scholarly journals Bearing Remaining Useful Life Prediction Based on a Nonlinear Wiener Process Model

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Juan Wen ◽  
Hongli Gao ◽  
Jiangquan Zhang
Keyword(s):  
Wiener Process ◽  
Particle Filtering ◽  
Process Model ◽  
Experimental Studies ◽  
Prediction Method ◽  
Exponential Model ◽  
Remaining Useful Life ◽  
Maintenance Cost ◽  
Useful Life ◽  
Two Stages

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.

scholarly journals Centralized Maintenance Time Prediction Algorithm for Freight Train Wheels Based on Remaining Useful Life Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Hongmei Shi ◽  
Jinsong Yang ◽  
Jin Si
Keyword(s):  
Wiener Process ◽  
Process Model ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Monitoring Data ◽  
Prediction Algorithm ◽  
Model Parameters ◽  
Freight Train ◽  
Fixed Group ◽  
Useful Life

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.

A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process

2018 ◽  
Vol 127 ◽  
pp. 452-460 ◽  
Author(s):  
Yaogang Hu ◽  
Hui Li ◽  
Pingping Shi ◽  
Zhaosen Chai ◽  
Kun Wang ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Real Time ◽  
Wiener Process ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
The Real ◽  
Useful Life

Remaining useful life prediction for an adaptive skew-Wiener process model

2017 ◽  
Vol 87 ◽  
pp. 294-306 ◽  
Author(s):  
Zeyi Huang ◽  
Zhengguo Xu ◽  
Xiaojie Ke ◽  
Wenhai Wang ◽  
Youxian Sun
Keyword(s):  
Wiener Process ◽  
Life Prediction ◽  
Process Model ◽  
Remaining Useful Life ◽  
Useful Life

scholarly journals A Sequential Bayesian Updated Wiener Process Model for Remaining Useful Life Prediction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 5471-5480 ◽  
Author(s):  
Tianmei Li ◽  
Hong Pei ◽  
Zhenan Pang ◽  
Xiaosheng Si ◽  
Jianfei Zheng
Keyword(s):  
Wiener Process ◽  
Life Prediction ◽  
Process Model ◽  
Remaining Useful Life ◽  
Useful Life

Prognostics of lithium-ion batteries based on different dimensional state equations in the particle filtering method

2016 ◽  
Vol 39 (10) ◽  
pp. 1537-1546 ◽  
Author(s):  
Xiaohong Su ◽  
Shuai Wang ◽  
Michael Pecht ◽  
Peijun Ma ◽  
Lingling Zhao
Keyword(s):  
Lithium Ion Batteries ◽  
Particle Filtering ◽  
Prediction Accuracy ◽  
Lithium Ion ◽  
Exponential Model ◽  
State Equation ◽  
Remaining Useful Life ◽  
State Equations ◽  
Battery Capacity ◽  
Useful Life

Accurate prediction of the remaining useful life of lithium-ion batteries plays a significant role in various devices and many researchers have focused on lithium-ion battery reliability and prognosis. A particle filter (PF) is an effective filter for estimation and prediction of time series data where model structure is available. The prediction accuracy of a PF depends on two key factors: parameter initialization and the state equation. In this paper, parameters are estimated using a PF and two empirical exponential models, i.e. the exponential model and improved exponential model, are used to track the battery capacity degradation; each model uses a different state equation. Experiments were performed to compare prediction accuracy using the related parameters estimation model with that using the capacity decline model; this paper compares the effects of the different state equations on the lithium-ion battery remaining useful life prediction. The experimental results show the merits of the capacity decline model based on particle filtering. The capacity decline model PF is more suitable for estimating the battery capacity trend in the long term.

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
Keyword(s):  
Linear Regression ◽  
Wiener Process ◽  
Inventory Management ◽  
Power Supply ◽  
Process Model ◽  
Critical Role ◽  
Remaining Useful Life ◽  
Wear Model ◽  
Combined Model ◽  
Useful Life

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.

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