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RESEARCH PAPER
Two-stage Remaining Useful Life Prediction Based on the Wiener Process With Multi-feature Fusion and Stage Division
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Limin Jia 3,4
 
 
 
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1
School of Traffic and Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China
 
2
Qingdao Metro Group Corporation LTD, Qingdao 266035, China
 
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School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
 
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State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, 100044, China
 
 
Submission date: 2024-02-22
 
 
Final revision date: 2024-03-31
 
 
Acceptance date: 2024-06-07
 
 
Online publication date: 2024-07-13
 
 
Publication date: 2024-07-13
 
 
Corresponding author
Zhongyi Zuo   

School of Traffic and Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China
 
 
Eksploatacja i Niezawodność – Maintenance and Reliability 2024;26(4):189803
 
HIGHLIGHTS
  • A two-stage RUL prediction method based on the Wiener Process with multi-feature fusion and stage division is proposed.
  • A linear feature fusion method is introduced for the CHI construction.
  • A Z-score outlier detection strategy is introduced to address the issue of stage division in the degradation modeling.
  • The proposed method is explained and the feasibility is proved by three analysis results of bearings.
KEYWORDS
TOPICS
ABSTRACT
Remaining life prediction (RUL) is a critical link of maintenance decision-making, the accurate RUL prediction is an important means to monitor the operating status and achieve the safe operation of equipment. However, existing studies rarely considered the multi-stage characteristics of indicator fusion in the degradation process, and directly used the Wiener process to establish degradation model, which results in significant errors in RUL prediction results. Therefore, to solve above issues, a two-stage RUL prediction method of bearing based on the Wiener process model with data fusion and stage division is proposed in the paper. Firstly, the concept of multi-feature fusion is introduced to construct a comprehensive health indicator (CHI) that considers indicator performance. After that, a two-stage RUL prediction model based on the CHI is developed, and a method for detecting change points and dividing stages is proposed. Finally, the effectiveness and predictability of the proposed method and CHI are demonstrated based on the bearing test datasets.
ACKNOWLEDGEMENTS
This research was funded by the Fundamental Research Funds for the Central Universities (Science and technology leading talent team project, Grant No.2022JBQY007).
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