基于深度学习特征提取和WOA-SVM状态识别的轴承故障诊断
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摘要
针对滚动轴承故障诊断问题,利用深度学习神经网络、鲸鱼优化算法(WOA)和支持向量机(SVM)等技术,提出了一种基于深度学习特征提取和WOA-SVM状态识别相结合的故障诊断模型。先通过深度学习自适应提取故障频谱特征,并将其与数理统计方法提取的时域特征相融合,再通过WOA-SVM对融合后的联合特征进行故障诊断。该模型在对滚动轴承试验台的故障诊断中实现了不同工况下多种故障类型的可靠识别,并且在一定程度上提高了故障分类的准确性。为了验证WOA-SVM在深度学习提取特征的轴承故障识别中的可行性和有效性,对比了粒子群支持向量机和遗传支持向量机,结果表明WOA-SVM具有较高的收敛精度和收敛速度。
For the fault diagnosis of rolling bearings, a fault diagnosis model based on the deep learning feature extraction and WOA-SVM state recognition was proposed. The fault frequency domain feature was extracted by the depth learning adaptive method, and then it was fused with the time domain feature extracted by the mathematical statistics method. The fused joint features were used in diagnosis through the processing of WOA-SVM. By the model, it has realized the reliable identification of various fault types of rolling bearings under different working conditions on a test bench and improves the accuracy of fault classification to a certain extent. In order to verify the feasibility and effectiveness of bearing fault identification based on WOA-SVM, the diagnosis results were compared with those by the PSO-SVM and GA-SVM. The results show that WOA-SVM has higher convergence accuracy and convergence speed.
For the fault diagnosis of rolling bearings, a fault diagnosis model based on the deep learning feature extraction and WOA-SVM state recognition was proposed. The fault frequency domain feature was extracted by the depth learning adaptive method, and then it was fused with the time domain feature extracted by the mathematical statistics method. The fused joint features were used in diagnosis through the processing of WOA-SVM. By the model, it has realized the reliable identification of various fault types of rolling bearings under different working conditions on a test bench and improves the accuracy of fault classification to a certain extent. In order to verify the feasibility and effectiveness of bearing fault identification based on WOA-SVM, the diagnosis results were compared with those by the PSO-SVM and GA-SVM. The results show that WOA-SVM has higher convergence accuracy and convergence speed.
引文
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[ 3 ] 李万清.基于朴素贝叶斯方法和权值分析方法的电机轴承故障诊断[J].机电工程,2012,29(4):390-393.LI Wanqing.Fault diagnosis of motor bearings based on naive Bayes method and weight analysis method[J].Mechanical and Electrical Engineering,2012,29(4):390-393.
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[ 5 ] 周将坤,陆森林.基于EMD平均能量法的滚动轴承故障诊断[J].轻工机械,2010,28(2):36-40.ZHOU Jiangkun,LU Senlin.Fault diagnosis of rolling bearing based on EMD mean energy method[J].Light Industrial Machinery,2010,28(2):36-40.
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[ 7 ] HINTON G E,OSINDERO S,TEN Y W.A fast learning algorithm for deep belief nets[J].Neural Comput,2006,18(7):1527-1542.
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[ 9 ] HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionalitu of data with neurl network[J].Science,2006,313(5786):504-507.
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[11] 朱煜奇,黄双喜,杨天祺,等.基于栈式降噪自编码的故障诊断[J].制造业自动化,2017,39(3):152-156.ZHU Yuqi,HUANG Shuangxi,YANG Tianqi,et al.Fault diagnosis based on stack noise reduction self-coding[J].Manufacturing Automation,2017,39(3):152-156.
[12] 温江涛,闫常弘,孙洁娣,等.基于压缩采集与深度学习的轴承故障诊断方法[J].仪器仪表学报,2018,39(1):171-179.WEN Jiangtao,YAN Changhong,SUN Jiedi,et al.Bearing fault diagnosis method based on compression acquisition and deep learning[J].Journal of Instruments and Instruments,2018,39(1):171-179.
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[16] VINCENT P,LAROCHELLE H,BENGIO Y,et al.Stacked denoising autoencoders:learning useful representations in a deep network with a local denoising criterion[J].Journal of Machine Learning Research,2010,11(6):3371-3408.
[17] BENGIO Y,LAMBLIN P,POPOVICI D,et al.Greedy layer-wise training of deep networks[C]//Advances in Neural Information Processing Systems.Cambridge:MIT Press,2007.
[18] MIRJALILI S,MIRJALILI S M,LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95(5):51-67.
[19] LI Bing,LIU Pengyuan,HU Renxi,et al.Fuzzy lattice classifier and its application to bearing fault diagnosis[J].Applied Soft Computing Journal,2012,12(6):1708-1719.