Fault Prediction And Precise Maintenance Of Drop-out Fuses
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Relying on drop out fuse online current and temperature monitoring, action count and leakage statistics, partial discharge capture and insulation status trend edge acquisition, combined with rule engine and lightweight machine learning model, anomalies are classified and located. Operation and maintenance are mainly based on remote diagnosis, targeted inspection and replacement plan, and the fault closed loop is improved by using historical action and environmental correlation analysis.
