基于多通道数据融合及CNN的电机故障诊断方法
On the Motor Fault Diagnosis Method Based on Multi-channel Data Fusion and CNN
潘鹏程 1向阳 1陈天佑 1姜苗1
作者信息
- 1. 武汉理工大学 船海与能源动力工程学院,武汉 430063;高性能船舶技术教育部重点实验室(武汉理工大学),武汉 430063
- 折叠
摘要
为解决因电机结构复杂、信号非平稳等因素导致电机故障诊断困难问题及传统故障诊断算法对专家经验的依赖,提出一种基于多通道数据融合与卷积神经网络(CNN)相结合的电机故障诊断方法.该方法首先采集电机驱动端的振动信号和定子电流信号并对其进行时频域转换,再将两者频域信号进行归一化处理并转变为二维图谱数据,最后构建CNN网络模型,确定网络层数、学习率等超参数,并将样本输入模型进行故障特征提取和分类诊断.结果表明,在合适的参数下采用该方法的电机故障诊断准确率为 100%,对比单独采用振动信号或电流信号的传统故障诊断方法和1D-CNN模型,该方法能够更有效地对电机各类故障进行诊断.
Abstract
In order to solve the problem of difficult motor fault diagnosis caused by complex motor structures and non-station-ary signals,as well as the dependence of traditional fault diagnosis algorithms on expert experience,a fault diagnosis method for motors based on multi-channel data fusion and convolutional neural networks(CNN)was proposed.The method first collects vi-bration signals and stator current signals at the motor drive end and converts them into frequency domain signals,then normalizes the frequency domain signals of the two signals and converts them into two-dimensional spectrum data.Finally,a CNN network model is constructed,the hyperparameter such as network layers and learning rate are determined,and samples are input into the model for fault feature extraction and classification diagnosis.The results showed that the accuracy of motor fault diagnosis using this method under appropriate parameters is100%.Compared with traditional fault diagnosis methods and the1D-CNN model u-sing vibration or current signals alone,this method can more effectively diagnose various motor faults.
关键词
推进电机/数据融合/卷积神经网络/故障诊断Key words
propulsion motor/data fusion/convolutional neural network/fault diagnosis引用本文复制引用
基金项目
工信部绿色智能内河船舶创新专项(20201g0047)
出版年
2024
NETL
NSTL
万方数据