基于改进LSTM模型的电气设备故障预测与诊断模型研究

打开文本图片集

中图分类号：TM507 文献标志码：A 文章编号：2095-2945（2025）27-0124-04

Abstract：Toenhance theacuracyandreal-time performanceoffaultdiagnosisforpower equipment，thispaper proposesa faultpredictionanddiagnosissystemforelectrcalequipmentbasedonmachinelearningtechnologyByintegratingmulti-source sensordata，optimizingsignalprocessingalgorithms，andimprovingtheeepleamingmodel，itachieveseficientidentification andprecisediagnosisofvariousfaultsinpowerequipment.ExperimentalresultsshowthattheimprovedLongShort-Term Memory（LSTM）neuralnetworkmodeloutperformstraditionalmethodsintermsoffaultrecognitionaccuracyandtimelinessThe systememonstratesexcelentanti-interferenceabilityandgeneralizationperformance，andcanadapttocomplexandvariable operatingenvironments.Theresearchnotonlyprovidesinnovativesolutionsforintellgentmonitoringandmaintenanceofpower equipment，butalsoenrichesthetheoreticalsysteminthefieldoffaultdiagnosis.Ithasimportanttheoreticalsignfcancend practical value，and can provide strong support for the safe and stable operation of power systems.

Keywords： improvedLSTM model; electrical equipment; fault prediction; fault diagnosis; inteligent monitoring

随着电力系统规模的持续扩展，其运行越来越复杂，设备故障的预测与诊断已成为保障电力系统稳定性与可靠性的关键环节。（剩余4350字）