Research on the Life Prediction Method of Meters Based on a Nonlinear Wiener Process

Abstract

Due to the high reliability of present meters, it is difficult to obtain the failure time of meters through accelerated life tests. Based on the failure data of the accelerated life test, this paper studies the mathematical model based on the Wiener process and establishes the degradation model of the instrument by the maximum likelihood to estimate the parameters of the Wiener model. With full consideration of the possible nonlinear effects in modeling, the time scale transformation method is used to study and obtain the reliability life prediction model of smart meters based on nonlinear data. Finally, the reliability life prediction model of meters is verified and evaluated through the example data of the accelerated life test of smart meters. Compared with the conventional method, this method has less error in calculating the reliability, greatly saves test time, and has a higher accuracy than estimating the lifetime model using the Wiener process directly.

Smart meter failure prediction based on weakened grey markov model with IOWA operator

“…Reference [1] addresses the issue of reliability prediction for smart meters and proposes a Time Delayed Bayesian Network (TDBN) model, which updates conditional probability tables by adding cross-correlation coefficients and time shifts to improve prediction accuracy. Reference [2] builds a degradation model for meters based on failure data from accelerated lifespan tests and estimates the parameters of the Wiener model using the Maximum Likelihood method, thereby introducing a smart meter reliability and lifespan prediction model that accounts for nonlinear effects. Reference [3] uses polynomial regression to establish the relationship between the physical properties and lifespan of smart meters based on daily operational data of smart meters and regional transformers, thus creating a lifespan prediction model.…”

“…The aforementioned references [1][2][3][4] mainly focus on predicting the remaining lifespan of smart meters, while references [5][6][7][8][9] pertain to the prediction of smart meter fault types, involving various stress models that require substantial computational resources and are challenging to apply in industrial practice. Reference [10] addresses the prediction of smart meter fault data.…”