Chassis Hardware Fault Diagnostics with Hidden Markov Model Based Clustering
Paper i proceeding, 2020
Predictive maintenance is a key component regarding cost reduction in automotive industry and is of great importance. It can improve both feeling of comfort and safety, by means of early detection, isolation and prediction of prospective failures. That is why automotive industry and fleet managers are turning to predictive analytic to maintain a lead position in industry. A patent application has been recently submitted, proposing a two stage solution, including a real-time solution (onboard diagnostic system) and an offline solution (in the back-end), for health monitoring/assessment of different chassis components. Hardware faults are detected based on changes of the fundamental eigen-frequencies of the vehicle where time series of interest, from in-car sensory system, are collected/reported for advanced data analytic in the backend. The main focus of this paper in on the latter solution, using an unsupervised machine learning approach. A clustering approach based on Mixture of Hidden Markov Models, is adopted to conduct automatic diagnosis and isolation of faults. Detection and isolation of tyre and wheel bearing faults has been considered for this study but same framework can be used to handle other components faults, such as suspension system faults. In order to validate the performance of the proposed approach tests were performed at Hallared test track in Gothenburg, and data were collected for two faulty states (for faulty wheel bearing and low-tyre pressure) and no-fault state.