计算机与数字工程2024，Vol.52Issue(5)：1536-1540,5.DOI:10.3969/j.issn.1672-9722.2024.05.047
基于深度学习的电机故障诊断
Motor Fault Diagnosis Based on Deep Learning
摘要
Abstract
Fault diagnosis plays a very important role in ensuring the stable operation of motor.Therefore,fault diagnosis is a hot topic in current research.In this study,the short-time Fourier transform is used to transform the one-dimensional vibration sig-nal into a two-dimensional time-frequency diagram,so as to solve the nonlinear and instability problems of the vibration signal of the motor bearing.As the input of the convolutional neural network,the sample data set is formed through the direct extraction of the fault feature signal.The fault diagnosis model is established by convolution neural network and softmax multi-classifier,and the ac-curacy of the algorithm optimization is verified in Python,which proves that the algorithm can improve the accuracy of motor fault di-agnosis.关键词
卷积神经网络/softmax多分类器/故障诊断/短时傅里叶变换Key words
convolutional neural network/softmax multi-classifier/fault diagnosis/short time Fourier transform分类
机械制造引用本文复制引用
王晓兰,马泽娟,王惠中..基于深度学习的电机故障诊断[J].计算机与数字工程,2024,52(5):1536-1540,5.基金项目
国家自然科学基金项目(编号:61963024)资助. （编号:61963024）
