Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data

Predictive maintenance is becoming more and more important for the commercial vehicle manufactures, as focus shifts from productto service-based operation. The idea is to provide a dynamic maintenance schedule, fulfilling specific needs of individual vehicles. Luckily, the same shift of focus, as well as technological advancements in the telecommunication area, make long-term data collection more widespread, delivering the necessary data. We have found, however, that the standard attribute-value knowledge representation is not rich enough to capture important dependencies in this domain. Therefore, we are proposing a new rule induction algorithm, inspired by Michalski’s classical AQ approach. Our method is aware that data concerning each vehicle consists of time-ordered sequences of readouts. When evaluating candidate rules, it takes into account the composite performance for each truck, instead of considering individual readouts in separation. This allows us more flexibility, in particular in defining desired prediction horizon in a fuzzy, instead of crisp, manner.