Measurement error prediction-based reliability assessment framework for electric metering devices under harsh natural environments

• Published in: Measurement : journal of the International Measurement Confederation 2024-06, Vol.232, p.114481, Article 114481
• Main Authors: Ma, Lisha, Teng, Zhaosheng, Tang, Qiu, Wan, Ziping, Li, Ning, Meng, Zhiqiang
• Format: Article
• Language: English
• Subjects: Bi-directional outlier detection; Electric metering devices (EMDs); Harsh natural environment; Measurement error (ME); Multi-stress fusion nonlinear degradation (MFND); Reliability assessment
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2024.114481

•To reduce the impact of outliers on the reliability assessment of electric metering devices (EMDs), a novel bi-directional (BD) outlier detection approach is proposed, which employs MCD robust analysis and Thompson tau to identify horizontal and longitudinal outliers in the measurement error (ME) observations, respectively. The BD detection method can effectively avoid misdetections and omissions.•Then, a Wiene-based multi-stress fusion nonlinear degradation (MFND) model is constructed to establish the relationship between multiple stresses and the ME data. It can provide future input values for environmental stresses according to the characteristics of the environmental stress fusion term. The fluctuation trend of ME under time-varying stresses can be effectively tracked using MFND, and its parameters can be greatly interpreted via affine invariant ensemble MCMC (GWMCMC).•A reliability assessment framework that can characterize the dynamic performance of EMDs is proposed by combining the BD outlier detection method and the MFND model, which can accurately predict the trend of ME data. Then, the reliability of batch devices is assessed based on the predicted pseudo-life, and recommendations are made for maintenance intervals. Finally, we conduct extensive experimental tests using ME data from smart meters, and the results demonstrate that our framework possesses exceptional predictive capability in real-world harsh environments, affirming its effectiveness. Reliability assessment for electric metering devices (EMDs), which includes environmental stress analysis, measurement error (ME) prediction, and reliability estimation, can be utilized for predictive instrument maintenance, especially under harsh environmental stresses. Nevertheless, the actual evaluation process is limited by data noise and the unavailability of future environmental inputs for the degradation model. To this end, we first extract the main environmental factors affecting ME and then fuse them using the weighted principal component analysis (WPCA) method to provide future environmental input values for the prediction model. Next, a bi-directional (BD) outlier detection method based on MCD robust analysis and the Thompson tau method is proposed to detect outliers from horizontal and longitudinal perspectives. Then, an ME prediction method called multi-stress fusion nonlinear degradation (MFND) is put forward, which considers the cyclic variation of ME over time and the effect of environm