Automotive fault diagnosis - part II: a distributed agent diagnostic system

For pt.I see Crossman, J.A. et al., ibid., p.1063-75. We describe a novel diagnostic architecture, distributed diagnostics agent system (DDAS), developed for automotive fault diagnosis. The DDAS consists of a vehicle diagnostic agent and a number of signal diagnostic agents, each of which is responsible for the fault diagnosis of one particular signal using either a single or multiple signals, depending on the complexity of signal faults. Each signal diagnostic agent is developed using a common framework that involves signal segmentation, automatic signal feature extraction and selection, and machine learning. The signal diagnostic agents can concurrently execute their tasks; some agents possess information concerning the cause of faults for other agents, while other agents merely report symptoms. Together, these signal agents present a full picture of the behavior of the vehicle under diagnosis to the vehicle diagnostic agent. DDAS provides three levels of diagnostics decisions: signal-segment fault; signal fault; vehicle fault. DDAS is scalable and versatile and has been implemented for fault detection of electronic control unit (ECU) signals; experiment results are presented and discussed.