Learning Error Patterns from Diagnosis Trouble Codes

Abstract

Diagnostic trouble codes (DTCs) are steadily produced by a vehicle's control units to support the diagnosis process when the vehicle is maintained or to initiate predictive maintenance. Although, DTCs carry a lot of information, possibly including environmental data such as the engine temperature, the velocity, etc., they are of little help to an automotive engineer if seen without a context. In fact, a concrete problem can mostly be diagnosed if an already known pattern of DTCs is present. However, detecting new patterns in masses of vehicle data gathered each day from thousands of vehicles and recognizing known patterns accurately cannot be performed manually by automotive engineers. We propose an unsupervised DTC pattern learning framework supporting the daily field data analysis of original equipment manufacturers (OEMs).