基于小波包能量熵和DBN的轴承故障诊断
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摘要
轴承是旋转机械设备的关键部件,目前已有很多轴承故障诊断方法,但其中一些方法只能针对特定的轴承故障进行诊断,可能不适用于其他轴承故障问题,而且大部分方法的诊断准确率还可以进一步提高。提出小波包能量熵与深度置信网络(DBN)相结合的方法进行轴承故障诊断。首先对轴承振动信号进行小波包变换,然后以能量熵的形式构建特征向量,这些特征向量含有不同频段内的振动能量大小,可以用于区分各种轴承故障。最后利用基于DBN的深度模型对能量熵特征向量进行故障识别。使用两类轴承数据集进行验证,分别获得100%和99.5%的故障识别准确率。实验结果表明,该诊断方法具有较好的通用性,而且可以达到很高的诊断准确率。
Bearings are critical components of rotary machinery equipment. Numerous studies have been conducted on bearing fault diagnosis.Some of these methods can only be used for diagnosis of a certain type of bearing failure and cannot detect other failures. The diagnostic accuracy rate for most methods can be further improved. A new method is proposed for bearing fault diagnos is based on wavelet packet energy entropy and deep belief network(DBN).The bearing vibration signal is processed using wavelet packet transform to get the energy entropy feature vector. The feature vector represents the vibration energy in different frequency bands, which can be used to distinguish the fault type. The deep model based on DBN is adopted to recognize fault types.The proposed method achieves 100% and 99.5% fault recognition accuracy on two bearing datasets, respectively.The experimental results show that the proposed method has good versatility and can achieve high diagnostic accuracy.
Bearings are critical components of rotary machinery equipment. Numerous studies have been conducted on bearing fault diagnosis.Some of these methods can only be used for diagnosis of a certain type of bearing failure and cannot detect other failures. The diagnostic accuracy rate for most methods can be further improved. A new method is proposed for bearing fault diagnos is based on wavelet packet energy entropy and deep belief network(DBN).The bearing vibration signal is processed using wavelet packet transform to get the energy entropy feature vector. The feature vector represents the vibration energy in different frequency bands, which can be used to distinguish the fault type. The deep model based on DBN is adopted to recognize fault types.The proposed method achieves 100% and 99.5% fault recognition accuracy on two bearing datasets, respectively.The experimental results show that the proposed method has good versatility and can achieve high diagnostic accuracy.
引文
[1] 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 CH L, JI J J. Approach for bearing fault diagnosis based on KPCA and ELM[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(2):23-29
[2] 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 J T, YAN CH H, SUN J D, et al. Bearing fault diagnosis method based on compressed acquisitionand deep learning[J]. Chinese Journal of Scientific Instrument, 2018, 39(1):171-179.
[3] 陈勇旗, 赵一鸣, 陈杨. 基于固有时间尺度分解的滚动轴承故障诊断[J]. 电子测量与仪器学报, 2015, 29(11):1677-1682.CHEN Y Q, ZHAO Y M, CHEN Y. Fault diagnosis of roller bearing based on intrinsic time-scale decomposition[J]. Journal of Electronic Measurement and Instrumentation, 2015,29(11):1677-1682.
[4] 张雪英, 栾忠权, 刘秀丽. 基于深度学习的滚动轴承故障诊断研究综述[J]. 设备管理与维修, 2017(18):130-133.ZHANG X Y, LUAN ZH Q, LIU X L. Summary of research on fault diagnosis of rolling bearing based on deep learning[J]. Plant Maintenance Engineering, 2017(18):130-133.
[5] 张亚楠, 魏武, 武林林. 基于小波包Shannon熵SVM和遗传算法的电机机械故障诊断[J]. 电力自动化设备, 2010, 30(1):87-91.ZHANG Y N, WEI W, WU L L. Motor mechanical fault diagnosis based on wavelet packet shannon entropy SVM and genetic algorithm[J]. Electric Power Automation Equipment, 2010, 30(1):87-91.
[6] TAMILSELVAN P, WANG P. Failure diagnosis using deep belief learning based health state classification[J]. Reliability Engineering & System Safety, 2013, 115(7):124-135.
[7] 任浩, 屈剑锋, 柴毅,等. 深度学习在故障诊断领域中的研究现状与挑战[J]. 控制与决策, 2017, 32(8):1345-1358.REN H, QU J F, CHAI Y, et al. Deep learning for fault diagnosis: The state of the art and challenge[J]. Control and Decision, 2017, 32(8):1345-1358.
[8] 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 W H, SHAN W P, ZENG X Q. Bearing fault classification and recognition based on deep belief network[J]. Journal of Vibration Engineering, 2016, 29(2):340-347.
[9] JIA F, LEI Y, LIN J, et al. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J]. Mechanical Systems & Signal Processing, 2016(72-73):303-315.
[10] LIU H, LI L, MA J. Rolling bearing fault diagnosis based on STFT-deep learning and sound signals[J]. Shock and Vibration, 2016, 2016(2):12.
[11] 袁建虎,韩涛,唐建,等.基于小波时频图和CNN的滚动轴承智能故障诊断方法[J].机械设计与研究,2017,33(2):93-97.YUAN J H, HAN T, TANG J, et al.An approach to intelligent fault diagnosis of rolling bearingusing wavelet time-frequency representations and CNN[J]. Machine Design and Research, 2017, 33(2):93-97.
[12] 汤宝平, 习建民, 李锋. 基于Elman神经网络的旋转机械故障诊断[J]. 计算机集成制造系统, 2010, 16(10):2148-2152.TANG B P, XI J M, LI F. Fault diagnosis for rotating machinery based on Elamn neural network[J]. Computer Integrated Manufacturing Systems, 2010, 16(10):2148-2152.
[13] ZHANG B, GEORGOULAS G, ORCHARD M, et al. Rolling element bearing feature extraction and anomaly detection based on vibration monitoring[C]. Mediterranean Conference on Control and Automation, IEEE, 2008:1792-1797.
[14] 周小勇, 叶银忠. 故障信号检测的小波基选择方法[J]. 控制工程, 2003, 10(4):308-311.ZHOU X Y, YE Y ZH. Wavelet basis selection method for fault signal detection[J]. Control Engineering of China, 2003, 10(4):308-311.
[15] 王婉秋, 杨松. 结构故障诊断中的小波分解层数选取方法研究[J]. 强度与环境, 2009, 36(4):34-41.WANG W Q, YANG S. A method for choosing the wavelet decomposition level instructural fault analysis[J]. Structure & Environment Engineering, 2009, 36(4):34-41.
[16] 罗红梅, 齐明侠, 裴峻峰,等. 滚动轴承故障诊断中精确转频的实用计算新方法[J]. 振动与冲击, 2007, 26(5):64-66.LUO H M, QI M X, PEI J F, et al.A new practical method for calculating precise frequency conversion in fault diagnosis of rolling bearings[J]. Journal of Vibration and Shock, 2007, 26(5):64-66.
[17] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313(5786):504-507.
[18] SHAO H D, JIANG H K, ZHANG X, et al. Rolling bearing fault diagnosis using an optimization deep belief network[J]. Measurement Science and Technology, 2015, 26 (11):1-17.
[19] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7):1527-1554.
[20] NG A. Sparse autoencoder[J]. CS294A Lecture Notes, 2011, 72(2011): 1-19.
[21] LOU X, LOPARO K A. Bearing fault diagnosis based on wavelet transform and fuzzy inference[J]. Mechanical Systems and Signal Processing, 2004, 18(5):1077-1095.
[22] ALMEIDA L F D, BIZARRIA J W, BIZARRIA F C, et al. Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron[J]. Journal of Vibration & Control, 2015, 21(16): 3456-3464.
[23] VAN M, KANG H. Bearing defect classification based on individual wavelet local fisher discriminant analysis with particle swarm optimization[J]. IEEE Transactions on Industrial Informatics, 2016, 12(1): 124-135.
[2] 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 J T, YAN CH H, SUN J D, et al. Bearing fault diagnosis method based on compressed acquisitionand deep learning[J]. Chinese Journal of Scientific Instrument, 2018, 39(1):171-179.
[3] 陈勇旗, 赵一鸣, 陈杨. 基于固有时间尺度分解的滚动轴承故障诊断[J]. 电子测量与仪器学报, 2015, 29(11):1677-1682.CHEN Y Q, ZHAO Y M, CHEN Y. Fault diagnosis of roller bearing based on intrinsic time-scale decomposition[J]. Journal of Electronic Measurement and Instrumentation, 2015,29(11):1677-1682.
[4] 张雪英, 栾忠权, 刘秀丽. 基于深度学习的滚动轴承故障诊断研究综述[J]. 设备管理与维修, 2017(18):130-133.ZHANG X Y, LUAN ZH Q, LIU X L. Summary of research on fault diagnosis of rolling bearing based on deep learning[J]. Plant Maintenance Engineering, 2017(18):130-133.
[5] 张亚楠, 魏武, 武林林. 基于小波包Shannon熵SVM和遗传算法的电机机械故障诊断[J]. 电力自动化设备, 2010, 30(1):87-91.ZHANG Y N, WEI W, WU L L. Motor mechanical fault diagnosis based on wavelet packet shannon entropy SVM and genetic algorithm[J]. Electric Power Automation Equipment, 2010, 30(1):87-91.
[6] TAMILSELVAN P, WANG P. Failure diagnosis using deep belief learning based health state classification[J]. Reliability Engineering & System Safety, 2013, 115(7):124-135.
[7] 任浩, 屈剑锋, 柴毅,等. 深度学习在故障诊断领域中的研究现状与挑战[J]. 控制与决策, 2017, 32(8):1345-1358.REN H, QU J F, CHAI Y, et al. Deep learning for fault diagnosis: The state of the art and challenge[J]. Control and Decision, 2017, 32(8):1345-1358.
[8] 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 W H, SHAN W P, ZENG X Q. Bearing fault classification and recognition based on deep belief network[J]. Journal of Vibration Engineering, 2016, 29(2):340-347.
[9] JIA F, LEI Y, LIN J, et al. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J]. Mechanical Systems & Signal Processing, 2016(72-73):303-315.
[10] LIU H, LI L, MA J. Rolling bearing fault diagnosis based on STFT-deep learning and sound signals[J]. Shock and Vibration, 2016, 2016(2):12.
[11] 袁建虎,韩涛,唐建,等.基于小波时频图和CNN的滚动轴承智能故障诊断方法[J].机械设计与研究,2017,33(2):93-97.YUAN J H, HAN T, TANG J, et al.An approach to intelligent fault diagnosis of rolling bearingusing wavelet time-frequency representations and CNN[J]. Machine Design and Research, 2017, 33(2):93-97.
[12] 汤宝平, 习建民, 李锋. 基于Elman神经网络的旋转机械故障诊断[J]. 计算机集成制造系统, 2010, 16(10):2148-2152.TANG B P, XI J M, LI F. Fault diagnosis for rotating machinery based on Elamn neural network[J]. Computer Integrated Manufacturing Systems, 2010, 16(10):2148-2152.
[13] ZHANG B, GEORGOULAS G, ORCHARD M, et al. Rolling element bearing feature extraction and anomaly detection based on vibration monitoring[C]. Mediterranean Conference on Control and Automation, IEEE, 2008:1792-1797.
[14] 周小勇, 叶银忠. 故障信号检测的小波基选择方法[J]. 控制工程, 2003, 10(4):308-311.ZHOU X Y, YE Y ZH. Wavelet basis selection method for fault signal detection[J]. Control Engineering of China, 2003, 10(4):308-311.
[15] 王婉秋, 杨松. 结构故障诊断中的小波分解层数选取方法研究[J]. 强度与环境, 2009, 36(4):34-41.WANG W Q, YANG S. A method for choosing the wavelet decomposition level instructural fault analysis[J]. Structure & Environment Engineering, 2009, 36(4):34-41.
[16] 罗红梅, 齐明侠, 裴峻峰,等. 滚动轴承故障诊断中精确转频的实用计算新方法[J]. 振动与冲击, 2007, 26(5):64-66.LUO H M, QI M X, PEI J F, et al.A new practical method for calculating precise frequency conversion in fault diagnosis of rolling bearings[J]. Journal of Vibration and Shock, 2007, 26(5):64-66.
[17] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313(5786):504-507.
[18] SHAO H D, JIANG H K, ZHANG X, et al. Rolling bearing fault diagnosis using an optimization deep belief network[J]. Measurement Science and Technology, 2015, 26 (11):1-17.
[19] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7):1527-1554.
[20] NG A. Sparse autoencoder[J]. CS294A Lecture Notes, 2011, 72(2011): 1-19.
[21] LOU X, LOPARO K A. Bearing fault diagnosis based on wavelet transform and fuzzy inference[J]. Mechanical Systems and Signal Processing, 2004, 18(5):1077-1095.
[22] ALMEIDA L F D, BIZARRIA J W, BIZARRIA F C, et al. Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron[J]. Journal of Vibration & Control, 2015, 21(16): 3456-3464.
[23] VAN M, KANG H. Bearing defect classification based on individual wavelet local fisher discriminant analysis with particle swarm optimization[J]. IEEE Transactions on Industrial Informatics, 2016, 12(1): 124-135.
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