scholarly journals A novel Wiener process model with measurement errors for degradation analysis

2016 ◽  
Vol 18 (03) ◽  
pp. 396-405 ◽  
Author(s):  
Zhihua Wang ◽  
Junxing Li ◽  
Yongbo ZHANG ◽  
Huimin Fu ◽  
Chengrui Liu ◽  
...  
Keyword(s):  
Wiener Process ◽  
Measurement Errors ◽  
Process Model ◽  
Degradation Analysis

A Wiener process model for accelerated degradation analysis considering measurement errors

2016 ◽  
Vol 65 ◽  
pp. 8-15 ◽  
Author(s):  
Junxing Li ◽  
Zhihua Wang ◽  
Xia Liu ◽  
Yongbo Zhang ◽  
Huimin Fu ◽  
...  
Keyword(s):  
Wiener Process ◽  
Measurement Errors ◽  
Process Model ◽  
Accelerated Degradation ◽  
Degradation Analysis

scholarly journals Degradation analysis on high-cycle bending fatigue for woven SiC/SiC composites based on Wiener process model

2021 ◽  
Vol 198 ◽  
pp. 109295
Author(s):  
Xi Liu ◽  
Rongqiao Wang ◽  
Dianyin Hu ◽  
Long Zhang ◽  
Gaoxiang Chen
Keyword(s):  
Wiener Process ◽  
Process Model ◽  
Bending Fatigue ◽  
Degradation Analysis ◽  
Sic Composites

Sample selection of prognostics validation test based on multi-stage Wiener process

2018 ◽  
Vol 233 (4) ◽  
pp. 605-614
Author(s):  
Zhiao Zhao ◽  
Yong Zhang ◽  
Guanjun Liu ◽  
Jing Qiu
Keyword(s):  
Wiener Process ◽  
Process Model ◽  
Failure Modes ◽  
Early Stage ◽  
Sample Selection ◽  
Degradation Process ◽  
Time Interval ◽  
Degradation Data ◽  
Multi Stage ◽  
Selection Of

Sample allocation and selection technology is of great significance in the test plan design of prognostics validation. Considering the existing researches, the importance of prognostics samples of different moments is not considered in the degradation process of a single failure. Normally, prognostics samples are generated under the same time interval mechanism. However, a prognostics system may have low prognostics accuracy because of the small quantity of failure degradation and measurement randomness in the early stage of a failure degradation process. Historical degradation data onto equipment failure modes are collected, and the degradation process model based on the multi-stage Wiener process is established. Based on the multi-stage Wiener process model, we choose four parameters to describe different degradation stages in a degradation process. According to four parameters, the sample selection weight of each degradation stage is calculated and the weight of each degradation stage is used to select prognostics samples. Taking a bearing wear fault of a helicopter transmission device as an example, its degradation process is established and sample selection weights are calculated. According to the sample selection weight of each degradation process, we accomplish the prognostics sample selection of the bearing wear fault. The results show that the prognostics sample selection method proposed in this article has good applicability.

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.

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