基于小波包神经网络的轴承故障识别模型
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摘要
针对传统故障特征提取过程复杂、诊断方案单一且准确性差等问题,提出了基于多阈值小波包和深度置信网络(DBN)的轴承故障识别方案。本文作者采用最优小波基函数和软硬阈值结合方法对原始振动信号进行三层分解降噪处理,得到8个从低频到高频段的信号成分,对其进行组合重构作为神经网络的输入样本;通过DBN在数据处理上的特征重构优势,建立了DBNBP神经网络的轴承故障识别模型,确定模型的各类参数。经多次实验,探究不同样本输入对模型识别率的影响,并与传统的浅层神经网络识别模型做对比分析,结果表明:经训练的DBNBP轴承故障识别模型可从原始数据、小波包分解信号实现轴承故障信号的准确特征学习和分类,结合识别率和诊断时间考虑,经小波包分解信号输入具有更优的诊断效率。
Aiming at the problems of complex process in traditional fault feature extraction, single diagnosis and poor accuracy, a bearing fault identification scheme based on multi-threshold wavelet packet and deep belief network(DBN) was proposed. The optimal wavelet basis function and soft-hard threshold combination method were used to perform three-layer decomposition and noise reduction on the original vibration signal, and eight signal components from low frequency to high frequency band were obtained. The combined reconstruction of 8 frequency bands was used as the input sample of the neural network. Based on the advantages of DBN in data processing, the bearing fault identification model of DBNBP neural network was established and various parameters of the model were determined. The effects of different sample inputs on the identification rate of the model was explored and compared with the traditional neural network model. The results show that the trained DBNBP bearing fault identification model can accurately predict the bearing fault signal from the original data and wavelet packet decomposition signals. Feature learning and classification, combined with recognition rate and diagnostic time considerations, have better diagnostic efficiency through wavelet packet decomposition signal input.
Aiming at the problems of complex process in traditional fault feature extraction, single diagnosis and poor accuracy, a bearing fault identification scheme based on multi-threshold wavelet packet and deep belief network(DBN) was proposed. The optimal wavelet basis function and soft-hard threshold combination method were used to perform three-layer decomposition and noise reduction on the original vibration signal, and eight signal components from low frequency to high frequency band were obtained. The combined reconstruction of 8 frequency bands was used as the input sample of the neural network. Based on the advantages of DBN in data processing, the bearing fault identification model of DBNBP neural network was established and various parameters of the model were determined. The effects of different sample inputs on the identification rate of the model was explored and compared with the traditional neural network model. The results show that the trained DBNBP bearing fault identification model can accurately predict the bearing fault signal from the original data and wavelet packet decomposition signals. Feature learning and classification, combined with recognition rate and diagnostic time considerations, have better diagnostic efficiency through wavelet packet decomposition signal input.
引文
[1] 李舜酩,郭海东,李殿荣.振动信号处理方法综述[J].仪器仪表学报,2013,34(8):1907-1915.LI S M,GUO H D,LI D R.Review of Vibration Signal Processing Methods[J].Chinese Journal of Scientific Instrument,2013,34(8):1907-1915.
[2] 陈喆,彭钰林,王舒文,等.从离散到连续:分数阶信号处理的理论、方法与应用[J].电子学报,2012,40(11):2282-2289.CHEN Z,PENG Y L,WANG S W,et al.From Discrete to Continuous:Fractional Signal Processing Theories,Methods and Applications[J].Acta Electronica Sinica,2012,40(11):2282-2289.
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[8] 姚亚夫,张星.基于瞬时能量熵和SVM的滚动轴承故障诊断[J].电子测量与仪器学报,2013,27(10):957-962.YAO Y F,ZHANG X.Fault Diagnosis Approach for Roller Bearing Based on EMD Momentary Energy Entropy and SVM[J].Journal of Electronic Measurement and Instrument,2013,27(10):957-962.
[9] 张兰芳,张朝龙,纪娟娟.基于核主元分析和极端学习机的轴承故障诊断方法[J].电子测量与仪器学报,2018,32(2):23-29.ZHANG L F,ZHANG C L,JI J J.Approach for Bearing Fault Diagnosis Based on KPCA and ELM[J].Journal of Electronic Measurement and Instrument,2018,32(2):23-29.
[10] 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.LEI Y G,JIA F,KONG D T,et al.Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era[J].Journal of Mechanical Engineering,2018,54(5):94-104.
[11] SHAO H,JIANG H,WANG F,et al.Rolling Bearing Fault Diagnosis Using Adaptive Deep Belief Network with Dual-tree Complex Wavelet Packet[J].Isa Transactions,2017:187-201.
[12] 王丽华,谢阳阳,张永宏,等.采用深度学习的异步电机故障诊断方法[J].西安交通大学学报,2017,51(10):128-134.WANG L H,XIE Y Y,ZHANG Y H,et al.A Fault Diagnosis Method for Asynchronous Motor Using Deep Learning[J].Journal of Xi’an Jiaotong University,2017,51(10):128-134.
[13] 赵光权,葛强强,刘小勇,等.基于DBN的故障特征提取及诊断方法研究[J].仪器仪表学报,2016,37(9):1946-1953.ZHAO G Q,GE Q Q,LIU X Y,et al.Fault Feature Extraction and Diagnosis Method Based on Deep Belief Network[J].Chinese Journal of Scientific Instrument,2016,37(9):1946-1953.
[14] 单外平,曾雪琼.基于深度信念网络的信号重构与轴承故障识别[J].电子设计工程,2016,24(4):67-71.SHAN W P,ZENG X Q.Signal Reconstruction and Bearing Fault Identification Based on Deep Belief Network[J].Electronic Design Engineering,2016,24(4):67-71.
[2] 陈喆,彭钰林,王舒文,等.从离散到连续:分数阶信号处理的理论、方法与应用[J].电子学报,2012,40(11):2282-2289.CHEN Z,PENG Y L,WANG S W,et al.From Discrete to Continuous:Fractional Signal Processing Theories,Methods and Applications[J].Acta Electronica Sinica,2012,40(11):2282-2289.
[3] 胡智勇,胡杰鑫,谢里阳,等.滚动轴承振动信号处理方法综述[J].中国工程机械学报,2016,14(6):525-531.HU Z Y,HU J X,XIE L Y,et al.Review on Signal Processing for Rolling Bearing Vibrations[J].Chinese Journal of Construction Machinery,2016,14(6):525-531.
[4] KANKAR P K,SHARMA S C,HARSHA S P.Fault Diagnosis of Ball Bearings Using Continuous Wavelet Transform[J].Applied Soft Computing,2011,11(2):2300-2312.
[5] LI W,HUANG Z,LIN H,et al.Envelope Analysis by Wavelet-filter Based Spectral Kurtosis for Bearing Health Monitoring[C]//Proceedings of Instrumentation and Measurement Technology Conference(I2MTC),2013 IEEE International.IEEE,2013:1721-1724.
[6] LEI Y,LIN J,HE Z,et al.A Review on Empirical Mode Decomposition in Fault Diagnosis of Rotating Machinery[J].Mechanical Systems and Signal Processing,2013,35(1/2):108-126.
[7] VAN M,KANG H J,SHIN K S.Rolling Element Bearing Fault Diagnosis Based on Non-local Means De-noising and Empirical Mode Decomposition[J].Iet Science Measurement Technology,2014,8(6):571-578.
[8] 姚亚夫,张星.基于瞬时能量熵和SVM的滚动轴承故障诊断[J].电子测量与仪器学报,2013,27(10):957-962.YAO Y F,ZHANG X.Fault Diagnosis Approach for Roller Bearing Based on EMD Momentary Energy Entropy and SVM[J].Journal of Electronic Measurement and Instrument,2013,27(10):957-962.
[9] 张兰芳,张朝龙,纪娟娟.基于核主元分析和极端学习机的轴承故障诊断方法[J].电子测量与仪器学报,2018,32(2):23-29.ZHANG L F,ZHANG C L,JI J J.Approach for Bearing Fault Diagnosis Based on KPCA and ELM[J].Journal of Electronic Measurement and Instrument,2018,32(2):23-29.
[10] 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.LEI Y G,JIA F,KONG D T,et al.Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era[J].Journal of Mechanical Engineering,2018,54(5):94-104.
[11] SHAO H,JIANG H,WANG F,et al.Rolling Bearing Fault Diagnosis Using Adaptive Deep Belief Network with Dual-tree Complex Wavelet Packet[J].Isa Transactions,2017:187-201.
[12] 王丽华,谢阳阳,张永宏,等.采用深度学习的异步电机故障诊断方法[J].西安交通大学学报,2017,51(10):128-134.WANG L H,XIE Y Y,ZHANG Y H,et al.A Fault Diagnosis Method for Asynchronous Motor Using Deep Learning[J].Journal of Xi’an Jiaotong University,2017,51(10):128-134.
[13] 赵光权,葛强强,刘小勇,等.基于DBN的故障特征提取及诊断方法研究[J].仪器仪表学报,2016,37(9):1946-1953.ZHAO G Q,GE Q Q,LIU X Y,et al.Fault Feature Extraction and Diagnosis Method Based on Deep Belief Network[J].Chinese Journal of Scientific Instrument,2016,37(9):1946-1953.
[14] 单外平,曾雪琼.基于深度信念网络的信号重构与轴承故障识别[J].电子设计工程,2016,24(4):67-71.SHAN W P,ZENG X Q.Signal Reconstruction and Bearing Fault Identification Based on Deep Belief Network[J].Electronic Design Engineering,2016,24(4):67-71.