Hong Guo1, Jacob
A. Crossman1, Yi Lu
Murphey1, Mark Coleman2, Robert Mills2 and Shane Rachedi2
1Department of Electrical and Computer Engineering
The University of Michigan-Dearborn
2Advanced Diagnostic Design
Diagnostic Service Planning
Ford Motor Company
Abstract
In this paper, we describe an intelligent signal
analysis system employing the wavelet transformation towards solving 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 mutli-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.