Objective
The project has made significant progress in the following areas:
* Specification, installation and commissioning of data acquisition systems has been achieved both with the SME partners, and with the R&D performers who are conducting the experimental data acquisition.
* Test data have now been acquired from the SME and other industrial partners, as well as from the R&D performers.
* The test have been analysed and conclusions drawn on the issues of feature identification, feature extraction and modelling techniques which are to be used in the project.
* The specification for the system design has been carried out, and extensive work has now been done on hardware, software, and system integration.
* Prototype software modules have been developed including: a fuzzy classifier using fuzzy logic; a fault and symptom tree module for machine and fault definition; a generic neural network module for novelty assessment and classification; user interface modules for overall system integration and communication with the hardware for data collection and other software modules.
* Work has commenced on dissemination and exploitation; a web site (see URL above) has been produced; industrial dissemination events have taken place in France and Finland; and the R&D partners are now currently discussing the submission of a number of academic papers to conferences and journals. A detailed exploitation plan and IPR agreement has been drafted and is being considered by the partners.
* A number of technical and management meetings have taken place, and the consortium has worked closely to ensure that the project meets the objectives of the SME partners, retaining an industrial focus and producing exploitable results (in keeping with the nature of the CRAFT initiative.
This proposal is prepared with the help of an Exploratory Award whichincluded a Feasibility Study. Tool and machine condition monitoring is of vital importance to modern industry in its quest for high reliability, quality and efficiency. Machine maintenance is also a major expense throughout every industrial sector. It has been shown that effective predictive maintenance can result in an 8% maintenance cost saving and a further 8% increase in productivity [1]. These figures are borne out across Europe, as shown by the European Benchmark Study on Maintenance [2]. Most condition monitoring systems are unnecessarily complex since they aim to diagnose the nature of faults within machines. For efficient asset management however diagnosis of the nature of a fault is not required. Instead plant operators need to know which machines have problems, where they are and how long specific machines will last before faults lead to terminal failure. The purpose of this project is to develop the understanding to allow a permanently installed instrument to be developed capable of providing an output of remaining life of each bearing or gear within a machine. This project aims to advance the technology currently available for machine condition monitoring by the application of intelligent sensor systems. In particular, this project aims to develop an intelligent acoustic emission (AE) sensor system which will be capable ofpredicting number of days to failure for a particular machine. The system will primarily take data from AE sensors but will also use vibration and temperature data; an intelligent software module will combine the information to provide a more accurate representation of current machine condition. Such a system will have particular relevance to many SMEs, including the core proposers. Although it is not possible to develop vibration techniques alone to achieve this goal, the acoustic emission (AK) approach seems ideally suited. In the last three years the high frequency AE technique has been shown to provide a very powerful yet simple means of identifying machines with problems at an early stage.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences software
- engineering and technology mechanical engineering manufacturing engineering subtractive manufacturing
- social sciences economics and business economics production economics productivity
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
DE4 2AJ Matlock
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.