[1]王骁贤,张保华,陆思良.基于连续小波变换和卷积神经网络的无刷直流电机故障诊断[J].机械与电子,2018,(06):29-32.
　WANG Xiaoxian,ZHANG Baohua,LU Siliang.Fault Diagnosis of Brushless Direct Current Motor Based on Continuous Wavelet Transform and Convolutional Neural Network[J].Machinery & Electronics,2018,(06):29-32.
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基于连续小波变换和卷积神经网络的无刷直流电机故障诊断()
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机械与电子[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2018年06期
页码:
29-32
栏目:
机电一体化技术
出版日期:
2018-06-24

文章信息/Info

Title:
Fault Diagnosis of Brushless Direct Current Motor Based on Continuous Wavelet Transform and Convolutional Neural Network
文章编号:
1001-2257(2018)06-0029-04
作者:
王骁贤1; 张保华1; 陆思良2
(1.安徽大学电子信息工程学院,安徽 合肥 230601;2.安徽大学高节能电机及控制技术国家地方联合工程实验室,安徽 合肥 230601)
Author(s):
WANG Xiaoxian1;  ZHANG Baohua1;  LU Siliang2
(1. School of Electronics and Information Engineering, Anhui University, Hefei 230601, China; 2. National Engineering Laboratory of Energy-Saving Motor and Control Technology, Anhui University, Hefei 230601, China)
关键词:
无刷直流电机; 电机故障诊断; 连续小波变换; 卷积神经网络; 深度学习
Keywords:
brushless direct current motor;  motor fault diagnosis;  continuous wavelet transform;  convolutional neural network;  deep learning?
分类号:
TH165.3
文献标志码:
A
摘要:
提出一种能够精确诊断无刷直流电机不同种类轴承故障、转子不平衡、霍尔元件故障和定子绕组三相不平衡故障的方法。将数据采集系统采集到的一维的机械振动信号进行连续小波变换，即可将一维的时域信号转变为二维的时频图像。对不同故障的时频图像利用基于卷积神经网络的深度学习算法进行训练，得到无刷直流电机故障网络模型。利用训练好的模型对验证数据进行推理，即可实现电机故障检测和分类。实验表明，电机8种健康/故障模式的分类精度接近100%。
Abstract:
This study proposes a method that can accurately detect the faults of brushless direct current motor (BLDCM), including bearing fault, rotor imbalance, Hall sensor fault, imbalance of stator winding resistance.?Firstly, the one-dimensional vibration signal acquired from the data acquisition system was transferred to the two-dimensional time-frequency spectrogram by using the continuous wavelet transform.?Then, the spectrograms corresponding to different motor faults were trained by applying the deep learning technique based on the convolutional neural network (CNN). Hence a well-trained model for BLDCM fault diagnosis was established.??Finally, motor faults could be detected and classified by applying the simulated model to analyze the testing data. The experimental results show that the classification accuracy?is close to 100% when the proposed method is used to motor faults diagnosis.

参考文献/References:

[1]Lu S L,Wang X X.A new methodology to estimate the rotating phase of a BLDC motor with its application in variable-speed bearing fault diagnosis[J].IEEE Transactions on Power Electronics,2018,33(4): 3399-3410.
[2]余文航,孔凡让,张海滨,等.利用无线模块的列车轴承在线监测系统的设计[J].机械与电子,2014(10):55-58.
[3]袁涛,张尚斌,何清波.时变奇异值分解在轴承故障特征提取中的应用研究[J].机械与电子,2017,35(6):8-11.
[4]Liu Y B,He B,Liu F,et al.Feature fusion using kernel joint approximate diagonalization of eigen-matrices for rolling bearing fault identification[J].Journal of Sound and Vibration,2016,385:389-401.
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　ZHU Junchi,LI Wenchao,LU Ying.Research on the Improved Fuzzy Control of Brushless DC Motor for Electric Vehicles[J].Machinery & Electronics,2017,(06):51.
[2]周荣亚.新型无刷直流电机逆变电路功耗分析[J].机械与电子,2016,(11):48.
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备注/Memo

备注/Memo:
收稿日期：2018-03-06
 基金项目：国家自然科学基金资助项目(51605002)
作者简介：王骁贤(1990-)，女，安徽安庆人，硕士，工程师，研究方向为机电设计与信号处理；张保华(1962-)，女，安徽亳州人，博士，副教授，硕士生导师，研究方向为光机电算一体化；陆思良(1987-)，男，广西钦州人，博士，副教授，硕士生导师，研究方向为机电状态监测诊断，工业工厂自动化。
更新日期/Last Update: 2019-10-30