Innovative analytics to estimate the probability of failure and remaining useful life of medium-voltage breakers

2017 ◽  
Vol 2017 (1) ◽  
pp. 2379-2382 ◽  
Author(s):  
Massimo Scarpellini ◽  
Kevin Perdon ◽  
Luca Cavalli ◽  
Marco Testa
Author(s):  
Dragan Stevanovic

In this paper, Weibull and Poisson distribution calculation are carried out with new data to conclude a conclusion are they suitable for circuit breakers remaining useful life assessment (RUL). Old data are covering a 10 years period consisting of measured voltage drop on CB contacts and number of tripped short circuit faults. In this paper, new data, from the last 3 years, would be used to make a comparison with old data and make conclusions have been probability distributions correctly chosen.


2005 ◽  
Vol 48 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Matthew Watson ◽  
Carl Byington ◽  
Douglas Edwards ◽  
Sanket Amin

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.


Author(s):  
Renxiong Liu

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Bincheng Wen ◽  
Mingqing Xiao ◽  
Guanghao Wang ◽  
Zhao Yang ◽  
Jianfeng Li ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document