Case-base reasoning in vehicle fault diagnostics

This paper presents our research in case-based reasoning (CBR) with application to vehicle fault diagnosis. We have developed a distributed diagnostic agent system, DDAS that detects faults of a device based on signal analysis and machine learning. The CBR techniques presented are used to rind root cause of vehicle faults based on the information provided by the signal agents in DDAS. Two CBR methods are presented, one used directly the diagnostic output from the signal agents and another uses the signal segment features. We present experiments conducted on real vehicle cases collected from auto dealers and the results show that both method are effective in finding root causes of vehicle faults.