Automotive signal diagnostics using wavelets and machine learning

In this paper, we describe an intelligent signal analysis system employing the wavelet transformation in the solution of vehicle engine diagnosis problems. Vehicle engine diagnosis often involves multiple signal analysis. The developed system first partitions a leading signal into small segments rep...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2000-09, Vol.49 (5), p.1650-1662
Main Authors: Hong Guo, Crossman, J.A., Murphey, Y.L., Coleman, M.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Automobiles
Automotive engineering
Automotive engines
Computer science
control theory
systems
Diagnosis
Diagnostic systems
Engines
Exact sciences and technology
Fuzzy sets
Fuzzy systems
Information, signal and communications theory
Intelligent systems
Intelligent vehicles
Learning and adaptive systems
Learning systems
Machine learning
Mathematical analysis
Segments
Signal analysis
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
Wavelet
Wavelet analysis
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Summary:In this paper, we describe an intelligent signal analysis system employing the wavelet transformation in the solution of vehicle engine diagnosis problems. Vehicle engine diagnosis often involves multiple signal analysis. The developed system first partitions a leading signal into small segments representing physical events or states based on wavelet multi-resolution analysis. Second, by applying the segmentation result of the leading signal to the other signals, the detailed properties of each segment, including inter-signal relationships, are extracted to form a feature vector. Finally, a fuzzy intelligent system is used to learn diagnostic features from a training set containing feature vectors extracted from signal segments at various vehicle states. The fuzzy system applies its diagnostic knowledge to classify signals as abnormal or normal. The implementation of the system is described and experiment results are presented.
ISSN:0018-9545
1939-9359
DOI:10.1109/25.892549