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

Xiangang Cao, Pengfei Li and Song Ming
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Xiangang Cao: School of Mechanical Engineering, Xi’an University of Science and Technology, 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

Sustainability, 2021, vol. 13, issue 15, 1-19

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.

Keywords: stochastic deterioration equipment; data-driven; nonlinear Wiener process; RUL prediction; maintenance decision; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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