits for Agilis are calculated at approximately €107,500.
Thus, the overall profit from the commercialisation of the project results, five years after the end of the project, is calculated at €789,500. Assuming that a job is created per €80,000 of profit, then a total of 10 job positions will be created 5 years after the CRANESInspect system hits the market. These 10 new job positions will be created among the consortium SMEs and, according to the previously presented profits, they will be distributed as follows: The profit of Tangent (ca. €176,000) will lead them to create 2.20 new job positions, the profit of Innotec (ca. €310,000) will enable them to create 3.88 new job positions, the profit of TTest (ca. €196,000) will allow them to create 2.45 new job positions, the profit of Agilis (ca. €107,500) will allow them to create 1.34 new job positions.
The markets of application and the numbers of units sold have been derived by examining the target market (Table 5) for each of the items listed in the “Products & Services” column. In addition, Table 5 presents the keywords upon which the search has been performed. During the market analysis it has been assumed that apart from the sales of the fully-integrated CRANESInspect system, each product or service can be offered for sale independently to the interested parties. It can be seen that there are approximately 6,088 potential customers for the project outputs worldwide. It is highlighted that the types of enterprises have been paired with the CRANESInspect products from the column “Products & Services” in Table 4, in order to accurately project the sales figures. Furthermore, the market penetration of each of the components has been conservatively assumed at 3%.
Dissemination Activities
During the CRANESInspect project three publications have been developed. These have been presented in scientific and technical conferences, and have been published in the conferences proceedings. Specifically, the results of the project have been presented in the following scientific and technical conferences:
(1) The 12th International Conference titled “Application of Contemporary Non-Destructive Testing in Engineering”. The aim of the conference was to bring together colleagues from academia and industry in novel NDT-related research areas and applications. More information about the conference can be found at http://lab.fs.uni-lj.si/latem/ndt/index.php?Naslov_linka=index . The work presented in this conference can be seen below:
S. Rafael, S. Soua, L. Zhao, T.H. Gan, N.A. Makris, T. Kavatzikidis, "Guided Waves focusing techniques for accurate defect detection in plate structures", ICNDT 2013, Application of Contemporary Non-destructive testing in Engineering in PORTOROŽ, Slovenia on September 4th - 6th, 2013.
(2) The “Tenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies”. The Conference was organized by the British Institute of Non Destructive Testing in cooperation with the US Society for Machinery Failure Prevention Technology (http://www.bindt.org/Events/CM_Conferences_&_Seminars/CM_2013_and_MFPT_2013). The presented work in the BINDT conference can be seen below:
N. A. Makris, L. Zhao, S. Soua, "Optimised Non Destructive Testing Technique for Crane Inspection Applications Based on Guided Waves and Acoustic Emission", CM 2013 and MFPT 2013, The Tenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 18-20 June 2013, Kraków, Poland.
(3) The “2012 IEEE International Ultrasonics Symposium”, covering topics in the fields of NDE & Industrial Applications, Physical Acoustics and Transducers & Transducer Materials (https://ius2012.ifw-dresden.de/). The presented work in the Symposium can be seen below:
L. Zhao, S. Soua, T. Kavatzikidis, N. Makris, "Guided Ultrasonic Wave Propagation in Complex Crane Structure Lattice – Main Structure Joining Plate", 2012 IEEE International Ultrasonics Symposium, October 7-10, 2012.
A public website of the project has been developed for (a) disseminating project results, (b) providing information related to the project and the partners and (c) enabling communication among all interested parties. It should be noted that the website will serve as a reference point for all those interested on CRANESInspect project results. As a result, it will be very helpful for communication with interested external entities.
Videos of the developed AE technique have been produced by the consortium. The videos describe AE excitation analysis on typical welded plates which are employed in the monitored crane for reinforcing purposes. These videos have been uploaded in the public area of the project website where they can be downloaded or viewed by any interested authority or independent user. Currently, the private section of the website contains videos from the third validation trial (please see below) and the final demonstration activity of the system.
Additionally, printed promotional material has been produced in order to promote the project and attract participants to workshops. The CRANESInspect brochure is a two fold A4 leaflet that presents an overview of the CRANESInspect project, outlines the partners of the consortium and lists a number of facts about cranes and the targets of the CRANESInspect project. Furthermore, the leaflet describes the need that initially led to the project, describes the monitoring zones and the sensors location on the crane, and explains the rationale behind the selection of the specific monitoring zones. The inner and outer views of the CRANESInspect leaflet are depicted in Figure 26. Additionally, the technical brochure of the CRANESInspect system is presented in Figure 27.
List of Websites:
The project website is: www.cranesinspect.eu
Contact Details: Mr. Ioannis Friganiotis AINOOUCHAOU PLIROFORIKI AE (iKNOWHOW INFORMATICS S.A.) Tel: +302106041425, Fax: +302106041675, E-mail: ifriganiotis@iknowhow.com
European seaports handle around 18% of worldwide container throughput and approximately 15% of global seaborne trade. Ship to Shore (STS) cranes form the most valuable infrastructure in seaports and are responsible for loading and unloading containers from container ships. Most modern cranes can achieve an average of 30-50 container moves per hour and in an effort to increase the productivity of ports there is a constant trend towards increasing the number of moves per hour per crane (i.e. 80 moves per hour are theoretically possible). STS cranes are being used in high cycle applications; the weight of the containers combined with the continuous operation in extreme environmental conditions pose substantial stresses to the structural condition of cranes.
Over the years this situation has led to numerous crane accidents, with the majority being fatal, leading the Health and Safety Executive (HSE) to issue an update on crane safety in ports. According to the HSE update on crane safety, critical structural crane members should be examined periodically for fatigue cracking; these cracks are characterised by low probability of detection and can only be detected when they reach a significant, often dangerous, size.
Current methods of inspection involve visual, magnetic particle and ultrasonics; however all these methods require complete paint removal, significantly increasing the inspection cost. Hence, it is evident that the container transport maritime industry urgently seeks a Structural Health Monitoring (SHM) system in order to ensure the structural integrity of their crane infrastructure.
The CRANESInspect system is a SHM system that offers continuous monitoring of cranes in real-time, with the ultimate target to prevent catastrophic and fatal accidents. The system is characterised by reduced human intervention and operator subjectivity offering a cost-effective solution for the inspection of cranes. The system employs a hybrid Non Destructive Testing (NDT) technique that is based on the techniques of Acoustic Emission (AE) and Long Range Ultrasonics (LRU). Towards this direction, the system employs a dual capability sensor array that acts as an AE receiver, and a LRU transmitter. The transducer array has been designed to operate under harsh environmental conditions and is capable of conforming to any irregular surface that is encountered on the crane.
The CRANESInspect system has been equipped with automated defect detection and sizing software. The CRANESInspect software is capable of processing both AE and LRU signals, and the LRU method is triggered based on the information provided by the AE technique. The software has been supported with Artificial Neural Networks (ANNs) which are responsible for defect classification through analysing the AE signals. Once a defect is identified the software can provide information for the defect location through a customised trilateration technique. Immediately, the LRU sensors are activated providing information regarding the defect size.
The CRANESInspect system is capable of demonstrating wireless transmission capabilities. Specifically, a wireless communication network, based on the IEEE 802.11n wireless transmission protocol, has been developed and tested to transmit data from the sensors to the main unit.
Project Context and Objectives:
The CRANESInspect project targeted at developing a continuous, real-time and non-invasive approach for monitoring failures of cranes. Towards this direction, an advanced integrated Structural Health Monitoring (SHM) system was developed to continuously monitor Ship to Shore cranes.
During the project, a hybrid Non-Destructive Testing (NDT) technique, based on Acoustic Emission (AE) and Long Range Ultrasonics (LRU), was developed. The technique was supported by new sensors systems specifically developed to detect structural damage and cracks in the main frame caused from fatigue and corrosion.
The CRANESInspect monitoring and prevention system features important advantages, such as:
- Real time monitoring of the structure to ensure safety
- Prevention of a catastrophic and fatal accident
- Cost-effective and total solution for inspection/maintenance
- Reduction of human intervention and operator subjectivity
- Highly sensitive and accurate Acoustic Emission (AE) technology
During the project, the consortium placed effort to advance the final CRANESInspect system further than any similar system in existence toward a complete and commercially competitive solution. As a result, during the CRANESInspect project the following objectives had to be met:
• Clarification of the tasks and aim of the project, based on the expectation of the End-User and the SMEs
• Documentation of the specifications for each part of the final CRANESInspect system
• Identification of the defect types and critical parts of large cranes
• Development of a theoretical model to understand the properties of the guided wave in large complex lattice structures
• Numerical simulation of the defect detection performance of the developed transducers on critical parts of the crane
• Simulation of the interaction of defects with the LRU
• The ability of the AE/LRU techniques to improve the probability of defect detection, location and sizing
• Design the new generation of AE/LRU sensors/transducers specifically designed for the CRANESInspect CM application
• Development of new AE/LRU transducers and transducer arrays designed to withstand harsh environments and to be mounted on various crane components
• Investigation of in-service noise parameters and external or environmental acoustic interference factors using initially, measurements on acoustic emission from in-service cranes in order to identify the requirements and specifications of the signal pre-conditioning module
• Design and development of the signal pre-conditioning module
• Design and implementation of signal processing routines on selected hardware
• Preliminary research on neural network architectures has been conducted to select the optimal scheme for detecting and sizing faults from AE and LRU sources
• Development of automated fault detection and sizing software
• Development of working AE/LRU transducer prototype with signal pre-conditioning capabilities
• The aforementioned prototype should be able to transmit data through a wired (or wireless) protocol
• Integration of the CRANESInspect prototype system
• Prototype testing and validation
• Training of the SMEs on the use of the CRANESInspect system
• Promote the developed technique and maintain a technology transfer program that ensures successful implementation and provides a marketing platform for the consortium SMEs
• Ensure a widespread use and awareness of the developed SHEM technology this will contribute to the increased crane safety
• To monitor and record progress and delivery of work packages
• To inform the Commission of progress made
• To optimize the power of the project team
• To disseminate the key project results to maximize the benefit to Europe
• To ensure best use is made of project resources; develop and maintain the project’s website
• To resolve any problems and disputes that may arise during the project and cannot be solved at individual work package level
Initially, the CRANESInspect consortium identified and formed the key technical requirements for the continuous structural health monitoring of cranes in respect to the wider field of port equipment as determined by the End-User of the project, i.e. Cargotec. All the priorities, constraints and key crane operating requirements were defined, and formed the basis of the research and technological development of the project; these items were continuously updated during the project and according to the needs that evolved.
A Finite Element Analysis was performed in order to identify the most critical components of a typical Ship-to-Shore crane structure. The results indicated that the reinforcing plates were the most prone to defects areas of the crane. Specifically, it was shown that the reinforcing plates were characterised by 91% probability of damage occurrence in contrast to the main I-beam (3%), the square cross section lattice (6%), and the cylindrical boom (1%). These results were in good agreement with the maintenance record of the crane operator, according to which the reinforcing plates were the most defected crane components.
A Semi Analytical Finite Element Method (SAFEM) was developed to generate ultrasonic guided waves in the previous structures, which are characterised by complex cross-section geometries. The analysis utilised the obtained phase and group velocity dispersion curves for the propagation of the guided waves in an I-Beam. The method of transient analysis that presents the propagation of the LRU (Long Range Ultrasonics) in an I-Beam with arbitrary cross section was described. In addition to dispersion curves, SAFEM was carried out for a complete modelling of the modal propagation in complex cross section structures. Modelling analysis of LRU based on the SAFEM method was performed. The analysis focused on the use of LRU in order to inspect the identified critical crane components, which involve:
- The main supporting reinforced I-Beam,
- The square beam forming the lattice,
- The cylindrical boom,
- The joining plate
According to the End-User, the welded areas of the joining plates to the main supporting I-beam are extremely prone to corrosion. The main supporting reinforced I-beam is a complex cross section structure, where the guided waves can propagate however, they were highly attenuated because of the number of junctions and welded supports that are encountered on the direction of the wave propagation. In order to decrease this effect, low frequency of operation was considered. Relatively short square cross section beams (4.6m) are forming the lattice of the crane. These are joining the main I-beams and the spacing between two junctions on the main I-beam is 3.5m. The modelling results showed efficient propagation along this type of structure. Additionally, the cylindrical boom is equivalent to a hollow cylinder; this is recognized to be a very efficient support of LRU and it was proved that Longitudinal and Torsional LRU can propagate with very low dispersion effect. The analysis of Dispersion Curves (DC) gave the phase velocity for the propagation of LRU in the selected structures. Dispersion curves enabled the selection of less dispersive modes facilitating an efficient defect investigation. In order to investigate these DC, the simulation work considered 3 main parts: (1) Modelling of the DC, (2) Analysis of the DC, and (3) Selection and optimisation of the operating conditions.
The design selection procedure of the piezoelectric AE sensor element was provided along with the design selection criteria. The preliminary experimental testing of the initial sensor element was described, and various experimental designs were proposed for the transducers, in order to facilitate the array development. Additionally, the design and optimisation of the transducer array was performed with respect to defect detection performance. The developed transducer array employs dual capability AE/LRU sensors that act as AE receptors and LRU transmitters. The optimisation of the design of the transducer array was performed through the theoretical models that were developed during the first period of the project. It was shown that enhanced surface wave can travel along the plate edge and defect boundary; hence producing a strong signal correlating to the presence of defects. Additionally, it was shown that the Probability of Detection (PoD) of the 6-sensors array is lower than the PoD of the 3-sensors array. Two different focusing techniques were developed and tested; a) Time Delay Focussing (TDF) and (b) Time Reversal Focussing (TRF). Theoretical and experimental results indicated that TRF technique is characterised by improved Signal to Noise (S/N) ratio, increasing the PoD. The optimised transducer array was developed into a mountable form. Particularly, two transducer array designs were implemented; (a) a transducer array able to be mounted to the reinforcing plates, and (b) a transducer array able to mounted to any crane component. The design drawings of both transducer array prototypes were provided along with instructions related to their deployment on the crane components. The experimental validation of the characteristics of the transducer’s array was performed through; (a) Experimental testing through leak break AE waveforms in steel plates, and (b) Experimental testing of LRU focussing capability.
The experimental design and test specifications of the CRANESInspect system were analytically presented. With significant help from the End-User of the project, i.e. Cargotec, and its’ customer, Rotterdam Shortsea Terminals (RST), the test-bed crane was specified and its’ technical characteristics (components geometries, dimensions and technical drawings) were described. RST provided their crane maintenance record, along with photos from their maintenance activities, confirming that the reinforcing plates and their welded areas are the most prone to defects structures. The CRANESInspect system was implemented on the working crane in Rotterdam. The sensor installation, data acquisition and logging, live and post data processing were reported. Preliminary measurements on AE from the operating crane were used to test the influence of the in-service parameters to the overall system operation. Moreover, the requirements of the signal pre-conditioning module based on info from previous related work packages were investigated.
The second field trial on the crane revealed that the CRANESInspect system was successfully integrated to the monitored crane, since two of the most important crane-operating parameters, i.e. torque and motor speed, were successfully recorded along with the AE signals. The correlation and relationship between several features of Acoustic Emission (AE) signals and the operating parameters of the monitored crane was established. Various features of the AE signals were extracted and a number of different correlations of the parameters were plotted. Additionally, the filtering of the acoustic noise from measured AE signals took place. Specifically, high-pass Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters were designed and implemented on real AE signals in an effort to filter out acoustic noise with frequency content lower than 50 Hz. The analysis indicated that FIR filters presented better filtering behaviour than the IIR filters.
It should be noted that a significant amount of AE data were recorded during the field trials, which were utilised towards the training of the Artificial Neural Networks (ANNs). In the CRANESInspect system defect identification is achieved through the employment of ANNs which were specifically designed according to the nature and morphology of the AE signals. Initially, an extensive literature research was conducted against the structure of the fault detection software. It was decided that the fault detection software should be divided in three discrete modules; (a) the signal denoising/preconditioning module, (b) the feature extraction / selection module and (c) the defect recognition module. Then, as part of the pattern recognition software a literature research was performed aiming to identify the use of neural networks as defect recognition components in LRU and/or Acoustic Emission applications. The results indicated that Error Back Propagation ANNs were the most commonly employed structures, and the implementation of this architecture was investigated in the CRANESInspect project. The ANNs were trained with defected and non-defected signals; specifically the AE signals from the crane monitoring field trials were used as indicative of non-defected samples, whereas AE signals acquired during the pencil lead brake experiments on the reinforcing plate were used as indicative of the existence of crack propagation. Defect localisation was achieved through the widely employed trilateration technique. The theoretical background and the software block diagram of the defect location feature were provided. The defect location software was verified against numerous experiments that presented a good repeatability factor. Additionally, the theoretical background and software block diagram of the focussing feature were described. The capabilities of the focussing feature were verified against numerous focussing experiments. Particularly, it was shown that acceptable Probabiltiy of Detection values (PoD) were obtained proving that defects of 1mm to 5mm can be detected by the CRANESInspect system.
Additionally, the development and testing of the wireless transmission protocol was performed. The wireless transmission protocol was specifically developed according to the requirements of the CRANESInspect project. Two protocols were selected: (a) IEEE 802.11n was selected for the communication between the sensor and the control center, i.e. CRANESInspect hardware box, and (b) IEEE 802.11ac was selected for the communication between the control center and the user. Based on the employed protocols the hardware components were defined and a prototype wireless module was developed. Additionally, the wireless software was designed and developed to operate both in Windows and Linux. The performance and capabilities of the wireless system were verified through carefully designed experiments both in indoors and outdoors environments. It was observed that under bad environmental conditions the calculated signal strength loss, approximately 24% for a distance of 100m, allowed the wireless communication between the sensor and the main unit (control center). Finally, the packaging of the wireless module took place according to the Ingress Protection (IP) ratings, in order to protect the module against the environmental conditions encountered on the crane.
The integration of each component into the final CRANESInspect system took place. The testing of the CRANESInspect system was performed both in the laboratory and on the crane. Additionally, detailed instructions on the correct use of the system were formulated, accompanied by safety precautions that should be taken into account during the installation, assembly and operation of the prototype CRANESInspect system.
Project Results:
The Research and Development work in the CRANESInspect project is reflected in the following project results:
1) AE/LRU sensors
2) Dual capability transducers and transducer arrays
3) CRANESInspect hardware
4) Wireless module
5) Fault detection software
6) Complete CRANESInspect Structural Health Monitoring (SHM) system
In this section, information and characteristics of each component are presented.
1) AE/LRU sensors
The design of the AE sensor is presented in Figure 1. A number of PZT (piezoelectric) materials were investigated and the analysis identified the Lead Zirconate Titanate PZT-5H (d33=5.93e-10 C/N, density=7500 Kg/m3) as the best fit to the CRANESInspect application. Electrode layers were attached to the PZT element and connected to the main potential input. The electrodes were made of Nickel (E=200 GPA, density=8900 kg/m3). In addition, a cover plate was employed in order to protect the PZT element and the electrode plates. The cover plate material was made of Alumina (E=300 GPA, density=390 Kg/m3).
The effect of different components on the behaviour of the sensor in the considered frequency bandwidth was investigated through modelling. The following study will focus on the frequency range up to 1 MHz. The employed excitation potential was a tone burst of 1 cycle at 100 kHz. A wideband frequency range was excited (up to 0.5 MHz). Figures 2 and 3 of Table 1 present the movement of the cover plate as a result of the applied voltage. It is evident that most of the resulting deformation is taking place in the Z direction however minor X and Y strain are obtained. Moreover, the waveforms show that there is a high frequency displacement, this displacement is reflected in the X and Y directions (Figure 4).
The selected sensor design was experimentally tested on steel plate structures using pencil lead brake tests of 0.5mm diameter. The results indicated that the sensor elements are delivering correct and coherent output level comparable to commercially available AE sensors from Vallen, which were taken as a reference (Figure 5).
The design and the prototype of the dual purpose sensor are presented in Figure 6. The sensor consists of two sides, one dedicated to reception and the other to transmission. A thin layer of cork is used to isolate both sides ensuring that the transmitter doesn’t resonate in the backing mass of the receiver.
2) Dual capability transducers and transducer arrays
In the CRANESInpsect two different transducer array designs were implemented; (a) a transducer array for reinforcing plates and (b) a transducer array for any flat surface. The design and prototype of the mountable AE/LRU transducer array for the reinforcing plate is presented in Figure 7. It is important to note that the array was designed in such a way so that the number and position of sensors can be easily modified. Hence, according to the application the number of the AE/LRU sensors can be adjusted. In Figure 8, two pictures of the transducer array mounted on a typical reinforcing plate encountered on the crane under investigation are shown.
The transducer array design for any flat surface is presented in Figure 9. Similarly to the transducer array for the reinforcing plate, the number and position of sensors can be easily modified.
Figure 10 below presents the mounted transducer prototypes on the main I-beam of the monitored crane. The sensors can be easily mounted and stay attached on the crane via magnetic force. Additionally, the necessary preparation for mounting the sensors has been kept at minimum levels. Specifically, sanding with a fine sand paper is required for a basic cleaning of the structure followed by the application of liquid coupling gel which acts as a coupling medium between the sensors and the surface under investigation. For the sensor installation on the main I-beam, presented in Figure 10, the trolley of the crane has been used in order to facilitate the installation procedure as well as for safety reasons.
Once the sensors are mounted on the crane, a cable packaging procedure is followed in order to minimise the effect on the normal operation of the crane, maximising in this way the durability of the sensors and hence the efficiency of the condition monitoring application. The mounted sensors on their final form with the packaged cables are presented in Figure 11.
3) CRANESInspect hardware
The Condition Monitoring system developed for this project is based on several devices from National Instruments embedded in a bespoke enclosure. It includes specific components for monitoring such as AE and LRU equipment. Figure 12 shows a basic disposition of the system’s components and connections. Following on from the general hardware diagram, a brief description of the components used to assembly the system is listed.
• Chassis PXIe-1082: The NI PXIe-1082 eight-slot chassis features a high-bandwidth backplane to meet a wide range of high-performance test and measurement application needs. It accepts PXI Express modules in every slot and supports standard PXI hybrid-compatible modules in up to four slots. The chassis also incorporates all the features of the latest PXI specification including support for both PXI and PXI Express modules and built-in timing and synchronisation features.
• Controller PXIe-8135 RT: The microprocessor and Arithmetic/Logic Unit (ALU) are embedded in the device which will manage both the operation of different modules attached to the chassis and the chassis itself. This is the most important device in the system and the one which will have the most demanding capabilities. The system will be operated by LabVIEW RT which is a real time operative system that enhances the reliability and real time operation of the different software processes running in the system.
• Data acquisition card PXIe-6368 (AE): This data acquisition card was selected in order to acquire signals for AE. It is capable to run 16 simultaneous analog inputs at 2MS/s which fits with the requirements for AE measuring.
• Data acquisition card PXI 6220: This data acquisition card was chosen to acquire parametric signals. It is a low cost card capable to run 16 analog inputs at 250KS/s.
• Decoupling circuits: The function of these circuits is to power the AE sensors preamplifiers and to decouple the DC-voltage from the AE signal. The dimensions are 35x60x65mm (HxWxL not including BNC connectors) and the weight is 250g. Finally, the temperature range is from -5°C to 85°C.
• Power amplifier: In order to excite the LRU transducers, a power amplifier was required. This device increases the maximum output range from the PXIe-6368 card from ±10V to ±150V. With this wider range, the excitation is possible and then the signal can propagate and be recorded by the receiver sensors. The MK4 Teletest® from PI power amplifier was modified and reassembled in order to be able to communicate and work with the National Instruments equipment. Some modifications were done as suppression of the D/A converter and modification of the switching circuitry. Figures 13 and 14 present an exploded and a render view of the CRANESInspect hardware. Finally, in Figure 15 a photograph of the prototype with connections is presented.
4) Wireless module
The hardware components of the developed wireless system includes; (a) a processing unit and (b) a wireless transmitter. The processing unit was specifically selected to reduce the processing load of the Control Centre and temporarily store the data acquired by the sensor before the wireless transmission. It is reminded that a “Wireless node-Control Centre” architecture has been adopted in the CRANESInspect system, with the Control Centre located on the crane and being responsible for the data communication to the. In this way, the quality of the data communication scheme between the sensors and the main CRANESInspect unit is significantly improved.
The wireless module is presented in Figure 16, and it has been specifically designed to host the processing unit, the wireless transmitter and the Analog to Digital Converter (ADC), and is capable of accommodating up to four (4) sensors. The Computer Aided Design (CAD) model of the wireless box is presented in Figure 16(a). It is highlighted that during the encapsulation of the wireless box special consideration has been placed towards ensuring that the box is totally protected against dust, contact and the ingress of water. Towards this direction, the choice of the suitable enclosure was performed according to the Ingress Protection (IP) and National Electrical Manufacturers Association (NEMA) ratings. Finally, the prototype of the wireless node is presented in Figure 16(b).
5) Fault detection software
The CRANESInspect prototype system has been equipped with a software responsible for the control of the system and the detection and localisation of defects The software has been developed in the LabView platform and the front panel is shown in Figure 17. It consists of three main parts, i.e. AE localisation, GW focusing and the Artificial Neural Networks (ANNs).
Artificial Neural Networks
The ANN was designed to classify the recorded AE signals in defected and non-defected; hence a pattern recognition ANN has been developed. The scope of the ANN is to classify inputs into a set of target categories; defected and non-defected. The ANN architecture that was selected has a two-layer feed-forward network with sigmoid hidden and output neurons. The ANN can achieve very good classification rate, if it is equipped with enough neurons in its hidden layer. A series of tests have been performed in order to select the best ANN hidden layer structure for the CRANESInspect application. Such a test is presented in Figure 18 which presents the ANN software interface. It can be seen that the interface consists of 3 graphs: (a) the training label graph, (b) the training error graph, and (c) the classification result graph. Specifically, Figure 18 presents the defect classification results of the network for a Testing Data array where the first 7 rows were consisted of signals originating from defected samples, whereas the next 6 rows were consisted of non-defected signals. It can be seen that the network can effectively classify the defected samples from the non-defected ones, behaviour that is also reflected from the training error graph, where training error less than 10-4 was achieved.
Defect location
In Figure 19, a screenshot of the software is shown. The interface consists of two panels and one graph. The panels are used to enter the parameters of the acquisition and localisation and the graph shows the localisation of the defect. Defect location is performed through the trilateration process which determines absolute or relative locations of points by measurements of distances, using the geometry of circles. With the trilateration method, all the parameters are included and solved in the LabView software, including the equations and the locations of the sensors. Finally, when the coordinates are found and the localisation result is represented in a graph as shown in Figure 20. In the localisation graph, the sensors location and the AE source localised are presented as dots and the distance between the different sensors and the defects are presented as lines of different colours for each sensor.
Defect Focussing
The software for focusing was developed in the LabView platform. In Figure 21, the front panel of the focusing part of the software is shown. It consists of two parts, transmission and reception; one graph and one parameter panel is displayed for each part. The transmission parameters such as amplitude, cycles per pulse and frequency can be modified within the software depending on the test specifications.
6) Complete Structural Health Monitoring CRANESInspect system
A detailed schematic of the system hardware is presented below (Figure 22). The dotted line shows the system enclosure boundary. The components surrounded by the dotted line are placed into the system enclosure boundary. The external components are shown outside this limit.
Potential Impact:
A detailed business plan for the CRANESInspect system was developed during the project; the purpose of the business plan was to ensure the successful commercialisation of the system through the early identification of the most appropriate exploitation routes for the CRANESInspect system. A SWOT analysis was initially performed to identify the Strengths, Weaknesses, Opportunities and Threats of the CRANESInspect system. This analysis is presented in Figure 23, below.
The sale price of the complete CRANESInspect system was calculated based on a monthly rate that covers the cost of the hardware and consumables in addition to the service and reporting costs. The sale price also depends on the monitoring full duration period. In Table 2 and Figure 24 the monthly costs of the CRANESInspect product are shown, depending on the number of units sold and on the full operation time. It should be noted that the cost of a single CRANESInspect system has been calculated at €21,060.
Figure 24 clearly indicates that the monthly operating costs of the CRANESInspect system are inversely proportional to the operation time of the system. For a continuous monitoring period of 2 years (24 months) the total system cost is shown in Table 3. The 20% and 30% profit and the break-even point for these percentage profits on the CRANESInspect costs are also shown. In Figure 25 the total cost and profits at 20% and 30% are shown. Additionally, it can be seen that the profit made over 24 months from the sales of 6 complete CRANESInspect systems are calculated at approximately €43,000 (thus €21,500 per year). Besides the complete CRANESInspect system, each SMEs will be in a position of gaining profit from the commercialisation of each system component. The products will include the AE/LRU sensors, the Digital Signal Processing (DSP) hardware and the operating defect detection software. Therefore, the CRANESInspect project will result in commercialised products that will be sold by the IP owners as these are displayed in Table 4.
Each of the commercial products will have a defined primary market individually (please refer to Table 5) and will also be sold as a component of the fully-integrated CRANESInspect system which will be sold by the IP owners (i.e. Tangent, Innotec and TTest) to interested crane operators, crane manufacturers and inspection services companies. Table 4 depicts the sales of the CRANESInspect commercial system five years after project ends. The overall price of the CRANESInspect system presented in Table 4 refers to the monitoring application of a STS crane with 16 reinforcing plates (as is the case with the test-bed of the CRANESInspect project). In accordance with the Deliverables 3.1 and 3.2 it is assumed that 3 AE/LRU sensors are needed for monitoring each plate and the price of each dual sensor is taken equal to €100. In order to calculate the costs of the CRANESInspect hardware the following price estimations were assumed: price of the PXIe data transmission and acquisition system equals to €2,500 per unit; price of the power amplification unit for signal transmission equals to €600 per unit; price of the decoupling units assumed at €60 each; price of the pre-amplification units for signal transmission assumed at €60 each. As a result, €240 will be need for the pre-amplifier and decoupling circuit per plate, resulting in €3840 for the monitoring application of 16 plates. Also, the DSP CRANESInspect hardware includes the wireless communication module which cost €230 and is capable of accommodating up to two plates; thus the cost of the wireless box for the specific application was calculated at €1,840. Additionally, the price of the CRANESInspect software was considered at €10,000.
It should be noted that the consortium SMEs have arrived at the prices for the CRANESInspect products by analysing the prices of state of the art systems and techniques, their own cost of systems and estimation of the manufacturing cost of the CRANESInspect system and its components. Moreover, as the products are innovative, and in accordance with Figure 25, the profit was set at approximately 30%.
Table 4 presents the calculated economic benefits for each SME participant of the CRANESInspect consortium. Specifically, Tangent will receive the IP rights on the AE/LRU transducers which they will be developing after project ends. The financial profits for Tangent from the transducers sales are calculated at approximately €176,000. Innotec through their participation in the project will receive the IP rights on the DSP system hardware which they will commercialise after project ends. The financial profits from the hardware sales for Innotec, five years after project ends, are calculated at approximately €310,000. TTest through their participation in the CRANESInspect project will have the opportunity to commercialise the developed fault detection software. The economic benefits for TTest from the software sales five years after the end of the project are calculated at approximately €196,000. In addition, Tangent, Innotec and TTest will have the opportunity to exploit the fully integrated CRANESInspect system. Agilis through their participation in the project will receive the IP ownership of the complete CRANESInspect system. As it was shown, the profits from the sales of the complete system were calculated at €21,500 per year. As a result, five years after project ends the economic benefits for Agilis are calculated at approximately €107,500.
Thus, the overall profit from the commercialisation of the project results, five years after the end of the project, is calculated at €789,500. Assuming that a job is created per €80,000 of profit, then a total of 10 job positions will be created 5 years after the CRANESInspect system hits the market. These 10 new job positions will be created among the consortium SMEs and, according to the previously presented profits, they will be distributed as follows: The profit of Tangent (ca. €176,000) will lead them to create 2.20 new job positions, the profit of Innotec (ca. €310,000) will enable them to create 3.88 new job positions, the profit of TTest (ca. €196,000) will allow them to create 2.45 new job positions, the profit of Agilis (ca. €107,500) will allow them to create 1.34 new job positions.
The markets of application and the numbers of units sold have been derived by examining the target market (Table 5) for each of the items listed in the “Products & Services” column. In addition, Table 5 presents the keywords upon which the search has been performed. During the market analysis it has been assumed that apart from the sales of the fully-integrated CRANESInspect system, each product or service can be offered for sale independently to the interested parties. It can be seen that there are approximately 6,088 potential customers for the project outputs worldwide. It is highlighted that the types of enterprises have been paired with the CRANESInspect products from the column “Products & Services” in Table 4, in order to accurately project the sales figures. Furthermore, the market penetration of each of the components has been conservatively assumed at 3%.
Dissemination Activities
During the CRANESInspect project three publications have been developed. These have been presented in scientific and technical conferences, and have been published in the conferences proceedings. Specifically, the results of the project have been presented in the following scientific and technical conferences:
(1) The 12th International Conference titled “Application of Contemporary Non-Destructive Testing in Engineering”. The aim of the conference was to bring together colleagues from academia and industry in novel NDT-related research areas and applications. More information about the conference can be found at http://lab.fs.uni-lj.si/latem/ndt/index.php?Naslov_linka=index . The work presented in this conference can be seen below:
S. Rafael, S. Soua, L. Zhao, T.H. Gan, N.A. Makris, T. Kavatzikidis, "Guided Waves focusing techniques for accurate defect detection in plate structures", ICNDT 2013, Application of Contemporary Non-destructive testing in Engineering in PORTOROŽ, Slovenia on September 4th - 6th, 2013.
(2) The “Tenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies”. The Conference was organized by the British Institute of Non Destructive Testing in cooperation with the US Society for Machinery Failure Prevention Technology (http://www.bindt.org/Events/CM_Conferences_&_Seminars/CM_2013_and_MFPT_2013). The presented work in the BINDT conference can be seen below:
N. A. Makris, L. Zhao, S. Soua, "Optimised Non Destructive Testing Technique for Crane Inspection Applications Based on Guided Waves and Acoustic Emission", CM 2013 and MFPT 2013, The Tenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 18-20 June 2013, Kraków, Poland.
(3) The “2012 IEEE International Ultrasonics Symposium”, covering topics in the fields of NDE & Industrial Applications, Physical Acoustics and Transducers & Transducer Materials (https://ius2012.ifw-dresden.de/). The presented work in the Symposium can be seen below:
L. Zhao, S. Soua, T. Kavatzikidis, N. Makris, "Guided Ultrasonic Wave Propagation in Complex Crane Structure Lattice – Main Structure Joining Plate", 2012 IEEE International Ultrasonics Symposium, October 7-10, 2012.
A public website of the project has been developed for (a) disseminating project results, (b) providing information related to the project and the partners and (c) enabling communication among all interested parties. It should be noted that the website will serve as a reference point for all those interested on CRANESInspect project results. As a result, it will be very helpful for communication with interested external entities.
Videos of the developed AE technique have been produced by the consortium. The videos describe AE excitation analysis on typical welded plates which are employed in the monitored crane for reinforcing purposes. These videos have been uploaded in the public area of the project website where they can be downloaded or viewed by any interested authority or independent user. Currently, the private section of the website contains videos from the third validation trial (please see below) and the final demonstration activity of the system.
Additionally, printed promotional material has been produced in order to promote the project and attract participants to workshops. The CRANESInspect brochure is a two fold A4 leaflet that presents an overview of the CRANESInspect project, outlines the partners of the consortium and lists a number of facts about cranes and the targets of the CRANESInspect project. Furthermore, the leaflet describes the need that initially led to the project, describes the monitoring zones and the sensors location on the crane, and explains the rationale behind the selection of the specific monitoring zones. The inner and outer views of the CRANESInspect leaflet are depicted in Figure 26. Additionally, the technical brochure of the CRANESInspect system is presented in Figure 27.
List of Websites:
The project website is: www.cranesinspect.eu
Contact Details: Mr. Ioannis Friganiotis AINOOUCHAOU PLIROFORIKI AE (iKNOWHOW INFORMATICS S.A.) Tel: +302106041425, Fax: +302106041675, E-mail: ifriganiotis@iknowhow.com