基于迁移学习与加权多通道融合的齿轮箱故障诊断

PDF(3922 KB)

振动与冲击 ›› 2023, Vol. 42 ›› Issue (9) : 236-246.

论文

侯召国，王华伟，熊明兰，王峻洲

作者信息 +

Gearbox fault diagnosis based on transfer learning and weighted multi-channel fusion

HOU Zhaoguo, WANG Huawei, XIONG Minglan, WANG Junzhou

Author information +

文章历史 +

摘要

针对齿轮箱单一传感器故障识别精度波动大、数据利用率低、可靠性低及故障诊断模型在多工况下泛化能力不足等问题，提出了一种加权融合多通道数据与深度迁移模型的齿轮箱故障诊断方法。首先，为了充分挖掘齿轮箱多通道数据的信息，提出了基于信息熵加权的多通道融合方法，采用信息熵法计算各通道数据的融合权重，并对各通道的采样数据进行加权融合。其次，利用源域的融合数据对深度迁移模型进行预训练，将预训练得到的模型参数作为目标域模型的初始化参数，同时冻结目标域模型特征提取器的参数，并利用目标域的融合数据对目标域模型分类器的参数进行微调，实现深度迁移模型从源域到目标域的迁移以适应新的目标样本识别任务。最后，齿轮箱多工况迁移诊断试验结果表明，所提方法可有效用于齿轮箱的故障诊断，相比传统迁移学习方法BDA、TCA、JDA、JGSA、GFK及深度迁移学习方法AdaBN、MK-MMD、DCTLN这8种当前常用方法，具有更高的平均迁移诊断精度和变工况下良好的泛化性能。

Abstract

A gearbox fault diagnosis method based on weighted fusion of multi-channel data and deep transfer model was proposed to solve the problems of large fluctuation of fault identification accuracy of single sensor, low data utilization, low reliability and insufficient generalization ability of fault diagnosis model under multiple working conditions. Firstly, in order to fully mine the information of multi-channel data of gearbox, a multi-channel fusion method based on information entropy weighting is proposed. The fusion weight of data of each channel is calculated by using information entropy method, and the sampled data of each channel is weighted fused. Secondly, the deep transfer model is pre-trained by using the fusion data of source domain, and the model parameters obtained by the pre-training are used as the initialization parameters of the target domain model. Meanwhile, the parameters of the feature extractor of target domain model are frozen, and the parameters of the classifier of target domain model are fine-tuned by using the fusion data of target domain. In order to adapt to the new target sample recognition task, the deep transfer model is transferred from source domain to target domain. Finally, the multi-condition transfer diagnosis test results of gearbox show that the proposed method can be effectively used for gearbox fault diagnosis. Compared with the traditional transfer learning methods BDA, TCA, JDA, JGSA, GFK and the deep transfer learning methods AdaBN, MK-MMD, DCTLN, It has higher average transfer diagnosis accuracy and better generalization performance under variable working conditions.

导出引用

侯召国，王华伟，熊明兰，王峻洲. 基于迁移学习与加权多通道融合的齿轮箱故障诊断[J]. 振动与冲击, 2023, 42(9): 236-246

HOU Zhaoguo, WANG Huawei, XIONG Minglan, WANG Junzhou. Gearbox fault diagnosis based on transfer learning and weighted multi-channel fusion[J]. Journal of Vibration and Shock, 2023, 42(9): 236-246

参考文献

[1] LIU R N,YANG B Y,ZIO E,et al. Artificial intelligence for fault diagnosis of rotating machinery: A review[J]. Mechanical Systems and Signal Processing,2018,108:33-47.
[2] LEI Y G,YANG B,JIANG X W,et al. Applications of machine learning to machine fault diagnosis: A review and roadmap[J]. Mechanical Systems and Signal Processing,2020,138:106587.
[3] 韩淞宇,邵海东,姜洪开,等.基于提升卷积神经网络的航空发动机高速轴承智能故障诊断[J].航空学报.http://kns.cnki.net/kcms/detail/11.1929.V.20210619.0040.002.html
HAN Songyu,SHAO Haidong,JIANG Hongkai,et al.Intelligent fault diagnosis of aero-engine high-speed bearing using enhanced onvolutional neural network[J].Acta Aeronautica et Astronautica Sinica. http://kns.cnki.net/kcms/detail/11.1929.V.20210619.0040.002.html
[4] SONG Y,LI Y B,JIA L,et al. Retraining Strategy-Based Domain Adaption Network for Intelligent Fault Diagnosis[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2020,16(9):6163-6171.
[5] ZHANG T C,CHEN J L,LI F D,et al. Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions[J]. ISA Transactions,2021, https://doi.org/10.1016/j.isatra.2021.02.042.
[6] SHAO H D,JIANG H K,LIN Y,et al. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders[J]. Mechanical Systems and Signal Processing,2018,102:278-297.
[7] 田科位,董绍江,姜保军,等.基于改进深度残差网络的轴承故障诊断方法[J].振动与冲击,2021,40(20):247-254.
TIAN Kewei,DONG Shaojiang,et al.A bearing fault diagnosis method based on improved depth residual network[J].JOURNAL OF VIBRATION AND SHOCK, 2021,40(20):247-254.
[8] AZAMFAR M,SINGH J,BRAVO-IMAZ I,et al. Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis[J]. Mechanical Systems and Signal Processing,2020,144:106861.
[9] 侯文擎,叶鸣,李巍华.基于改进堆叠降噪自编码的滚动轴承故障分类[J].机械工程学报,2018,54(7):87-96.
HOU Wenqing,YE Ming LI Weihua,et al.Rolling Eelment Bearing Fault Classification Using Improved Stacked Denoising Autoencoders[J].JOURNAL OF MECHANICAL ENGINEERING,2018,54(7):87-96.
[10] 邵海东,张笑阳,程军圣,等.基于提升深度迁移自动编码器的轴承智能故障诊断[J].机械工程学报,2020,56(9):84-90.
SHAO Haidong,ZHANG Xiaoyang,CHENG Junsheng,et al.Intelligent Fault Diagnosis of Bearing Using Ehanced Deep Transfer Autoencoder[J]. JOURNAL OF MECHANICAL ENGINEERING,2020,56(9):84-90.
[11] LI X,ZHANG W,X N X,et al. Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2020,67(8):6785-6794.
[12] LI F,TANG T J,T B P,et al. Deep convolution domain-adversarial transfer learning for fault diagnosis of rolling bearings[J]. Measurement,2021,169:108339.
[13] LI X,ZHANG W,DING Q,et al. Multi-Layer domain adaptation method for rolling bearing fault diagnosis[J]. Signal Processing,2019,157:180-197.
[14] YANG B,LEI Y G,JIA F,et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings[J]. Mechanical Systems and Signal Processing,2019,122:692-706.
[15] ZOU Y S,LIU Y Z,DENG J L,et al. A novel transfer learning method for bearing fault diagnosis under different working conditions[J]. Measurement,2021,171:108767.
[16] JIAO J Y,ZHAO M,LIN J,et al. Residual joint adaptation adversarial network for intelligent transfer fault diagnosis[J]. Mechanical Systems and Signal Processing,2020,145:106962.
[17] QIN Y,YAO Q W,WANG Y,et al. Parameter sharing adversarial domain adaptation networks for fault transfer diagnosis of planetary gearboxes[J]. Mechanical Systems and Signal Processing,2021,160:107936.
[18] 刘飞,陈仁文,刑凯玲,等.基于迁移学习与深度残差网络的滚动轴承快速故障诊断算法[J].振动与冲击,2022,41(3):154-164.
LIU Fei,CHEN Renwen,XING Kailing,et al.Fast fault diagnosis algorithm for rolling bearing based on transfer learning and deep residual network[J].JOURNAL OF VIBRATION AND SHOCK, 2022,41(3):154-164.
[19] 陈超,沈飞,严如强.改进LSSVM迁移学习方法的轴承故障诊断[J].仪器仪表学报,2017,38(1):33-40.
CHEN Chao,SHEN Fei,YAN Ruqiang,et al.Enhanced least squares support vector machine based transfer learning strategy for bearing fault diagnosis[J].Chinese Journal of Scientific Instrument,2017,38(1):33-40.
[20] 陈祝云,钟琪,黄如意,等.基于增强迁移卷积神经网络的机械智能故障诊断[J].机械工程学报,2021,57:1-10.
CHEN Zhuyun,ZHONG Qi,HUANG Ruyi,et al.Intelligent Fault Diagnosis for Machinery Based on Enhanced Transfer Convolutional Neural Network[J].JOURNAL OF MECHANICAL ENGINEERING,2021,57:1-10.
[21] SHAO H D,LIN J,ZHANG L W,et al. A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance[J]. Information Fusion,2021,74:65-76.
[22] ZHAO X L,JIA M P,DING P,et al. Intelligent Fault Diagnosis of Multichannel Motor–Rotor System Based on Multimanifold Deep Extreme Learning Machine[J]. IEEE/ASME TRANSACTIONS ON MECHATRONICS,2020,25(5):2177-2187.
[23] 杨洁,万安平,王景霖,等.基于多传感器融合卷积神经网络的航空发动机轴承故障诊断[J].中国电机工程学报.htts://kns.cnki.net/kcms/detail/11.2107.TM.20211101.1325.007.html
YANG Jie,WAN Anping,WANG Jinglin，et al.Aeroengine Bearing Fault Diagnosis Based on Convolutional Neural Network for Multi-sensor Information Fusion[J].Proceegings of the CSEE. .htts://kns.cnki.net/kcms/detail/11.2107.TM.20211101.1325.007.html
[24] 陈仁祥,黄鑫,胡小林,等.多源信息深度融合的行星齿轮箱故障诊断方法[J].振动工程学报,2020,33(5):1094-1102.
CHEN Renxiang,HUANG Xin,HU Xiaolin,et al.Planetary gearbox fault diagnosis technique based on multi-source information deep fusion[J].Journal of Vibration Engineering, 2020,33(5):1094-1102.
[25] 单增海,李志远,张旭,等.基于多传感器信息融合和多粒度级联深林模型的液压泵健康状态评估[J].中国机械工程,2021,32(10):2374-2382.
SHAN Zenghai,LI Zhiyuan,ZHANG Xu,at al.Health Status Assessment of Hydraulic Pumps Based on Multi-sensor Information Fusion and Multi-grained Cascade Forest Model[J].CHINA MECHANICAL ENGINEERING, 2021,32(10):2374-2382.
[26] ZHANG W,LI C H,PENG G L,et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load[J]. Mechanical Systems and Signal Processing,2018,100:439-453.
[27] ZHANG W,PENG G L,LI C H,et al. A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals[J]. Sensors,2017,17,425.
[28] XU X W,TAO Z R,MING W W,et al. Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion[J]. Measurement,2020,165:108086.
[29] SHAO S Y,MCALEER S,YAN R Q,et al. HighlyAccurate Machine Fault Diagnosis Using Deep Transfer Learning[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2019,15(4):2446-2455.
[30] PHMSociety, PHM09 Data Challenge, Available: https://www.phmsociety.org/competition/PHM/09/apparatus, 2019.
[31] 井陆阳.基于深度卷积模型的旋转机械故障诊断方法研究[D].天津:天津大学,2017,32-34.
JING Luyang.Research on the fault diagnosis method for rotating machinery using deep convolutional neural network[D].TIAN Jin:Tianjin University,2017,32-34.
[32] LIU S W, JIANG H K , WU Z H,et al. Rolling bearing fault diagnosis using variational autoencoding generative adversarial networks with deep regret analysis [J]. Measurement,2021,(168):108371.
[33] MAO W T,HE L,YAN Y J,et al. Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine[J]. Mechanical Systems and Signal Processing,2017,(83):450-473.
[34] ZHOU Q,LI Y B,YU T,et al. A novel method based on nonlinear auto-regression neural network and convolutional neural network for imbalanced fault diagnosis of rotating machinery[J]. Measurement,2020,(161):107880.
[35] WANG J D,CHEN Y Q,HAO S J,et al. Balanced Distribution Adaptation for Transfer Learning//[C] 2017 IEEE International Conference on Data Mining.IEEE,2017,1129-1134.
[36] PAN S J,W.TSANG I,T.KWOK J,et al. Domain Adaptation via Transfer Component Analysis[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS,2011,22(2):199-210.
[37] LONG M S,WANG J M,DING G G,et al. Transfer Feature Learning with Joint Distribution Adaptation//[C]ICCV2013.ICCV,2013,2200-2207.
[38] ZHANG J,LI W Q,OGUNBONA P,et al. Joint Geometrical and Statistical Alignment for Visual Domain Adaptation//[C]CVPR.CVPR,2017,1859-1867.
[39] GONG B Q, SHI Y,SHA F,et al. Geodesic Flow Kernel for Unsupervised Domain Adaptation//[C]CVPR.CVPR,2012,1-8.
[40] LI Y H,WANG N Y,SHI J P,et al. Adaptive Batch Normalization for practical domain adaptation[J]. Pattern Recognition,2018,80:109-117.
[41] LONG M S,GAO Y,WANG J M,et al. Learning Transferable Features with Deep Adaptation Networks//[C]ICML.ICML,2015.
[42] GUO L,LEI Y G,XING S B,et al. Deep Convolutional Transfer Learning Network:A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2019,66(9):7316-7325.