journal article·2020-02-29Open access

RUL Prediction of Railway PCCS Based on Wiener Process Model with Unequal Interval Wear Data

Qingluan Guan, Xiukun Wei, Limin Jia, Ye He, Haiqiang Zhang

Applied Sciences· Volume 10· Issue 5

Abstract

The railway pantograph carbon contact strip (PCCS) plays a critical role in collecting the electric current from the catenary to guarantee the steady power supply for the train. The catenary contacts with the PCCS and slides from one side to another side when the train runs on the track, which generates the wear on the surface of the PCCS. The thickness of the PCCS cannot be smaller than a lower limit for the sake of safety. Therefore, the remaining useful life (RUL) prediction of the PCCS is beneficial for the pantograph maintenance and inventory management. In this paper, the wear data from Guangzhou Metro are analyzed in the first place. After that, the challenge of predicting the RUL for PCCS from the unequal interval wear data is addressed. A Wiener-process-based wear model and the unequal interval weighted grey linear regression combined model (UIWGLRCM) are proposed for the RUL prediction of the PCCS. The case studies demonstrate the effectiveness of the proposed method via a comparison of RUL prediction with another available method.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Electrical Contact Performance and Analysis · Railway Engineering and Dynamics · Reliability and Maintenance Optimization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Beijing Research Center of Urban Traffic Information Intelligent Sensing and Service Technologies, Beijing 100044, China

Guangzhou Metro Group Co., Ltd., Guangzhou 510000, China

Funding

National Key R&D Program of China.

References (25)

Show source-provided references
1. Petch, M. (2008). Prognostics and Health Management of Electronics, Wiley Online Library.10.1002/9780470385845 ↗
2. Rotating machinery prognostics: State of the art, challenges and opportunities10.1016/j.ymssp.2008.12.006 ↗
3. Prognostic modeling options for remaining useful life estimation by industry10.1016/j.ymssp.2010.11.018 ↗
4. A review on machinery diagnostics and prognostics implementing condition-based maintenance10.1016/j.ymssp.2005.09.012 ↗
5. A survey on life prediction of equipment10.1016/j.cja.2014.12.020 ↗
6. Residual life predictions from vibration-based degradation signals: A neural network approach10.1109/TIE.2004.824875 ↗
7. A model for residual life prediction based on Brownian motion with an adaptive drift10.1016/j.microrel.2010.09.013 ↗
8. A Wiener-process-based degradation model with a recursive filter algorithm for RUL estimation10.1016/j.ymssp.2012.08.016 ↗
9. A Wiener process-based remaining life prediction method for light-emitting diode driving power in rail vehicle carriage10.1177/1687814019832215 ↗
10. A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process10.1016/j.renene.2018.04.033 ↗
11. An adaptive prognostic approach incorporating inspection influence for deteriorating systems10.1109/TR.2018.2864789 ↗
12. Remaining useful life prediction of lithium-ion batteries based on the Wiener process with measurement error10.3390/en7020520 ↗
13. Degradation data analysis and remaining useful life estimation: A review on wiener-process-based methods10.1016/j.ejor.2018.02.033 ↗
14. Yuan, J. (1991). Grey System Theory and Application, Science Press.
15. Threshold regression for survival analysis: Modeling event times by a stochastic process reaching a boundary10.1214/088342306000000330 ↗
16. Meeker, W., and Escobar, L. (1998). Statistical Methods for Reliability Data, John Wiley & Sons Inc.
17. Residual life predictions in the absence of prior degradation knowledge10.1109/TR.2008.2011659 ↗
18. Direct prediction methods on lifetime distribution of organic light-emitting diodes from accelerated degradation tests10.1109/TR.2010.2040761 ↗
19. Structured replacement policies for components systems with complex degradation processes and dedicated sensors10.1287/opre.1110.0912 ↗
20. Brownian motion with adaptive drift for remaining useful life prediction: Revisited10.1016/j.ymssp.2017.07.015 ↗
21. Remaining lifetime online prediction based on step-stress accelerated degradation modeling
22. Harvey, A.C. (1989). Forecasting Structural Time Series Models and the Kalman Filter, Cambradge University Press.10.1017/CBO9781107049994 ↗
23. Maximum likelihood from incomplete data via the EM algorithm10.1111/j.2517-6161.1977.tb01600.x ↗
24. Li, B., and He, C. (2007, January 18–20). The combined forecasting method of GM(1,1) with linear regression and its application. Proceedings of the IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, China.
25. Control Problems of Grey Systems10.1016/S0167-6911(82)80025-X ↗