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Novelty detection framework for monitoring connected vehicle systems with imperfect data

Author

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  • M. Badfar
  • M. Yildirim
  • R.B. Chinnam

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.

Suggested Citation

  • M. Badfar & M. Yildirim & R.B. Chinnam, 2025. "Novelty detection framework for monitoring connected vehicle systems with imperfect data," International Journal of Production Research, Taylor & Francis Journals, vol. 63(18), pages 6690-6703, September.
  • Handle: RePEc:taf:tprsxx:v:63:y:2025:i:18:p:6690-6703
    DOI: 10.1080/00207543.2025.2484320
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