An effective residual life prediction method of rolling element bearings based on degradation trajectory analysis

2021 ◽  
Vol 35 (12) ◽  
pp. 5299-5307
Author(s):  
Sifang Zhao ◽  
Qiang Song ◽  
Mingsheng Wang
Author(s):  
Wen-he Wang ◽  
Jun Yi ◽  
Ai-xia Ma ◽  
Shi-ming Shen ◽  
Xiao-chun Yu

According to the corrosion failure characteristics of buried oil and gas pipelines, electrochemical corrosion rates of the local corrosion and pitting corrosion flaws were derived by Faraday’s laws, and the limit size model of corrosion defects was established based upon ASME-B31G criteria. A new method of the residual life prediction for buried pipelines was finally developed based on the corrosion rate model and limit size model of corrosion defects, and the prediction method was validated with an example and the results showed that the method is reliable for pipelines with corroded defects.


2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xin Zhang ◽  
Shuaiwen Tang ◽  
Taoyuan Liu ◽  
Bangcheng Zhang

A new residual life prediction method for complex systems based on Wiener process and evidential reasoning is proposed to predict the residual life of complex systems effectively. Moreover, the better maintenance strategies and decision supports are provided. For the residual life prediction of complex systems, the maximum likelihood method is adopted to estimate the drift coefficient, and the Bayesian method is adopted to update the parameters of Wiener process. The process of parameters estimation and the probability density function (PDF) of the residual life are deduced. To improve the accuracy of the residual life prediction results, the evidential reasoning (ER) is used to integrate the prediction results of Wiener process. Finally, a case study of gyroscope is examined to illustrate the feasibility and effectiveness of the proposed method, compared with fuzzy theory, which provides an important reference for the optimization of the reliability of complex systems and improvement.


2018 ◽  
Vol 8 (8) ◽  
pp. 1373 ◽  
Author(s):  
Qian Li ◽  
Kehong Lv ◽  
Jing Qiu ◽  
Guanjun Liu

Based on the dynamic properties of electrical connector intermittent failure, the model and methods for residual life prediction for electrical connectors are studied in this paper. Firstly, the mechanism of electrical connector intermittent failure is analyzed, and the area enclosed by the contact resistance curve and the fault threshold is defined as the generalized severity of intermittent failure to describe how severe the electrical connector’s intermittent failure is. Then, the Hidden Semi-Markov Model (HSMM) is introduced to build the residual life prediction model of the electrical connector. Further, the evaluation method of using the state and prediction method for residual life are studied. Finally, by carrying out the residual life prediction test, the effectiveness of the residual life prediction method for electrical connectors based on intermittent failure and HSMM is verified.


2014 ◽  
Vol 1030-1032 ◽  
pp. 1133-1137
Author(s):  
Xin Liu ◽  
Yun Xian Jia ◽  
Jie Zhou

Residual life prediction is a critical and difficult problem in condition-based maintenance decision-making. Aiming to deal with the problems that practical data is limited and the estimation of initial parameters is not accurate in maintenance practice, a residual life prediction method for gearbox based on stochastic filtering (SF) is proposed. In this method, recursive expectation maximization (REM) algorithm is introduced to update the parameters, and a maximum likelihood estimation method is designed to update the unknown parameters. Finally, the validity and practicability of the model are validated by an example.


Author(s):  
Ming-Yi You ◽  
Guang Meng

Similarity based residual life prediction method is an emerging method for component residual life prediction. Studies on (a) the effect of weight function on prediction accuracy; (b) prediction robustness; and (c) prediction uncertainty of such method are rare. However, the abovementioned factors are essential concerns for wide application of a similarity based residual life prediction method. In this article, the essential elements of a similarity based residual life prediction method is outlined first with an extended weight function introduced. Afterward, an evaluation framework for investigating the prediction robustness of a similarity based residual life prediction method is established. In addition, a prediction uncertainty estimation method is proposed based on historical samples, inspired by cross-validation technique. In an extensive numerical investigation, a comparative study on the effect of weight function on prediction accuracy is conducted by tuning the parameters in the weight function. The prediction robustness of the similarity based residual life prediction method is evaluated in comparison with a time-series forecasting based residual life prediction method. Finally, the proposed prediction uncertainty estimation method is illustrated, which may facilitate further application of the similarity based residual life prediction method.


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