基于深度卷积神经网络与WPT-PWVD的轴承故障智能诊断

黄鑫1,陈仁祥1,2,杨星1,张霞1,黄钰3,余腾伟1

振动与冲击 ›› 2020, Vol. 39 ›› Issue (16) : 236-243.

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (16) : 236-243.
论文

基于深度卷积神经网络与WPT-PWVD的轴承故障智能诊断

  • 黄鑫1,陈仁祥1,2,杨星1,张霞1,黄钰3,余腾伟1
作者信息 +

A bearing fault intelligent diagnosis method based on deep convolution neural network and WPT-PWVD

  • HUANG Xin1, CHEN Renxiang1,2, YANG Xing1, ZHANG Xia1, HUANG Yu3, YU Tengwei1
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摘要

针对轴承故障诊断中人工提取特征依赖经验,且泛化性和自适应能力弱等问题,提出一种基于深度卷积神经网络(DCNN)与WPT-PWVD的智能故障诊断新方法。①利用小波包变换(WPT)将轴承故障信号进行自适应分解以提取有效高频成分并进行重构;②利用希尔伯特算法对重构信号做包络解调并进行伪魏格纳分布(PWVD)以得到能揭示轴承主要故障信息的时频图;③构建DCNN网络对轴承故障时频图自动学习提取故障特征,并通过在DCNN特征输出层后添加的Softmax多分类器进行网络参数微调,将特征自动学习提取与故障分类融为一体,实现轴承故障智能诊断。使用所提方法对不同工况、不同故障程度及不同故障类型的轴承进行诊断,结果证明了所提方法诊断精度高,且泛化能力强。

Abstract

Considering problems of low efficiency, complex process, bad generalization and weak self-adaption of artificial feature extraction in bearing fault diagnosis, a new intelligent fault diagnosis method based on deep convolution neural network (DCNN) and the wavelet package transform (WPT) was proposed.Firstly, the bearing fault signal is self-adaptively decomposed into several frequency bands by WPT and effective high-frequency components are extracted and reconstructed.Secondly, the Hilbert algorithm is used to perform envelope demodulation on the reconstructed signal and the PWVD algorithm is performed on the demodulated signal to obtain time-frequency distribution maps which can reveal main fault information.Finally, DCNN is constructed to automatically extract features from the time-frequency distribution maps, then the Softmax multi-classifier is added in feature output layer to perform fine-tune parameters so that feature learning and fault diagnosis is fused.The method is used to diagnose bearing faults under different working conditions, different degrees and different faults.The results prove that the proposed method has high diagnostic accuracy as well as strong generalization.

关键词

深度卷积神经网络(DCNN) / 小波包变换(WPT) / 伪魏格纳分布(PWVD) / 时频图 / 故障智能诊断

Key words

deep convolution neural network(DCNN) / wavelet package transform(WPT) / pseudo Wigner-Ville distribution(PWVD) / time-frequency maps / fault intelligent diagnosis

引用本文

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黄鑫1,陈仁祥1,2,杨星1,张霞1,黄钰3,余腾伟1. 基于深度卷积神经网络与WPT-PWVD的轴承故障智能诊断[J]. 振动与冲击, 2020, 39(16): 236-243
HUANG Xin1, CHEN Renxiang1,2, YANG Xing1, ZHANG Xia1, HUANG Yu3, YU Tengwei1. A bearing fault intelligent diagnosis method based on deep convolution neural network and WPT-PWVD[J]. Journal of Vibration and Shock, 2020, 39(16): 236-243

参考文献

[1] 王红军, 万鹏. 基于EEMD和小波包变换的早期故障敏感特征获取[J]. 北京理工大学学报, 2013, 33(9):945-950.
Wang Hongjun, Wan Peng.Earlier fault sensitive feature acquisition based on EEMD and wavelet packet transform[J].Journal of Beijing University of Technology, 2013, 33 (9): 945-950.
[2]Wang L, Liu Z, Miao Q, et al. Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis[J]. Mechanical Systems & Signal Processing, 2018, 103:60-75.
[3]Wang T, Liang M, Li J, et al. Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis[J]. Mechanical Systems & Signal Processing, 2014, 45(1):139-153.
[4]雷亚国, 贾峰, 孔德同,等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5):94-104.
Leia Guo, Jia Feng, Kong Detong, et al. Opportunities and challenges of mechanical intelligent fault diagnosis under large data [J].Journal of Mechanical Engineering, 2018, 54 (5): 94-104.
[5]Lu C, Wang Z Y, Qin W L, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J]. Signal Processing, 2017, 130(C):377-388.
[6]李巍华,单外平,曾雪琼. 基于深度信念网络的轴承故障分类识别[J]. 振动工程学报, 2016, 29(2):340-347.
Li Weihua, Shan Weiping, Zeng Xueqiong.Bearing fault classification and identification based on depth belief network[J].Journal of Vibration Engineering, 2016, 29 (2): 340-347.
[7]雷亚国, 贾峰, 周昕,等. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报, 2015, 51(21):49-56.
Rei Yaguo, Jia Feng, Zhou Xin, et al. Large data health monitoring method for mechanical equipment based on depth learning theory [J].Journal of Mechanical Engineering, 2015, 51 (21): 49-56.
[8]周飞燕, 金林鹏, 董军.卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6):1229-1251.
Zhou Feiyan, Jin Linpeng, Dong Jun. A review of convolutional neural networks[J].Journal of Computer Science, 2017, 40 (6): 1229-1251.
[9] Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1):1929-1958.
[10] Behley J, Steinhage V, Cremers A B. Laser-based segment classification using a mixture of bag-of-words[C]// Ieee/rsj International Conference on Intelligent Robots and Systems. IEEE, 2013:4195-4200.
[11] Han J, Zhang D, Wen S, et al. Two-Stage Learning to Predict Human Eye Fixations via SDAEs[J]. IEEE Trans Cybern, 2016, 2(46):487-498.
[12]曹建军, 张培林, 任国全,等. 提升小波包最优基分解算法及在振动信号降噪中的应用[J]. 振动与冲击, 2008, 27(8):114-116.
Cao Jianjun, Zhang Peilin, Ren Guoquan, et al. Lifting wavelet package decomposing algorithm under the best bases and its applications in de-noising for vibration signal [J].Vibration and Impact, 2008, 27 (8): 114-116.
[13] 武和雷, 朱善安, 林瑞仲,等. 基于能量算子解调法的滚动轴承故障诊断[J]. 农业机械学报, 2003, 34(1):118-120.
Wu He Lei,Zhu Shan'an,Lin Ruizhong,et al. Rolling bearing fault diagnosis based on energy operator demodulation[J].Journal of Agricultural Machinery,2003,34(1): 118-120.
[14]牟伟杰, 石林锁, 蔡艳平,等. 基于 KVMD-PWVD 与 LNMF 的内燃机振动谱图像识别诊断方法[J].振动与冲击, 2017, 36(2):45-51.
Mou Weijie, Shi Linsuo, Cai Yanping, et al. Image recognition and diagnosis method for vibration spectrum of internal combustion engine based on KVMD-PWVD and LNMF [J]. Vibration and impact, 2017, 36 (2): 45-51.
[15]康海英, 栾军英, 郑海起,等. 基于小波包变换和伪魏格纳分布的轴承故障诊断[J]. 军械工程学院学报, 2004, 16(5):5-8.
Kang Haiying,Luan Junying,Zheng Haiqi,et al.Bearing fault diagnosis based on wavelet packet transform and pseudo-Wigner distribution[J].Journal of Ordnance Engineering College,2004,16(5): 5-8.
[16]Sokolova M, Japkowicz N, Szpakowicz S. Beyond accuracy, f-score and ROC: a family of discriminant measures for performance evaluation[C]// Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence. Springer-Verlag, 2006:1015-1021.
[17]Jin X, Zhao M, Chow T W S, et al. Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis[J]. IEEE Transactions on Industrial Electronics, 2013, 61(5):2441-2451.
[18]I.A. AbuMahfouz. "A comparative study of three artificial neural networks for the detection and classification of gear faults." International Journal of General Systems 34.3(2005):261-277.

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