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

2020 ◽  
Vol 10 (5) ◽  
pp. 1616 ◽  
Author(s):  
Qingluan Guan ◽  
Xiukun Wei ◽  
Limin Jia ◽  
Ye He ◽  
Haiqiang Zhang

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.

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Hongmei Shi ◽  
Jinsong Yang ◽  
Jin Si

Many freight trains for special lines have in common the characteristics of a fixed group. Centralized Condition-Based Maintenance (CCBM) of key components, on the same freight train, can reduce maintenance costs and enhance transportation efficiency. To this end, an optimization algorithm based on the nonlinear Wiener process is proposed, for the prediction of the train wheels Remaining Useful Life (RUL) and the centralized maintenance timing. First, Hodrick–Prescott (HP) filtering algorithm is employed to process the raw monitoring data of wheel tread wear, extracting its trend components. Then, a nonlinear Wiener process model is constructed. Model parameters are calculated with a maximum likelihood estimation and the general deterioration parameters of wheel tread wear are obtained. Then, the updating algorithm for the drift coefficient is deduced using Bayesian formula. The online updating of the model is realized, based on individual wheel monitoring data, while a probability density function of individual wheel RUL is obtained. A prediction method of RUL for centralized maintenance is proposed, based on two set thresholds: “maintenance limit” and “the ratio of limit-arriving.” Meanwhile, a CCBM timing prediction algorithm is proposed, based on the expectation distribution of individual wheel RUL. Finally, the model is validated using a 500-day online monitoring data on a fixed group, consisting of 54 freight train cars. The validation result shows that the model can predict the wheels RUL of the train for CCBM. The proposed method can be used to predict the maintenance timing when there is a large number of components under the same working conditions and following the same path of degradation.


2017 ◽  
Vol 87 ◽  
pp. 294-306 ◽  
Author(s):  
Zeyi Huang ◽  
Zhengguo Xu ◽  
Xiaojie Ke ◽  
Wenhai Wang ◽  
Youxian Sun

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 5471-5480 ◽  
Author(s):  
Tianmei Li ◽  
Hong Pei ◽  
Zhenan Pang ◽  
Xiaosheng Si ◽  
Jianfei Zheng

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Juan Wen ◽  
Hongli Gao ◽  
Jiangquan Zhang

Prognostic is an essential part of condition-based maintenance, which can be employed to enhance the reliability and availability and reduce the maintenance cost of mechanical systems. This paper develops an improved remaining useful life (RUL) prediction method for bearings based on a nonlinear Wiener process model. First, the service life of bearings is divided into two stages in terms of the working condition. Then a new prognostic model is constructed to reflect the relationship between time and bearing health status. Besides, a variety of factors that cause uncertainties toward the degradation path are considered and appropriately managed to obtain reliable RUL prediction results. The particle filtering is utilized to estimate the degradation state, qualify the uncertainties, and predict the RUL. The experimental studies show that the proposed method has a better performance in RUL prediction and uncertainty management than the exponential model and the linear model.


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