Automotive signal fault diagnostics. I. Signal fault analysis, signal segmentation, feature extraction and quasi-optimal feature selection

Abstract

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).