Network-based relevant feature identification and early detection of impending faults

As more electronic sensors for control units are added into next generation vehicles, and linked to communication infrastructure, greater amount of operational data has become unprecedentedly available close to real time. The ability to extract patterns in such real-time massive operational data to detect, isolate, predict, and mitigate faults is the key to enhance vehicle ownership experiences. In this paper, we present a Network Predictive Analytics (NPA) methodology for individualized, evidence-based, preventive vehicle health management. We leverage a recent novel measure called the Maximal Information Coefficient (MIC) — a method that computes an information-theoretic score for quantifying the association strength between two time-series, regardless of the type of relationship. Built upon MIC, we develop an automated method to extract fault-specific relevant features from Association Feature Networks by clustering and ranking dynamic feature relations. By monitoring identified features, our novel rate-based spectral Early Warning Signal (EWS-rate) signals further detects impending faults prior to their repairs. We study the effectiveness of our methodology over a set of data collected from real vehicles. The result shows the proposed Network Predictive Analytics (NPA) methodology can provide significant lead time in early detection of impending faults with high accuracy.