基于LSTM算法的电子部件故障预测

故障诊断与预测使用大量信息数据,需要采用推断统计、神经网络等研究方法,对测试数据进行分析和预测,从而评估设备健康状况,在故障发生前对指标进行预测和采取预防举措,最大限度保证电子设备健康工作。本文提出了基于LSTM(长短时记忆网络)算法的电子设备部件故障预测模型,针对时序型数据对轴承运行状态进行分析和预测。

Fault prediction for the bearing based on LSTM

Fault diagnosis and prediction use a large amount of information data,using inferential statistics,neural network and other research methods to analyze and predict test data,thereby assessing the health of the equipment,predicting indicators and taking preventive measures before the failure occurs,maximizing electronic assurance The equipment works healthily.This paper proposes an electronic equipment component fault prediction model based on LSTM (long and short time memory network) algorithm,which analyzes and predicts the bearing operating state for time series data.