Periodic Reporting for period 2 - iPIM (Development of an Intelligent Onshore Pipeline Integrity Monitoring System)
Okres sprawozdawczy: 2016-10-01 do 2018-03-31
With the aging infrastructure and the growing rate of new infrastructure development, the ultrasonic testing (UT) market is expected to reach USD 3.93 Billion by 2022, at a CAGR of 8.3% between 2016 and 2022 (According to Markets and Markets Research.com).
The proposed system aims to disrupt the NDT inspection industry along with other solutions that align the pipeline industry to the new industrial revolution called Industry 4.0
While the 3rd Industrial revolution has been known for the extensive use of controls, IT and Electronics for an automated and high productivity environment, the future of the industry is based on the integration of virtual and physical sub-systems. This is achieved by incorporation of Industrial Internet of Things, Cloud computing, Wireless Intelligence and Big Data in Sustainable Manufacturing and Lifecycle Assessment. This is exactly the framework within which the iPIM system was developed. It consists of two (external and internal) hardware components to facilitate the Structural Health Integrity monitoring of pipelines: a wireless network of collars and a pipeline inspection gauge (PIG). These hardware components provide a cost-effective approach to precision predictive maintenance via machine learning software. Comprised of a moving robotic mechanism and a permanently installed network of sensors, it will continuously monitor the pipeline for cracks, corrosion and other faults and send the information over a wireless communication system to the network monitoring centre where the fault will be displayed over a map of the pipeline.
The proposed system aims to disrupt the NDT inspection industry along with other solutions that align the pipeline industry to the new industrial revolution called Industry 4.0
While the 3rd Industrial revolution has been known for the extensive use of controls, IT and Electronics for an automated and high productivity environment, the future of the industry is based on the integration of virtual and physical sub-systems. This is achieved by incorporation of Industrial Internet of Things, Cloud computing, Wireless Intelligence and Big Data in Sustainable Manufacturing and Lifecycle Assessment. This is exactly the framework within which the iPIM system was developed. It consists of two (external and internal) hardware components to facilitate the Structural Health Integrity monitoring of pipelines: a wireless network of collars and a pipeline inspection gauge (PIG). These hardware components provide a cost-effective approach to precision predictive maintenance via machine learning software. Comprised of a moving robotic mechanism and a permanently installed network of sensors, it will continuously monitor the pipeline for cracks, corrosion and other faults and send the information over a wireless communication system to the network monitoring centre where the fault will be displayed over a map of the pipeline.
System specifications
Delivery of defect recognition training data, control and monitoring software
Delivery of the Machine Learning and data fusion system
Delivery of a prototype advanced signal processing electronics module
Delivery of a prototype energy harvesting module
Delivery of a prototype mini robotic pig
Results of modelling and validation of defective and defect-free pipeline samples
Delivery of dual-mode transducer
Delivery of transducer collar
Delivery of the integrated working prototypes of the system
Reported results of the long range ultrasonic guided wave testing on pipelines
iPIM system validated by field trials with a report of the results
Report on iPIM manufacturing model
Report on market opportunity and sales plan model
Report on finance and revenue
Report on promotion and distribution model
Business Plan
Project Website
All work has been completed
After close cooperation with the industry and NDT professional community we derived with a complete Pipeline Monitoring and Preventive maintenance solution that is built as follows:
System Components
External Inspection: Wireless Collar (Node)
Internal Inspection Tool: Pipeline Inspection Gauge (PIG)
The PIG applies the same LRU technology as the external nodes but is deployed inside the pipe. The iPIM-PIG leverages on the ability of LRU to reach areas not accessible to conventional probes; hence, this distinctive advantage will enable the inspection of previously unreachable areas.
Preventive Maintenance: Machine Learning Software
By applying Machine Learning and continuous monitoring of specific areas of the pipeline you can have access to accurate information about the specific conditions of a particular pipeline in certain environmental conditions. This is achieved by monitoring the pipeline over time and fine-tuning the interpretation of the data for accuracy.
The set of products developed in the iPIM project aim to identify corrosion before it impacts the Oil & Gas plant, facilities or remote pipelines with the use of Predictive Corrosion Management.
Predictive Corrosion Management is an Asset Performance Management (APM) extension that combines PIANSO analysis software with LRU Guided Waves installed sensors to continuously monitor pipeline health and manage risk. This powerful package allows operators in upstream, downstream, power, and chemical facilities to monitor at a higher frequency and take more precise measurements anytime, and anywhere. It helps improve safety and eliminates the need to shut down critical processes or deploy field maintenance professionals until necessary.
Market Analysis has helped us identify and target the following markets:
Targeted Markets
The targeted market is that of the Non Destructive Testing and Structural Health Management of the Petroleum Industry for buried, unpigable or hard to reach pipelines in the following sectors:
• Upstream: Oil and Gas, Pumping Stations
• Downstream: Refineries, LNG Plants, Petrochemical
• Power Plants: Nuclear, Conventional
Delivery of defect recognition training data, control and monitoring software
Delivery of the Machine Learning and data fusion system
Delivery of a prototype advanced signal processing electronics module
Delivery of a prototype energy harvesting module
Delivery of a prototype mini robotic pig
Results of modelling and validation of defective and defect-free pipeline samples
Delivery of dual-mode transducer
Delivery of transducer collar
Delivery of the integrated working prototypes of the system
Reported results of the long range ultrasonic guided wave testing on pipelines
iPIM system validated by field trials with a report of the results
Report on iPIM manufacturing model
Report on market opportunity and sales plan model
Report on finance and revenue
Report on promotion and distribution model
Business Plan
Project Website
All work has been completed
After close cooperation with the industry and NDT professional community we derived with a complete Pipeline Monitoring and Preventive maintenance solution that is built as follows:
System Components
External Inspection: Wireless Collar (Node)
Internal Inspection Tool: Pipeline Inspection Gauge (PIG)
The PIG applies the same LRU technology as the external nodes but is deployed inside the pipe. The iPIM-PIG leverages on the ability of LRU to reach areas not accessible to conventional probes; hence, this distinctive advantage will enable the inspection of previously unreachable areas.
Preventive Maintenance: Machine Learning Software
By applying Machine Learning and continuous monitoring of specific areas of the pipeline you can have access to accurate information about the specific conditions of a particular pipeline in certain environmental conditions. This is achieved by monitoring the pipeline over time and fine-tuning the interpretation of the data for accuracy.
The set of products developed in the iPIM project aim to identify corrosion before it impacts the Oil & Gas plant, facilities or remote pipelines with the use of Predictive Corrosion Management.
Predictive Corrosion Management is an Asset Performance Management (APM) extension that combines PIANSO analysis software with LRU Guided Waves installed sensors to continuously monitor pipeline health and manage risk. This powerful package allows operators in upstream, downstream, power, and chemical facilities to monitor at a higher frequency and take more precise measurements anytime, and anywhere. It helps improve safety and eliminates the need to shut down critical processes or deploy field maintenance professionals until necessary.
Market Analysis has helped us identify and target the following markets:
Targeted Markets
The targeted market is that of the Non Destructive Testing and Structural Health Management of the Petroleum Industry for buried, unpigable or hard to reach pipelines in the following sectors:
• Upstream: Oil and Gas, Pumping Stations
• Downstream: Refineries, LNG Plants, Petrochemical
• Power Plants: Nuclear, Conventional
Key features of iPIM to overcome limitations of current SOA
1. iPIM offers a permanent monitoring system for oil and gas pipeline.
2. As very sophisticated hardware and software system will be more accurate and offer precision in prediction of progress of fault over time.
3. iPIM will be able to detect corrosion and erosion defects under insulation at any location.
4. The location of the fault along with the distance from a reference point will be displayed on the user interface.
5. The power consumption will be very low as it will make use of energy harvesting system.
Benefits for pipeline operators:
> Minimise down time (=cost) for pipeline operations: The system reduces significantly the frequency of need for pigging operations (including pipeline cleaning pigging necessary prior to intelligent pigging) thus minimises down time.
>Optimization of cost of operation through accurate maintenance planning: The iPIM system monitoring allows for accurate prediction of Progression of Cracks over time which is a valuable contribution to maintenance work planning to increase cost efficiency. Accuracy of monitoring is enhanced through Machine Learning and Data Fusion. The accumulation of data over time is used to feed the Machine Learning function and learn from input of the analysed data. Data fusion is used to improve the accuracy of prediction of faults and their classification according to parameters determined for that particular pipeline and in integration with other sources of SHM (Structural Health Monitoring) offering increased Probability of Detection (POD) and higher Cross Sectional Area (CSA) resolution.
Impact to society:
iPIM will ensure onshore pipeline safety and reduce cost due to pipeline failure in Europe.
- Increase in the detection, evaluation and monitoring of defects in pipelines.
- Decrease in the probability of pipeline failure.
- Providing a substantial reduction in risk to communities, economies and the environment stem from pipeline rupture.
-Provide the pipeline owners and regulatory authorities with very valuable data that will facilitate the integration of the non-destructive testing (NDT) and structural integrity (SI) assessment, thus improving the confidence in NDT performance and enabling the SI community to adopt novel and more sophisticated approaches to pipe design, operation and life extension
- The monitoring and other related cost will be reduced. As estimated the cost reduction will be about 50% when compared to the use of existing NDT systems.
1. iPIM offers a permanent monitoring system for oil and gas pipeline.
2. As very sophisticated hardware and software system will be more accurate and offer precision in prediction of progress of fault over time.
3. iPIM will be able to detect corrosion and erosion defects under insulation at any location.
4. The location of the fault along with the distance from a reference point will be displayed on the user interface.
5. The power consumption will be very low as it will make use of energy harvesting system.
Benefits for pipeline operators:
> Minimise down time (=cost) for pipeline operations: The system reduces significantly the frequency of need for pigging operations (including pipeline cleaning pigging necessary prior to intelligent pigging) thus minimises down time.
>Optimization of cost of operation through accurate maintenance planning: The iPIM system monitoring allows for accurate prediction of Progression of Cracks over time which is a valuable contribution to maintenance work planning to increase cost efficiency. Accuracy of monitoring is enhanced through Machine Learning and Data Fusion. The accumulation of data over time is used to feed the Machine Learning function and learn from input of the analysed data. Data fusion is used to improve the accuracy of prediction of faults and their classification according to parameters determined for that particular pipeline and in integration with other sources of SHM (Structural Health Monitoring) offering increased Probability of Detection (POD) and higher Cross Sectional Area (CSA) resolution.
Impact to society:
iPIM will ensure onshore pipeline safety and reduce cost due to pipeline failure in Europe.
- Increase in the detection, evaluation and monitoring of defects in pipelines.
- Decrease in the probability of pipeline failure.
- Providing a substantial reduction in risk to communities, economies and the environment stem from pipeline rupture.
-Provide the pipeline owners and regulatory authorities with very valuable data that will facilitate the integration of the non-destructive testing (NDT) and structural integrity (SI) assessment, thus improving the confidence in NDT performance and enabling the SI community to adopt novel and more sophisticated approaches to pipe design, operation and life extension
- The monitoring and other related cost will be reduced. As estimated the cost reduction will be about 50% when compared to the use of existing NDT systems.