一种基于深度学习理论的齿轮系统故障诊断方法

徐文博; 任亚峰; 韩冰

doi:10.16578/j.issn.1004.2539.2020.08.014

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一种基于深度学习理论的齿轮系统故障诊断方法

理论研究 | 更新时间：2022-11-10

- 一种基于深度学习理论的齿轮系统故障诊断方法
- A Fault Diagnosis Approach of Gear System based on Deep Learning Theory
- 机械传动 2020年44卷第8期页码：78-83
- 作者机构：
  
  1.郑州机械研究所有限公司，河南郑州 450052
  2.太原科技大学机械工程学院，山西太原 030024
  3.西北工业大学陕西省机电传动与控制工程实验室，陕西西安 710072
- 作者简介：
  
  徐文博（1986— ），男，河南唐河县人，硕士，工程师，研究方向为弧齿锥齿轮、弯曲疲劳接触等。
- 基金信息：
  
  国家自然科学基金(51535009┫高等学校学科创新引智计划┣B13044)
- DOI：10.16578/j.issn.1004.2539.2020.08.014
  中图分类号：
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徐文博,任亚峰,韩冰.一种基于深度学习理论的齿轮系统故障诊断方法[J].机械传动,2020,44(08):78-83.

Xu Wenbo,Ren Yafeng,Han Bing.A Fault Diagnosis Approach of Gear System based on Deep Learning Theory[J].Journal of Mechanical Transmission,2020,44(08):78-83.
徐文博,任亚峰,韩冰.一种基于深度学习理论的齿轮系统故障诊断方法[J].机械传动,2020,44(08):78-83. DOI： 10.16578/j.issn.1004.2539.2020.08.014.

Xu Wenbo,Ren Yafeng,Han Bing.A Fault Diagnosis Approach of Gear System based on Deep Learning Theory[J].Journal of Mechanical Transmission,2020,44(08):78-83. DOI： 10.16578/j.issn.1004.2539.2020.08.014.

摘要

分别建立了基于快速傅里叶变换和深度置信网络的FFT-DBN模型、基于小波变换和深度卷积神经网络的WT-CNN模型以及基于希尔伯特-黄变换和深度卷积神经网络的HHT-CNN模型，通过将3种深度学习模型有机融合，进一步构建了基于深度学习理论的齿轮系统故障诊断综合评判模型。通过搭建功率封闭齿轮系统振动测试试验台，加工不同故障模式的测试齿轮副并提取其振动加速度信号作为样本，将基于深度学习理论的综合评判模型的故障识别效果与其他模型进行了对比。结果表明，基于深度学习理论的综合评判模型能够有效地辨识出多种齿轮故障；与其他模型相比，基于深度学习理论的综合评判模型的故障识别准确度更高。

Abstract

The FFT-DBN model based on fast Fourier transform and deep belief network， WT-CNN model based on wavelet transform and deep convolutional neural network and HHT-CNN model based on Hilbert Huang transform and deep convolutional neural network are established respectively. Through the integration of the three depth learning models， the comprehensive evaluation model of gear system fault diagnosis based on depth learning is further constructed. By setting up the vibration test bench of the power closed gear system， the test gear pairs with different failure modes are processed and their vibration acceleration signals are extracted as samples， the fault identification effect of the comprehensive evaluation model based on the depth learning is compared with other models， and the results show that the comprehensive evaluation model based on the depth learning can effectively identify a variety of gear faults. Comparing with other models， the fault recognition accuracy of the comprehensive evaluation model based on deep learning is higher.

关键词

故障诊断齿轮振动深度学习

Keywords

Fault diagnosisGear vibrationDeep learning

references

CHEN P，TOYOTA T，HE Z J.Automated function generation of symptom parameters and application to fault diagnosis of machinery under variable operating conditions[J].IEEE Transactions on System，Man and Cybernetics Part A：Systems and Humans，2001，31（6）：775-781.

CHEN P，TANIGUCHI M，TOYOTA T，et al.Fault diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming[J].Mechanical Systems and Signal Processing，2003，19（1）：175-194.

BLOUGH R J.Development and analysis of time variant discrete Fourier transform order tracking[J].Mechanical Systems and Signal Processing，2002，17（6）：1185-1199.

韩秋实，许宝杰，王红军，等.旋转机械故障诊断监测专家系统中的时间序列模式识别技术研究[J].机械工程学报，2002，38（3）：104-107.

HAN Qiushi，XU Baojie，WANG Hongjun，et al.Study on recognition technique of time series analysis in fault diagnosis expert system[J].Journal of Mechanical Engineering，2002，38（3）：104-107.

ZOU J，CHEN J.A comparative study time-frequency feature of cracked rotor by Wigner-Ville distribution and wavelet transform[J].Journal of Sound and Vibration，2004，276（1）：1-11.

YE Z M，WU B，SADEGHIAN A.Current signature analysis of induction motor mechanical faults by wavelet packet decomposition[J].IEEE Transactions on Industrial Electronics，2003，50（6）：1217-1228.

YU D J，CHENG J S，YANG Y.Fault diagnosis approach for roller bearings based on empirical mode decomposition method and Hilbert transform[J].Chinese Journal of Mechanical Engineering （English Edition），2005，18（2）：267-270.

于德介，程军圣，杨宇.Hilbert_Huang变换在齿轮故障诊断中的应用[J].机械工程学报，2005，41（6）：102-107.

YU Dejie，CHENG Junsheng，YANG Yu.Application of Hilbert-Huang transform method to gear fault diagnosis[J].Journal of Mechanical Engineering，2005，41（6）：102-107.

DUAN C D，HE Z J，JIANG H K.A sliding window feature extraction method for rotating machinery based on the lifting scheme[J].Journal of Sound and Vibration，2006，299（4/5）：774-785.

LAUTRE N K，MANNA A.A study on fault diagnosis and maintenance of CNC-WEDM based on binary relational analysis and expert system[J].The International Journal of Advanced Manufacturing Technology，2006，29（5）：490-498.

CHEN Y H，YANG B，DONG J W.Time-series prediction using a local linear wavelet neural network[J].Neurocomputing，2005，69（4）：449-465.

KIM Y J，BAE H，POO K M，et al.Equipment fault diagnosis system of sequencing batch reactors using rule-based fuzzy inference and on-line sensing data[J].Water Science and Technology，2006，53（4/5）：383-392.

ZHANG L，JACK L B，NANDI A K.Fault detection using genetic programming[J].Mechanical Systems and Signal Processing，2005，19（2）：271-289.

ROYA J，GERALD M K.A fuzzy neural network approach to machine condition monitoring[J].Computers & Industrial Engineering，2003，45（2）：323-330.

黄海波，李人宪，杨琪，等.基于DBNs的车辆悬架减振器异响鉴别方法[J].西南交通大学学报，2015，50（5）：776-782.

HUANG Haibo，LI Renxian，YANG Qi，et al.Identifying abnormal noise of vehicle suspension shock absorber based on deep belief networks[J].Journal of Southwest Jiaotong University，2015，50（5）：776-782.

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.

LEI Yaguo，JIA Feng，ZHOU Xin，et al.A deep learing-based method for machinery health monitoring with big data[J].Journal of Mechanical Engineering，2015，51（21）：49-56.

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 Guangquan，GE Qiangqiang，LIU Xiaoyong，et al.Fault feature extraction and diagnosis method based on deep belief network[J].Chinese Journal of Scientific Instrument，2016，37（9）：1946-1953.

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，2017，130：377-388.

CAO P，ZHANG S，TANG J.Pre-processing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning[J].IEEE Access，2017，6（99）：26241-26253.

CHEN Z，LI C，SANCHEZ R V.Multi-layer neural network with deep belief network for gearbox fault diagnosis[J].Journal of Vibroengineering，2015，17（5）：2379-2392.

ZENG X，LIAO Y，LI W.Gearbox fault classification using S-transform and convolutional neural network[C].Proceedings of the 2016 10th International Conference on Sensing Technology （ICST）.New York:IEEE，2016：1-5.

HEYDARZADEH M，HEDAYATI S，NOURANI M，et al.Gear fault diagnosis using discrete wavelet transform and deep neural networks[C].Proceedings of the IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society.New York:IEEE，2016：1494-1500.

LI F，PANG X，YANG Z.Motor current signal analysis using deep neural networks for planetary gear fault diagnosis[J].Measurement，2019，145：45-54.

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