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A novel transfer gear life prediction method by the cross-condition health indicator and nested hierarchical binary-valued network

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  • Chen, Dingliang
  • Cai, Wei
  • Yu, Hangjun
  • Wu, Fei
  • Qin, Yi

Abstract

As one of the crucial techniques in prognostics and health management, the long-term remaining useful life (RUL) prediction of gears is essential for maintenance decisions in engineering applications. However, most previous RUL prediction approaches may have unacceptable prediction performance due to insufficient prior life-cycle data and variable operating conditions. In this paper, a novel transfer life prediction method is proposed for gear RUL estimation under different working conditions. First, a health indicator (HI) transfer construction framework, QFMDCAET, is developed for generating cross-condition HIs under various working conditions based on a quadratic function-based multi-scale deep convolutional auto-encoder network and multi-kernel maximum mean discrepancy. With the acquired gear HIs, the nested hierarchical binary-valued network (NHBN) is designed to estimate the gear RUL. In NHBN, a Gumbel function is applied to improve the memory ability of NHBN. Moreover, a nested hierarchical mechanism is also used to fully utilize the sequence information of the cell state in NHBN. The experimental results illustrate the effectiveness of the proposed NHBN in gear RUL prediction, especially for long-term RUL prediction. Lastly, the comparison results indicate that the proposed NHBN-based RUL prediction method is superior to other conventional and advanced RUL prediction methods.

Suggested Citation

  • Chen, Dingliang & Cai, Wei & Yu, Hangjun & Wu, Fei & Qin, Yi, 2023. "A novel transfer gear life prediction method by the cross-condition health indicator and nested hierarchical binary-valued network," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
  • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023003046
    DOI: 10.1016/j.ress.2023.109390
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    References listed on IDEAS

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    1. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Luo, Hao & Yin, Shen, 2023. "An integrated multi-head dual sparse self-attention network for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
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    Cited by:

    1. Zhou, Haoxuan & Wang, Bingsen & Zio, Enrico & Wen, Guangrui & Liu, Zimin & Su, Yu & Chen, Xuefeng, 2023. "Hybrid system response model for condition monitoring of bearings under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    2. Zhang, Yadong & Zhang, Chao & Wang, Shaoping & Dui, Hongyan & Chen, Rentong, 2024. "Health indicators for remaining useful life prediction of complex systems based on long short-term memory network and improved particle filter," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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