滚动轴承细粒度故障诊断研究

doi:10.3778/j.issn.1002-8331.2210-0341

摘要/Abstract

摘要： 针对目前滚动轴承故障诊断主要采用监督式深度学习提取故障特征以及检测故障种类为粗粒度的现状，提出一种基于高斯混合模型（Gaussian mixed models，GMM）和深度残差收缩网络（deep residual shrinkage networks，DRSN）的滚动轴承细粒度故障诊断方法。GMM模型集成多个高斯分布函数，拟合细粒度故障数据的分布情况，实现对没有标签的轴承振动信号进行聚类，DRSN模型中注意力机制从大量故障特征信息中聚焦于对当前任务更为关键的信息，软阈值化旨在为处于不同健康状态的轴承样本设置不同的阈值。在凯斯西储大学（Case Western Reserve University，CWRU）滚动轴承故障数据中收集30种轴承健康状态对该方法进行了验证，结果表明，将非监督模型与深度学习模型融合，能够处理不含标签情况下的轴承故障数据，实现对轴承故障进行细粒度分类的目的，为后续的设备维护提供依据，具有较好的实际工程意义和推广性。

关键词: 细粒度故障诊断, 滚动轴承, 高斯混合模型, 深度残差收缩网络, 非监督学习, 深度学习

Abstract: Aiming at the current situation that supervised deep learning is mainly used to extract fault features and detect coarse-grained types of faults in rolling bearing fault diagnosis, a fine-grained fault diagnosis method for rolling bearings integrated with Gaussian mixture models (GMM) and deep residual shrinkage networks (DRSN) is proposed. The GMM model integrates multiple Gaussian distribution functions to fit the distribution of fine-grained fault data and realize the clustering of bearing vibration signals without labels. The attention mechanism in DRSN model focused on the more critical information for the current task from a large number of fault feature information. Soft threshold is designed to set different thresholds for bearing samples in different health states. The method is validated by collecting 30 bearing health states from Case Western Reserve University (CWRU) data. The results show that integrate the unsupervised model with the deep learning model, which can process the bearing fault data without labels, achieve the purpose of fine-grained classification of bearing faults, provide a basis for subsequent equipment maintenance, and have good practical engineering significance and popularization.

Key words: fine-grained fault diagnosis, rolling bearing, Gaussian mixture model, deep residual shrinkage network, unsupervised learning, deep learning

阮慧, 黄细霞, 李登峰, 王乐. 滚动轴承细粒度故障诊断研究[J]. 计算机工程与应用, 2024, 60(6): 312-322.

RUAN Hui, HUANG Xixia, LI Dengfeng, WANG Le. Research on Fine-Grained Fault Diagnosis of Rolling Bearings[J]. Computer Engineering and Applications, 2024, 60(6): 312-322.

参考文献

[1] 黄银花. 基于数据融合的滚动轴承故障诊断研究[J]. 电气传动自动化, 2011, 33(3): 56-59.
HUANG Y H. Research on fault diagnosis of rolling bearings based on data fusion[J]. Electric Drive Automation, 2011, 33(3): 56-59.
[2] DUAN Z H, WU T H, GUO S W, et al. Development and trend of condition monitoring and fault diagnosis of multisensors information fusion for rolling bearings: a review[J]. The International Journal of Advanced Manufacturing Technology, 2018, 96(1): 803-819.
[3] 余少勇. 基于深度学习的车辆检测及其细粒度分类关键技术研究[D]. 厦门: 厦门大学, 2017.
YU S Y. Research on key technologies of vehicle detection and its fine-grained classification based on deep learning[D]. Xiamen: Xiamen University, 2017.
[4] 王美华, 吴振鑫, 周祖光. 基于注意力改进CBAM的农作物病虫害细粒度识别研究[J]. 农业机械学报, 2021, 52(4): 239-247.
WANG M H, WU Z X, ZHOU Z G. Fine-grained identification research of crop pests and diseases based on improved CBAM via attention[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(4): 239-247.
[5] 陈前, 刘骊, 付晓东, 等. 部件检测和语义网络的细粒度鞋类图像检索[J]. 中国图象图形学报, 2020, 25(8): 1578-1590.
CHEN Q, LIU L, FU X D, et al. Fine-grained shoe image retrieval by part detection and semantic network[J]. Journal of Image and Graphics, 2020, 25(8): 1578-1590.
[6] 陈立潮, 朝昕, 曹建芳, 等. 融合独立组件的ResNet在细粒度车型识别中的应用[J]. 计算机工程与应用, 2021, 57(11): 248-253.
CHEN L C, CHAO X, CAO J F, et al. Application of ResNet with independent components in fine-grained vehicle recognition[J]. Computer Engineering and Applications, 2021, 57(11): 248-253.
[7] SHAO H D, CHENG J S, JIANG H K, et al. Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing[J]. Knowledge-Based Systems, 2020, 188: 105022.
[8] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521: 436-444.
[9] 周陈林, 董绍江, 李玲, 等. 滚动轴承多状态特征信息的改进型卷积神经网络故障诊断方法[J]. 振动工程学报, 2020, 33(4): 854-860.
ZHOU C L, DONG S J, LI L, et al. Method to improve convolutional neural network in rolling bearing fault diagnosis with multi-state feature information[J]. Journal of Vibration Engineering, 2020, 33(4): 854-860.
[10] SUN W J , ZHAO R, YAN R Q, et al. Convolutional discriminative feature learning for induction motor fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2017, 13(3): 1350-1359.
[11] OH H, JUNG J H, JEON B C, et al. Scalable and unsupervised feature engineering using vibration-imaging and deep learning for rotor system diagnosis[J]. IEEE Transactions on Industrial Electronics, 2018, 65(4): 3539-3549.
[12] CHE C C, WANG H W, NI X M, et al. Domain adaptive deep belief network for rolling bearing fault diagnosis[J]. Computers and Industrial Engineering, 2020, 143: 106427.
[13] SHAO H D, JIANG H K, ZHAO K, et al. A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearings[J]. Mechanical Systems and Signal Processing, 2018, 110: 193-209.
[14] PAN H, TANG W, XU J J, et al. Rolling bearing fault diagnosis based on stacked autoencoder network with dynamic learning rate[J]. Advances in Materials Science and Engineering, 2020, 2020: 6625273.
[15] ZHAO J, YANG S P, LI Q, et al. A new bearing fault diagnosis method based on signal-to-image mapping and convolutional neural network[J]. Measurement, 2021, 176: 109088.
[16] AYAS S, AYAS M S. A novel bearing fault diagnosis method using deep residual learning network[J]. Multimedia Tools and Applications, 2022, 81(16): 22407-22423.
[17] MA H J, LI S M, AN Z H. A fault diagnosis approach for rolling bearing based on convolutional neural network and nuisance attribute projection under various speed conditions[J]. Applied Sciences, 2019, 9(8): 1603.
[18] LIU S W, JIANG H K, WANG Y F, et al. A deep feature alignment adaptation network for rolling bearing intelligent fault diagnosis[J]. Advanced Engineering Informatics, 2022(52): 101598.
[19] 温江涛, 周熙楠. 模糊粒化非监督学习结合随机森林融合的旋转机械故障诊断[J].机械科学与技术, 2018, 37(11): 1722-1730.
WEN J T, ZHOU X N. Fault diagnosis of rotating machinery in combination with unsupervised learning of fuzzy granulation and random forest fusion[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(11): 1722-1730.
[20] GLOWACZ A, GLOWACZ W, GLOWACZ Z, et al. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals[J]. Measurement, 2018, 113: 1-9.
[21] LIAO L, LEE J. A novel method for machine performance degradation assessment based on fixed cycle features test[J]. Journal of Sound and Vibration, 2009, 326: 894-908.
[22] DEMPSTER A P, LAIRD N M, RUBIN D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1977, 39: 1-38.
[23] ZHAO M, ZHONG S, FU X, et al. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2020, 16(7): 4681-4690.
[24] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[25] HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023.
[26] ACUNA S, OPSTAD I S, GODTLIEBSEN F, et al. Soft thresholding schemes for multiple signal classification algorithm[J]. Optics Express, 2020, 28(23): 34434-34449.
[27] LI Z, WANG S H, FAN R R, et al. Teeth category classification via seven-layer deep convolutional neural network with max pooling and global average pooling[J]. International Journal of Imaging Systems and Technology, 2019, 29(4): 577-583.
[28] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]//International Conference on Machine Learning, 2015: 448-456.
[29] SMITH W A, RANDALL R B. Rolling element bearing diagnostics using the case western reserve university data: a benchmark study[J]. Mechanical Systems and Signal Processing, 2015, 64/65: 100-131.