The paper describes our research in vehicle signal fault diagnosis. A modern vehicle has embedded sensors, controllers and computer modules that collect a large number of different signals. These signals, ranging from simple binary modes to extremely complex spark timing signals, interact with each other either directly or indirectly. Modern vehicle fault diagnostics very much depend upon the input from vehicle signal diagnostics. Modeling vehicle engine diagnostics as a signal fault diagnostic problem requires a good understanding of signal behaviors relating to various vehicle faults. Two important tasks in vehicle signal diagnostics are to find what signal features are related to various vehicle faults, and how can these features be effectively extracted from signals. We present our research results in signal faulty behavior analysis, automatic signal segmentation, feature extraction and selection of important features. These research results have been incorporated in a novel vehicle fault diagnostic system, which is described in another paper (see Yi Lu Murphey et al., ibid., p.1076-98).
Automotive signal fault diagnostics - part I: signal fault analysis, signal segmentation, feature extraction and quasi-optimal feature selection
IEEE Transactions on Vehicular Technology ; 52 , 4 ; 1063-1075
2003
13 Seiten, 22 Quellen
Article (Journal)
English
Construction of fault-tolerant signal feature subsets
British Library Online Contents | 2015
|Gas turbine diagnostics : signal processing and fault isolation
TIBKAT | 2013
|Feature Extraction and Matching as Signal Detection
British Library Conference Proceedings | 1993
|Signal age fault detection in distributed embedded automotive systems
Automotive engineering | 2010
|