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Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven

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  • Xiangang Cao

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
    Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring, Xi’an 710054, China)

  • Pengfei Li

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Song Ming

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

Currently, the Remaining Useful Life (RUL) prediction accuracy of stochastic deterioration equipment is low. Existing researches did not consider the impact of imperfect maintenance on equipment degradation and maintenance decisions. Therefore, this paper proposed a remaining useful life prediction-based maintenance decision model under data-driven to extend equipment life, promoting sustainable development. The stochastic degradation model was established based on the nonlinear Wiener process. A combination of real-time update and offline estimation estimated the degradation model’s parameters and deduced the equipment’s RUL distribution. Based on the RUL prediction results, we established a maintenance decision model with the lowest long-term cost rate as the goal. Case analysis shows that the model proposed in this paper can improve the accuracy of RUL prediction and realize equipment sustainability.

Suggested Citation

  • Xiangang Cao & Pengfei Li & Song Ming, 2021. "Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven," Sustainability, MDPI, vol. 13(15), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8548-:d:605805
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    References listed on IDEAS

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    Cited by:

    1. Chen, Xiaowu & Liu, Zhen, 2022. "A long short-term memory neural network based Wiener process model for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Hou, WanJun & Peng, Yizhen, 2025. "Enhancing bearing life prediction: Sparse Gaussian process regression approach based on sequential ensemble and residual reduction for degradation prediction," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    3. Andrey A. Radionov & Ivan V. Liubimov & Igor M. Yachikov & Ildar R. Abdulveleev & Ekaterina A. Khramshina & Alexander S. Karandaev, 2023. "Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data," Energies, MDPI, vol. 16(12), pages 1-27, June.

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