A Reliability Evaluation Model for Smart Meters Based on Survival Analysis Theory

Smarter meter is one kind of electronic meter with multiple functions such as remote control, anomaly monitoring, edge computing, etc. Smarter meter is the basic unit of the energy metering system and the perception terminal of the electricity information collection system. The large quantity also brings difficulties while smart meters are widely deployed on the user side. Aiming at reducing the human and material resources which are spent on maintaining smart meters, A reliability evaluation model for smart meters based on multivariate big data fusion is built to evaluate smart meters’ operational reliability. Multivariate big data is merged and collated to obtain the covariate data and the smart meter survival label that affect the life span of smart meters. Based on the survival analysis theory, a cox proportional-hazards(CoxPH) model for the life cycle of a smart meter is established, and a deep neural network is used to characterize strongly non-linear correlation parameters to form a reliability evaluation model for the smart meter. Based on the actual operation and maintenance data of smart meters of some city, the effectiveness of the proposed model is verified. Test results show that the proposed model, which provides subsidiarity for smart meter maintenance, and can estimate reliability of the smart meter successfully based on its real-time running status.