Novelty detection framework for monitoring connected vehicle systems with imperfect data

M. Badfar, M. Yildirim and R.B. Chinnam

International Journal of Production Research, 2025, vol. 63, issue 18, 6690-6703

Abstract: Shrinking product development cycles and increasing vehicle complexities necessitate a new generation of monitoring and diagnostic algorithms that can demonstrate increased autonomy and adaptivity. Conventional approaches, which make strict assumptions about data fidelity and failure ground-truth availability, face challenges in modern connected vehicle applications. This paper proposes a novelty detection-based autonomous monitoring framework that flags anomalies under sparse and noisy data with limited or no access to ground-truth information. The framework proposes an optional mechanism for extracting age-degrading features and offers a robust approach for fusing the output of heterogeneous novelty detectors to determine the health state of target components. We validate the proposed framework using connected vehicle data for 12-volt battery systems employed by a large fleet of commercial vehicles of a global automotive manufacturer. To demonstrate versatility, we also tested the framework on bench-testing data from LFP/graphite battery cells. Results demonstrate the effectiveness of the proposed framework.

Date: 2025
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DOI: 10.1080/00207543.2025.2484320

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