基于CK-GPR的多应力环境智能电表剩余寿命预测

Remaining useful life prediction of smart meter based on CK-GPR in multi-stress environment

针对智能电表科学定期更换需求,建立一种基于智能电表基本误差数据的剩余寿命(RUL)预测方法。首先采用Person相关系数筛选对智能电表基本误差数据影响较大的环境应力作为模型输入;然后采用高斯核、Matern32核及周期核匹配多应力环境下智能电表基本误差趋势;利用贝叶斯方法和蒙特卡洛马尔科夫链(MCMC)求解模型。实验结果表明,不同公司智能电表具有不同环境耐受性,在高干热典型环境条件下,A公司智能电表数据后验上四分位线达到阈值,剩余寿命为43个月;B公司智能电表未出现普遍失效情况,但未来47个月会有较大可能失效,应着手进行故障排查和误差检定工作。在高干热典型环境下智能电表加速超差失效现象不符合计量规程规定的8年检定周期,应动态调整周期检定工作。

Aiming at the demand for scientific periodic replace of smart meters, a remaining useful life(RUL) prediction method based on the basic error data of smart meters is established. Firstly, the Person correlation coefficient is adopted to screen out the environmental stress that has great impact on the basic error data of the smart meter as the model input;then the Gaussian kernel, the Matern32 kernel, and the periodic kernel are adopted to match the basic error trend of the smart meter under the multi-stress environment;the Bayesian method and the Monte Carlo Markov Chain(MCMC) are used to solve the model. Experiment results show that smart meters from different companies have different environmental tolerances. Under typical environmental condition of high dry heat, the posterior upper quartile value of the smart meters from A company reaches the threshold, and the RUL is 43 months;the smart meters from company B has no general failures happened, however there will be a great possibility to failure in the next 47 months, and troubleshooting and error verification should be started. In the typical environment of high dry heat, the accelerated out-of-tolerance failure phenomenon of smart meters does not meet the 8-year verification period stipulated in the measurement regulations, and the periodic verification work should be dynamically adjusted.