Multi-class novelty detection in diagnostic trouble codes from repair shops

The complexity of vehicles has increased over the last years and will continue to do so. Hence, repairs in repair shops become more and more complex and thereby time-consuming, where time to repair is a competitive factor. During repairs and servicing of vehicles in the independent aftermarket the data read-out using diagnostic testers is transferred back to a common back-end. This “Big Data” contains millions of diagnostic trouble codes (DTCs) and freeze frames, where DTCs point to potential fault causes and freeze frames describe the vehicle's condition when the DTC was stored, e.g. the current engine RPM. This paper proposes an approach to benefit from this “Big Data” in order to (a) acquire knowledge for the development of new vehicles and components and (b) to speed up the time for future fault analyses and repairs. The aim is to detect the vehicle operation modes where a specific fault code was previously not or rarely observed, referred to as novelty detection (anomaly detection). This can point to rare fault causes, which are likely to have a longer time to repair since they are not common. From the field of machine learning, one-class classifiers are applied in two setups: (1) one-class novelty detection, where the data items from all operation modes are combined to one training set and (2) multi-class novelty detection, where an individual classifier is trained for each operation mode. On real data it is shown that novelties can be successfully detected. While it was found that many faults predominantly occur while the vehicle is in a specific operation mode, e.g. when the engine is warm and is in idle, the most interesting novelties were faults when the engine was off or cold. It was found that the multi-class case is superior for the underlying problem using autoSVDD to yield the best results.