Bearing Fault Diagnosis based on Deep Belief Networks and Particle Swarm Optimization Support Vector Machine
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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。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.
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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 
xi是信号x的第i个值, N是数据总数 波形因子 
峰峰值 Pk=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 Xr Xr=mean{sqrt[abs(X)]}2 裕度因子 L=PK/xr St 
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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
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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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