Open AccessGaussian Process based Remaining useful life Prediction for Electric Energy Metering Equipment
Author(s) -
Ning Li,
Junfeng Duan,
Jun Ma,
Wei Qiu,
Wei Zhang,
Zhaosheng Teng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2125/1/012032
Subject(s) - kernel density estimation , kernel (algebra) , weibull distribution , gaussian process , markov chain monte carlo , metering mode , computer science , monte carlo method , bayesian probability , gaussian , reliability engineering , engineering , statistics , mathematics , artificial intelligence , mechanical engineering , physics , combinatorics , quantum mechanics , estimator
Electric energy metering equipment (EEME) will fail in advance not as designed running in extreme environments. A multi-kernel Gaussian process regression model using measurement error data to perceive remaining useful life (RUL) for EEME is proposed. Firstly, the gauss kernel and periodic kernel are used to match the health index trend of EEME under a variety of typical environmental stresses. Furthermore, the Bayesian method and Monte Carlo Markov chain method are used to solve the model, and the Weibull distribution is used to fit the posterior trajectory to get the probability density estimation of the RUL.