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<p>Accurate modeling of performance degradation and dynamic prediction of remaining useful life (RUL) for electric driving system (EDS) with multiple failure modes are critical for reliability assessment and health management. This study presents an integrated framework that encompasses composite health indicator construction, nonlinear degradation modeling, and RUL prediction. The entropy weight method is used to integrate the damage contributions of different components under various failure modes, resulting in a composite health indicator for the EDS. A nonlinear degradation model construction method for the EDS is presented, with failure thresholds and inherent uncertainties determined through actual bench testing over the full life cycle. The model is further refined by incorporating the acceleration factor between the accelerated and user spectra. Additionally, a Bayesian parameter update method is proposed for RUL prediction and uncertainty quantification, users with varying degradation rates across different service cycles are analyzed. The case study demonstrates that after approximately 20% of the service life, the proposed method achieves prediction errors within 10%. As historical data accumulates, model parameter estimates stabilize, and the predicted RUL probability distribution becomes increasingly concentrated. Comparative experiments against Wiener and CNN-LSTM models further demonstrate the superior robustness and accuracy of the proposed method. This study provides specific references and technical support for the reliability assessment and intelligent operation and maintenance of complex systems.</p>

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Nonlinear degradation modeling and remaining useful life prediction for electric drive system with multiple failure modes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月6日

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  • Zhen Wang,
  • Yadong Li,
  • Shuishi Li,
  • Yandong Hou,
  • Weimin Du,
  • Lihui Zhao

摘要

Accurate modeling of performance degradation and dynamic prediction of remaining useful life (RUL) for electric driving system (EDS) with multiple failure modes are critical for reliability assessment and health management. This study presents an integrated framework that encompasses composite health indicator construction, nonlinear degradation modeling, and RUL prediction. The entropy weight method is used to integrate the damage contributions of different components under various failure modes, resulting in a composite health indicator for the EDS. A nonlinear degradation model construction method for the EDS is presented, with failure thresholds and inherent uncertainties determined through actual bench testing over the full life cycle. The model is further refined by incorporating the acceleration factor between the accelerated and user spectra. Additionally, a Bayesian parameter update method is proposed for RUL prediction and uncertainty quantification, users with varying degradation rates across different service cycles are analyzed. The case study demonstrates that after approximately 20% of the service life, the proposed method achieves prediction errors within 10%. As historical data accumulates, model parameter estimates stabilize, and the predicted RUL probability distribution becomes increasingly concentrated. Comparative experiments against Wiener and CNN-LSTM models further demonstrate the superior robustness and accuracy of the proposed method. This study provides specific references and technical support for the reliability assessment and intelligent operation and maintenance of complex systems.