ArticleSensors (Basel, Switzerland)2022
Failure Prediction and Replacement Strategies for Smart Electricity Meters Based on Field Failure Observation.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Smart meter health state prediction based on residual-connected self-attention ConvLSTM network.Scientific reports · 2026Article
- Survival Models for Predictive Maintenance and Remaining Useful Life in Sensor-Enabled Smart Energy Networks: A Review.Sensors (Basel, Switzerland) · 2026Review
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Authors and funding
5 authors.
Funding
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Abstract
It is helpful to have a replacement strategy by predicting the number of failures of in-service electricity meters. This paper presents a failure number prediction method for smart electricity meters based on on-site fault data. The prediction model was constructed by combining Weibull distribution with odds ratios, then the distribution parameters, failure prediction number, and confidence intervals of prediction number were calculated. A strategy of meter replacement and reserve were developed according to the prediction results. To avoid the uncertainty of prediction results due to the small amount of field data information, a Bayesian failure number prediction method was developed. The research results have value for making operation plans and reserve strategies for electricity meters.
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Registered trials
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