DBN与PSO-SVM的滚动轴承故障诊断

熊景鸣; 潘林; 朱昇; 孟宗

doi:10.13433/j.cnki.1003-8728.20190040

DBN与PSO-SVM的滚动轴承故障诊断

doi: 10.13433/j.cnki.1003-8728.20190040

熊景鸣^1,,
潘林¹,
朱昇¹,
孟宗²

1.
铜仁职业技术学院工学院, 贵州铜仁 554300
2.
燕山大学电气工程学院, 河北秦皇岛 066004

基金项目:

铜仁市科技计划项目铜市科研[2017]25号

河北省高等学校科学研究计划重点项目 ZD2015049

详细信息

作者简介:
熊景鸣(1993-), 讲师, 硕士, 研究方向为机械动力学分析, xjm_master@stumail.ysu.edu.cn

中图分类号: TH133.33
计量
- 文章访问数: 575
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- PDF下载量: 23
- 被引次数: 0
出版历程
- 收稿日期: 2018-01-05
- 刊出日期: 2019-11-05

Bearing Fault Diagnosis based on Deep Belief Networks and Particle Swarm Optimization Support Vector Machine

1.
School of Engineering, Tongren Polytechnic College, Guizhou Tongren 554300, China
2.
School of Electrical Engineering, Yanshan University, Hebei Qinhuangdao 066004, China

摘要

摘要: 针对如何提高轴承故障诊断的准确率和算法训练的效率问题，提出了一种深度信念网络（DBN）与粒子群优化支持向量机（PSO-SVM）的滚动轴承故障诊断方法。首先，求出信号的时频特征统计量，其次，利用DBN对时频特征统计量进行特征提取，最后，利用PSO-SVM进行分类。实验结果表明：相比于直接用PSO-SVM进行分类，该方法不仅准确率更高，而且算法训练的时间大大缩短了，提高了滚动轴承故障诊断的准确率和效率。
- 特征提取 /
- 深度信念网络 /
- 支持向量机 /
- 故障诊断 /
- /
Abstract: As how to improve the accuracy and algorithm efficiency of roll bearing fault diagnosis, a new method of bearing fault diagnosis based on deep belief network (DBN) and the particle swarm optimization support vector machine (PSO-SVM) with the time-frequency characteristic statistic is proposed. Firstly, time-frequency characteristic statistic of the bearing vibration signal is calculated. And then the DBN is used to extract features of time-frequency feature extraction. Finally, the extracted parameters are input to the PSO-SVM to be classified. The experimental results show that this method not only has higher accuracy, but also greatly shorten the training time, and the accuracy and efficiency of fault diagnosis is improved as a result.
- feature extraction /
- deep belief networks(DBN) /
- particle swarm optimization support vector machines(PSO-SVM) /
- fault diagnosis /
- roll bearing /
- experiment

HTML全文

图 1 DBN模型

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图 2 三维图

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图 3 粒子群适宜度曲线

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表 1 时域特征参量

特征参数	表达式	注意
平均值
有效值		x_i是信号x的第i个值, N是数据总数
波形因子
峰峰值	P_k=max[x]-min[x]
波峰因子
峭度		σ²为方差
峭度因子
脉冲因子	I=PK/(av)	av为绝对值的平均
X_r	X_r=mean{sqrt[abs(X)]}²
裕度因子	L=PK/x_r
S_t

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表 2 频域特征参量

特征参数	表达式	注意
频度重心
RMS变异频率
Root变异频率

下载: 导出CSV

表 3 轴承数据集

故障类型	训练/测试样本	数据集	类别编号
正常	100/100	97DE	000
内圈故障(0.177 8)	100/100	105DE	001
内圈故障(0.355 6)	100/100	169DE	010
内圈故障(0.533 4)	100/100	211DE	011
外圈故障(0.177 8)	100/100	130DE	100
外圈故障(0.355 6)	100/100	198DE	101
外圈故障(0.533 4)	100/100	236DE	110

下载: 导出CSV

表 4 分类器性能对照表

方法	运行时间/s	(c, g)	准确率/%
PSO-SVM	27.36	(7.093 1, 5.239 0)	84.85
DBN-PSO-SVM	6.62	(158.953 5, 31.818 3)	97.34

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