Data reduction techniques for intelligent fault diagnosis in automotive systems

Abstract

Faults in automotive systems significantly degrade the performance and efficiency of vehicles, and often times are the major causes of vehicle break-down leading to large expenditure for repair and maintenance. An intelligent fault diagnosis system can ensure uninterrupted and reliable operation of vehicular systems, and aid in vehicle health monitoring. Due to cost constraints, the current electronic control units (ECUs) for control and diagnosis have 1-2 MB of memory and 24-50 MHz of processor speed. Therefore, intelligent data reduction techniques and partitioning methodology are needed for effective fault diagnosis in automotive systems. In this paper, we propose a data-driven approach using a data reduction technique, coupled with a variety of classifiers, for an automotive engine system. Adaptive boosting (AdaBoost) is employed to improve the classifier performance. Our proposed techniques can be used for any vehicle systems without the need to tune the classification algorithms for a specific vehicle model. Our proposed fault diagnosis scheme results in significant reduction in data size (25.6 MB→12.8 KB) without loss of accuracy in classification. © 2006 IEEE.