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Severity-based diagnosis for vehicular electric systems with multiple, interacting fault modes

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  • Peters, Benjamin
  • Yildirim, Murat
  • Gebraeel, Nagi
  • Paynabar, Kamran

Abstract

Complex systems are comprised of multiple components that continuously interact in terms of how they degrade and fail. Diagnosing fault severity and causes of failures in these systems is often a non-trivial task. To address this challenge, we propose a data-driven, severity-based diagnosis framework for systems with multiple, interacting fault modes. We focus on the components of the automotive electric power generation and storage system, specifically, the Vehicle-Engine Start system comprised of the battery and the start-stop starter. Our framework leverages sensor data from several component-fault severity combinations. Using multiple feature extraction tools, we train separate classifiers using Regularized Multinomial Regression, and combine the performance of the classifiers using ensemble methods. We demonstrate the effectiveness of our approach by performing degradation-based diagnostic tests utilizing a real-world engine test-rig.

Suggested Citation

  • Peters, Benjamin & Yildirim, Murat & Gebraeel, Nagi & Paynabar, Kamran, 2020. "Severity-based diagnosis for vehicular electric systems with multiple, interacting fault modes," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:reensy:v:195:y:2020:i:c:s0951832019304661
    DOI: 10.1016/j.ress.2019.106605
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    References listed on IDEAS

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    1. Fang, Xiaolei & Paynabar, Kamran & Gebraeel, Nagi, 2017. "Multistream sensor fusion-based prognostics model for systems with single failure modes," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 322-331.
    2. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
    3. Manjurul Islam, M.M. & Kim, Jong-Myon, 2019. "Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 55-66.
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    Cited by:

    1. Yang, Zhe & Baraldi, Piero & Zio, Enrico, 2022. "A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Liu, Yi & Xiang, Hang & Jiang, Zhansi & Xiang, Jiawei, 2023. "Second-order transient-extracting S transform for fault feature extraction in rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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