RESEARCH PAPER
Two-stage Remaining Useful Life Prediction Based on the Wiener Process With Multi-feature Fusion and Stage Division
1
School of Traffic and Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China
2
Qingdao Metro Group Corporation LTD, Qingdao 266035, China
3
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
4
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
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).
REFERENCES (38)
1.
Vichare N M, Pecht M G. Prognostics and health management of electronic. IEEE Transactions on Components and Packaging Technologies, 2006, 29(1): 222-229, https://doi.org/10.1109/TCAPT.....
2.
Camci F, Chinnam R B. Health-state estimation and prognostics in machining processes, IEEE Transactions on Automation Science and Engineering, 2010, 7(3): 581-583, https://doi.org/10.1109/TASE.2....
3.
Li K, Ren L, Li X, et al. Remaining useful life prediction of equipment considering dynamic thresholds under the influence of maintenance. Eksploatacja i Niezawodnosc - Maintenance and Reliability, 2024, 26(1): 174903, https://doi.org/10.17531/ein/1....
4.
Wang Z, Shangguan W, Peng C, et al. Similarity based remaining useful life prediction for lithium-ion battery under small sample situation based on data augmentation. Eksploatacja i Niezawodnosc -Maintenance and Reliability, 2024, 26(1): 175585, https://doi.org/10.17531/ein/1....
5.
Lei Y G, Li N P, Guo L, et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction, Mechanical systems and signal processing, 2018, 104: 799-834, https://doi.org/10.1016/j.ymss....
6.
Zhang Z X, Si X S, Hu C H, et al. Degradation data analysis and remaining useful life estimation: A review on Wiener process-based methods, European Journal of Operational Research, 2018, 271(3): 775-796, https://doi.org/10.1016/j.ejor....
7.
Pecht M. Prognostics and health management of electronics, New Jersey: Wiley Online Library, 2008, https://doi.org/10.1002/978047....
8.
Cheng Y, Wu J, Zhu H, et al. Remaining useful life prognosis based on ensemble long short-term memory neural network, IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1-12, https://doi.org/10.1109/TIM.20....
9.
Wu J, Hu K, Cheng Y, et al. Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network, ISA transactions, 2020, 97: 241-250, https://doi.org/10.1016/j.isat....
10.
Chen J L, Jing H J, Chang Y H, et al. Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process, Reliability Engineering & System Safety, 2019, 185: 372-382, https://doi.org/10.1016/j.ress....
11.
Hu C, Youn B D, Wang P F, et al. Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life, Reliability Engineering & System Safety, 2012, 103: 120-135, https://doi.org/10.1016/j.ress....
12.
Li P, Liu X, Yang Y. Remaining useful life prognostics of bearings based on a novel spatial graph-temporal convolution network. Sensors (Basel), 2021, 21(12): 4217, https://doi: 10.3390/s21124217.
13.
Li Y X, Huang X Z, Ding P F, et al. Wiener-based remaining useful life prediction of rolling bearings using improved Kalman filtering and adaptive modification, Measurement, 2021, 182: 109706, https://doi.org/10.1016/j.meas....
14.
Liu M J, Dong Z S, Shi H, et al. Remaining useful life estimation of fan slewing bearings in nonlinear Wiener process with random covariate effect, Shock and Vibration, 2022, 2022: 1-19, https://doi.org/10.1155/2022/5....
15.
Anowar F, Sadaoui S, Selim B. Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Computer Science Review, 2021, 40: 100378, https://doi: 10.1016/j.cosrev.2021.100378.
16.
Widodo A, Yang B S. Application of relevance vector machine and survival probability to machine degradation assessment, Expert Systems with Applications, 2011, 38(3): 2592-2599, https://doi.org/10.1016/j.eswa....
17.
Liu Y C, Hu X F, Zhang W J. Remaining useful life prediction based on health index similarity. Reliability Engineering & System Safety, 2019, 185: 502-510, https://doi.org/10.1016/j.ress....
18.
Benkedjouh T, Medjaher K, Zerhouni N, et al. Health assessment and life prediction of cutting tools based on support vector regression, Journal of Intelligent Manufacturing, 2015, 26: 213-223, https://doi.org/10.1007/s10845....
19.
Liu D, Duan X C, Wang S P, et al. A reliability estimation method based on signal feature extraction and artificial neural network supported Wiener process with random effects, Applied Soft Computing, 2023, 136: 110044, https://doi.org/10.1016/j.asoc....
20.
Aye S, Heyns P. An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission, Mechanical Systems and Signal Processing, 2017, 84: 485-498, https://doi.org/10.1016/j.ymss....
21.
Song C Y, Liu K B. Statistical degradation modeling and prognostics of multiple sensor signals via data fusion: A composite health index approach, IISE Transactions, 2018, 50(10): 853-867, https://doi.org/10.1080/247258....
22.
Li Z, Wu J, Yue X. A shape-constrained neural data fusion network for health index construction and residual life prediction, IEEE Transactions on Neural Networks and Learning Systems, 2020, 32(11): 5022-5033, https://doi.org/10.1109/TNNLS.....
23.
Li N P, Gebraeel N, Lei Y G, et al. Remaining useful life prediction based on a multi-sensor data fusion model, Reliability Engineering & System Safety, 2021, 208: 107249, https://doi.org/10.1016/j.ress....
24.
Wu B, Zeng J, Shi H, et al. Multi-sensor information fusion-based prediction of remaining useful life of nonlinear Wiener process, Measurement Science and Technology, 2022, 33(10): 105106, https://doi.org/10.1088/1361-6....
25.
Chen Z, Xia T B, Zhou D, et al. A health index construction framework for prognostics based on feature fusion and constrained optimization. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-15, https://doi: 10.1109/TIM.2021.3104414.
26.
Liu M J, Dong Z S, Shi H. Multi-Sensor Information Fusion and Multi-Model Fusion-Based Remaining Useful Life Prediction of Fan Slewing Bearings with the Nonlinear Wiener Process, Sustainability, 2023, 15(15): 12010, https://doi.org/10.3390/su1515....
27.
Wang F T, Liu X T, Deng G, et al. Remaining life prediction method for rolling bearing based on the long short-term memory network, Neural processing letters, 2019, 50: 2437-2454, https://doi.org/10.1007/s11063....
28.
Zhang B, Zhang L J, Xu J W. Degradation feature selection for remaining useful life prediction of rolling element bearings, Quality and Reliability Engineering International, 2016, 32(2): 547-554, https://doi.org/10.1002/qre.17....
29.
Liao L X. Discovering prognostic features using genetic programming in remaining useful life prediction, IEEE Transactions on Industrial Electronics, 2013, 61(5): 2464-2472, https://doi.org/10.1109/TIE.20....
30.
Pan Y P, Er M J, Li X, et al. Machine health condition prediction via online dynamic fuzzy neural networks, Engineering Applications of Artificial Intelligence, 2014, 35: 105-113, https://doi.org/10.1016/j.enga....
31.
Guo L, Li N P, Jia F, et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings, Neurocomputing, 2017, 240: 98-109, https://doi.org/10.1016/j.neuc....
32.
Chen D, Qin Y, Wang Y, Zhou J. Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction. ISA transactions, 2021, 114: 44-56, https://doi.org/10.1016/j.isat....
33.
Shakya P, Kulkarni M S, Darpe A K. A novel methodology for online detection of bearing health status for naturally progressing defect, Journal of Sound and Vibration, 2014, 333(21): 5614-5629, https://doi.org/10.1016/j.jsv.....
34.
Guan Q L, Wei X K, Bai W F, et al. Two-stage degradation modeling for remaining useful life prediction based on the Wiener process with measurement errors, Quality and Reliability Engineering International, 2022, 38(7): 3485-3512, https://doi.org/10.1002/qre.31....
35.
Lee M, Whitmore G A. Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary, Statistical Science, 2006, 21(4): 501-513, https://doi.org/10.1214/088342....
36.
Rauch H E, Tung F, Striebel C T. Maximum likelihood estimates of linear dynamic systems. AIAA Journal. 1965, 3, 1445-1450, https://doi.org/10.2514/3.3166.
37.
Wang B, Lei Y G, Li N P, et al. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings, IEEE Transactions on Reliability, 2018, 69(1): 401-412, https://doi.org/10.1109/TR.201....
38.
Si X S, Wang W, Hu C H, et al. A Wiener-process-based degradation model with a recursive filter algorithm for RUL estimation, Mechanical Systems and Signal Processing, 2013, 35(1-2): 219-237, https://doi.org/10.1016/j.ymss....
CITATIONS (2):
1.
Novel nonlinear Wiener process degradation modeling and remaining life prediction
Caihua Peng, Jianhua Li, Lina Ren, Jintao Chen
Journal of the Brazilian Society of Mechanical Sciences and Engineering
Caihua Peng, Jianhua Li, Lina Ren, Jintao Chen
Journal of the Brazilian Society of Mechanical Sciences and Engineering
2.
Combined use of multiple entropy and weighted square envelope for remaining useful life prediction of railway gearbox with Bayesian deep learning
Youze Chen, Teng Wang, Rui Bai, Dongxiao Li, Yongbo Li
Journal of Vibration and Control
Youze Chen, Teng Wang, Rui Bai, Dongxiao Li, Yongbo Li
Journal of Vibration and Control
Share
RELATED ARTICLE
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
