计算机工程与应用2024，Vol.60Issue(14)：329-336,8.DOI:10.3778/j.issn.1002-8331.2304-0335
面向感应电机故障诊断的深度学习方法研究
Research on Deep Learning Method for Induction Motor Fault Diagnosis
摘要
Abstract
Vibration signals can effectively reflect the operating status of the motor,and are therefore considered an important basis for diagnosing induction motor faults.However,the original vibration signal has the problem of single features and long time series,and existing research usually extracts features based on expert experience,which is costly.In recent years,the accumulation of fault data has promoted the application of deep learning methods in fault diagnosis.A feature engineering method(MAC-LSTM)based on multi-attention mechanism and one-dimensional convolutional neural network is proposed for fault diagnosis of induction motors,which does not require any prior knowledge.Firstly,the multi-attention mechanism is used to expand the dimensionality of features,making the representation of original features more abundant.Secondly,the convolutional neural network extracts features from the time dimension and reduces the dimensionality,effectively solving the problem of the original signal timing being too long.Finally,LSTM captures the temporal dependence of the signal for fault diagnosis of induction motors.The experimental results show that MAC-LSTM has achieved excellent performance in fault diagnosis of induction motors based on vibration signals and has high generali-zation ability.关键词
感应电机/故障诊断/注意力机制/卷积神经网络/长短期记忆神经网络Key words
induction motor/fault diagnosis/attention mechanism/convolutional neural network/long and short term memory neural network分类
信息技术与安全科学引用本文复制引用
李莎莎,石颉..面向感应电机故障诊断的深度学习方法研究[J].计算机工程与应用,2024,60(14):329-336,8.基金项目
国家自然科学基金(62073231). （62073231）
