scholarly journals A Robust Model-Based Approach for Bearing Remaining Useful Life Prognosis in Wind Turbines

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 47133-47143
Author(s):  
Wei Teng ◽  
Chen Han ◽  
Yankang Hu ◽  
Xin Cheng ◽  
Lei Song ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Remaining Useful Life ◽  
Model Based ◽  
Robust Model ◽  
Life Prognosis ◽  
Useful Life

scholarly journals Remaining Useful Life Determination for Wind Turbines

2020 ◽  
Vol 1452 ◽  
pp. 012052
Author(s):  
Michael Pagitsch ◽  
Georg Jacobs ◽  
Dennis Bosse
Keyword(s):  
Wind Turbines ◽  
Remaining Useful Life ◽  
Useful Life

scholarly journals A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation

2019 ◽  
Vol 9 (3) ◽  
pp. 613
Author(s):  
Bangcheng Zhang ◽  
Yubo Shao ◽  
Zhenchen Chang ◽  
Zhongbo Sun ◽  
Yuankun Sui
Keyword(s):  
Kalman Filter ◽  
Health Management ◽  
Remaining Useful Life ◽  
Degradation Model ◽  
Time Prediction ◽  
Model Based ◽  
Stochastic Parameters ◽  
Deterioration Process ◽  
Useful Life ◽  
Posterior Estimation

Real-time prediction of remaining useful life (RUL) is one of the most essential works inprognostics and health management (PHM) of the micro-switches. In this paper, a lineardegradation model based on an inverse Kalman filter to imitate the stochastic deterioration processis proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm areused to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solvethe errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data,the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, theeffectiveness of the proposed approach is validated on an experimental data relating tomicro-switches for the rail vehicle. Additionally, it proposes another two methods for comparisonto illustrate the effectiveness of the method with an inverse Kalman filter in this paper. Inconclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors inRUL estimation of the micro-switches excellently.

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