Objective
This project is directed toward the development and application of neural networks new signal processing procedures in a novel approach to detecting and characterizing faults and other nonlinear mechanisms related to defects or structural damage.
This project is concerned with the development and application of neural networks new signal processing procedures in a novel approach to detecting and characterizing faults and other nonlinear mechanisms related to defects or structural damage.
The major difference in this work with respect to existing methodologies is the preprocessing of the raw data to enhance training procedures. Complex behaviour as in neurological systems has been successfully treated by using the digital equivalent of the human brain (ie neural networks). These can be trained to recognize highly complex patterns and therefore should have the capability of recognizing behaviour such as stress wave emissions from crack propagation or similar phenomena, hence they have the potential for use in identifying and distinguishing fault conditions. The establishment of the principles enables a significant advance in the technology for detecting and characterizing faults.
A new methodology for fault assessment in civil engineering structures has been developed. The results demonstrate the importance of separating the discriminatory features in the raw transducer time signals from the pattern classification in the form of a knowledge hierarchy. By doing this one can exploit the best aspects of temporal data preprocessing with the best neural classification procedures. This adds engineering insight and maximizes the probability of detecting faults. Feature extraction based on narmax data processing, curvature data and response only data appears to provide excellent signatures which can be uded to detect and discriminate nonlinear effects and faults.
The fault assessment procedures offer considerable potential and warrant further development and refinement to produce commercial products.
Although the study is directed toward components used in civil engineering structures in order to take advantage of specific competence in these areas, it is fundamental in its nature and therefore is directed at establishing principles that will result in applications to other key sectors of industry. The major difference in this work with respect to existing methodologies is the pre-processing of the raw data to enhance "training" procedures. Complex behaviour as in neurological systems has been succsfully treated by using the digital equivalent of the human brain, i.e. neural networks. These can be "trained" to recognise highly complex patterns and therefore should have the capability of recognising behaviour such as stress wave emissions from crack propagation or similar phenomena, hence they have the potential for use in identifying and distinguishing fault conditions. The establishment of the principles will enable a significant advance in the technology for detecting and characterizing faults.SP Such technologies could gainfully be developed for use in key sectors of the Aerospace industry, Civil Engineering Infrastructural systems (bridges/monumental buildings), and Manufacturing and Automobile industries, by initiating a programme of research involving of research involving both theoretical and experimental studies based upon new processing and neural network methodologies.
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.
- engineering and technology civil engineering
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering signal processing
- natural sciences computer and information sciences data science data processing
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Funding Scheme
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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.
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Coordinator
M13 9PL Manchester
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.