A pattern recognition approach for anomaly detection on buses brake system

Diagnosis of complex systems refers to the problem of identifying a breakdown or a failure based on an inspection, a control or a test. Monitoring such industrial complex systems is essential to schedule relevant maintenance actions. We consider an automotive subsystem to monitor: the brake system, because of its impact on the vehicles availability. Through a European project [1], data are acquired via in-vehicle communication protocols and additional sensors. This work aims at developing remote diagnostic and maintenance support tools driven by these data. Our approach combines an analytic model and detection techniques in order to monitor the brake system. We provide experimental results on vehicle data using two multivariate detection methods.