Automotive signal diagnostics using wavelets and machine learning
Hong Mei Guo, Jacob Crossman, Yi Lu Murphey, M. Coleman · IEEE Transactions on Vehicular Technology · 2000
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.