滚动轴承健康智能监测和故障诊断机制研究综述

doi:10.3778/j.issn.1673-9418.2307005

摘要/Abstract

摘要： 轴承作为工业设备机械系统中最关键并且最容易发生故障的零件之一，长期处在高负荷的运行状态。当其发生故障时或者不可逆的磨损时，可能带来事故甚至造成巨大经济损失。因此，对其进行有效的健康监测和故障诊断，对于保障工业设备安全稳定运行有着重要的意义。为进一步促进轴承健康监测和故障诊断技术的发展，对当前现有的模型及方法进行分析与总结，并对现有技术进行划分、对比。从使用的振动信号数据分布出发，首先，对数据分布均匀下的相关方法进行整理，主要按照基于信号分析和基于数据驱动两方面进行研究现状的分类、分析与总结，对该情况下故障检测方法所存在的不足与缺陷进行概述。其次，考虑实际工况下数据采集通常具有不均衡特性的问题，对处理该类情况下的检测方法进行总结，并将现有研究中对该问题的不同处理技术根据其侧重点不同分为数据处理方法、特征提取方法、模型改进方法，并对所存在的问题进行分析。最后，对现有工业设备中轴承故障检测存在的挑战及未来发展方向进行了总结与展望。

关键词: 健康监测, 故障诊断, 数据分布, 信号分析, 数据驱动

Abstract: As one of the most critical and failure-prone parts of the mechanical systems of industrial equipment, bearings are subjected to high loads for long periods of time. When they fail or wear irreversibly, they may cause accidents or even huge economic losses. Therefore, effective health monitoring and fault diagnosis are of great significance to ensure safe and stable operation of industrial equipment. In order to further promote the development of bearing health monitoring and fault diagnosis technology, the current existing models and methods are analyzed and summarized, and the existing technologies are divided and compared. Starting from the distribution of vibration signal data used, firstly, the relevant methods under uniform data distribution are sorted out, the classification, analysis and summary of the current research status are carried out mainly according to signal-based analysis and data-driven-based, and the shortcomings and defects of the fault detection methods in this case are outlined. Secondly, considering the problem of uneven data acquisition under actual working conditions, the detection methods for dealing with such cases are summarized, and different processing techniques for this problem in existing research are classified into data processing methods, feature extraction methods, and model improvement methods according to their different focuses, and the existing problems are analyzed and summarized. Finally, the challenges and future development directions of bearing fault detection in existing industrial equipment are summarized and prospected.

Key words: health monitoring, fault diagnosis, data distribution, signal analysis, data-driven

王婧, 许志伟, 刘文静, 王永生, 刘利民. 滚动轴承健康智能监测和故障诊断机制研究综述[J]. 计算机科学与探索, 2024, 18(4): 878-898.

WANG Jing, XU Zhiwei, LIU Wenjing, WANG Yongsheng, LIU Limin. Review of Research on Rolling Bearing Health Intelligent Monitoring and Fault Diagnosis Mechanism[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(4): 878-898.

参考文献

[1] 曹明, 黄金泉, 周健, 等. 民用航空发动机故障诊断与健康管理现状、挑战与机遇Ⅰ: 气路、机械和FADEC系统故障诊断与预测[J]. 航空学报, 2022, 43(9): 9-41.
CAO M, HUANG J Q, ZHOU J, et al. Current status, challenges and opportunities of civil aero-engine diagnostics & health managementⅠ: diagnosis and prognosis of engine gas path, mechanical and FADEC[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(9): 9-41.
[2] BRACEWELL R N, BRACEWELL R N. The Fourier transform and its applications[M]. New York: McGraw-Hill, 1986.
[3] FARGE M. Wavelet transforms and their applications to turbulence[J]. Annual Review of Fluid Mechanics, 1992, 24(1): 395-458.
[4] BOUDRAA A O, CEXUS J C. EMD-based signal filtering[J]. IEEE Transactions on Instrumentation and Measurement, 2007, 56(6): 2196-2202.
[5] WU Z, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[6] GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010.
[7] FAN X, ZUO M J. Gearbox fault detection using Hilbert and wavelet packet transform[J]. Mechanical Systems and Signal Processing, 2006, 20(4): 966-982.
[8] 李心一, 谢志江, 罗久飞. 加窗插值快速傅里叶变换在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2018, 29(10): 1166-1172.
LI X Y, XIE Z J, LUO J F. Applications of windowed interpolation FFT algorithm in rolling bearing fault diagnosis[J]. China Mechanical Engineering, 2018, 29(10): 1166-1172.
[9] 李恒, 张氢, 秦仙蓉, 等. 基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J]. 振动与冲击, 2018, 37(19):124-131.
LI H, ZHANG Q, QIN X R, et al. Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network[J]. Journal of Vibration and Shock, 2018, 37(19): 124-131.
[10] BAO Z, DU J, ZHANG W, et al. A transformer model-based approach to bearing fault diagnosis[C]//Proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, Taiyuan, Sep 17-20, 2021. Singapore: Springer, 2021: 65-79.
[11] TAO X, REN C, WU Y, et al. Bearings fault detection using wavelet transform and generalized Gaussian density modeling[J]. Measurement, 2020, 155: 107557.
[12] GUO J, LIU X, LI S, et al. Bearing intelligent fault diagnosis based on wavelet transform and convolutional neural network[J]. Shock and Vibration, 2020(19): 1-14.
[13] LIANG P, WANG W, YUAN X, et al. Intelligent fault diagnosis of rolling bearing based on wavelet transform and improved ResNet under noisy labels and environment[J]. Engineering Applications of Artificial Intelligence, 2022, 115: 105269.
[14] 唐纪凯, 卢一相, 柏壮壮, 等. 基于同步压缩小波变换和CNN的滚动轴承故障诊断[J]. 传感器与微系统, 2022, 41(6): 130-133.
TANG J K, LU Y X, BAI Z Z, et al. Fault diagnosis of rolling bearing based on synchrosqueezed wavelet transform and CNN[J]. Transducer and Microsystem Technologies, 2022, 41(6): 130-133.
[15] SUN Y, LI S, WANG X. Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image[J]. Measurement, 2021, 176: 109100.
[16] GROVER C, TURK N. Rolling element bearing fault diagnosis using empirical mode decomposition and hjorth parameters[J]. Procedia Computer Science, 2020, 167: 1484-1494.
[17] KUMAR H S, MANJUNATH S H. Use of empirical mode decomposition and K-nearest neighbour classifier for rolling element bearing fault diagnosis[J]. Materials Today: Proceedings, 2022, 52: 796-801.
[18] 李俊, 刘永葆, 余又红. 基于经验模态分解剩余信号能量特征的滚动轴承故障模式智能识别[J]. 燃气涡轮试验与研究, 2020, 33(3): 28-32.
LI J, LIU Y B, YU Y H. Intelligent pattern recognition of rolling bearing fault based on EMD residual characteristics signal energy[J]. Gas Turbine Experiment and Research, 2020, 33(3): 28-32.
[19] HU Y, ZHANG S, JIANG A, et al. A new method of wind turbine bearing fault diagnosis based on multi-masking empirical mode decomposition and fuzzy C-means clustering[J]. Chinese Journal of Mechanical Engineering, 2019, 32(1): 1-12.
[20] YE X, HU Y, SHEN J, et al. An improved empirical mode decomposition based on adaptive weighted rational quartic spline for rolling bearing fault diagnosis[J]. IEEE Access, 2020, 8: 123813-123827.
[21] 周浪, 王礼桂, 胡雷, 等. 基于分段累计近似与自适应噪声辅助集成经验模态分解的滚动轴承故障诊断方法[J]. 轴承, 2023(2): 26-30.
ZHOU L, WANG L G, HU L, et al. Fault diagnosis method for rolling bearings based on PAA and CEEMDAN[J]. Bearing, 2023(2): 26-30.
[22] GU J, PENG Y. An improved complementary ensemble empirical mode decomposition method and its application in rolling bearing fault diagnosis[J]. Digital Signal Processing, 2021, 113: 103050.
[23] 杜小磊, 陈志刚, 王衍学, 等. 形态经验小波变换和改进分形网络在轴承故障识别中的应用[J]. 振动工程学报, 2021, 34(3): 654-662.
DU X L, CHEN Z G, WANG Y X, et al. Application of morphological empirical wavelet transform and IFractalNet in bearing fault identification[J]. Journal of Vibration Engineering, 2021, 34(3): 654-662.
[24] 李继猛, 王慧, 李铭, 等. 基于改进的自适应无参经验小波变换的滚动轴承故障诊断[J]. 计量学报, 2020, 41(6): 710-716.
LI J M, WANG H, LI M, et al. Rolling bearing fault diagnosis based on improved adaptive parameterless empirical wavelet transform[J]. Acta Metrologica Sinica, 2020, 41(6):710-716.
[25] LI J, WANG H, WANG X, et al. Rolling bearing fault diagnosis based on improved adaptive parameterless empirical wavelet transform and sparse denoising[J]. Measurement, 2020, 152: 107392.
[26] GOUGAM F, RAHMOUNE C, BENAZZOUZ D, et al. Bearing fault diagnosis based on feature extraction of empirical wavelet transform (EWT) and fuzzy logic system (FLS) under variable operating conditions[J]. Journal of Vibroengineering, 2019, 21(6): 1636-1650.
[27] LI G, DENG C, WU J, et al. Rolling bearing fault diagnosis based on wavelet packet transform and convolutional neural network[J]. Applied Sciences, 2020, 10(3): 770.
[28] YU X, LIANG Z, WANG Y, et al. A wavelet packet transform-based deep feature transfer learning method for bearing fault diagnosis under different working conditions[J]. Measurement, 2022, 201: 111597.
[29] MOUMENE I, OUELAA N. Gears and bearings combined faults detection using optimized wavelet packet transform and pattern recognition neural networks[J]. The International Journal of Advanced Manufacturing Technology, 2022, 120(7/8): 4335-4354.
[30] GUO J, SHI Z, ZHEN D, et al. Modulation signal bispectrum with optimized wavelet packet denoising for rolling bearing fault diagnosis[J]. Structural Health Monitoring, 2022, 21(3): 984-1011.
[31] SHAO H, JIANG H, ZHANG X, et al. Rolling bearing fault diagnosis using an optimization deep belief network[J]. Measurement Science and Technology, 2015, 26(11): 115002.
[32] EREN L, INCE T, KIRANYAZ S. A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier[J]. Journal of Signal Processing Systems, 2019, 91: 179-189.
[33] SHAO H, JIANG H, ZHAO H, et al. A novel deep autoencoder feature learning method for rotating machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2017, 95: 187-204.
[34] NEUPANE D, SEOK J. Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: a review[J]. IEEE Access, 2020, 8: 93155-93178.
[35] ZHU J, CHEN N, SHEN C. A new deep transfer learning method for bearing fault diagnosis under different working conditions[J]. IEEE Sensors Journal, 2019, 20(15): 8394-8402.
[36] CHE C, WANG H, NI X, et al. Domain adaptive deep belief network for rolling bearing fault diagnosis[J]. Computers & Industrial Engineering, 2020, 143: 106427.
[37] TANG J, WU J, HU B, et al. Towards a fault diagnosis method for rolling bearing with bi-directional deep belief network[J]. Applied Acoustics, 2022, 192: 108727.
[38] YAN X, LIU Y, JIA M. Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions[J]. Knowledge-Based Systems, 2020, 193: 105484.
[39] YU J, LIU G. Knowledge extraction and insertion to deep belief network for gearbox fault diagnosis[J]. Knowledge-Based Systems, 2020, 197: 105883.
[40] XING S, LEI Y, WANG S, et al. Distribution-invariant deep belief network for intelligent fault diagnosis of machines under new working conditions[J]. IEEE Transactions on Industrial Electronics, 2020, 68(3): 2617-2625.
[41] 吴楠, 王喆. 基于一维CNN与BiLSTM结合的轴承故障诊断[J]. 组合机床与自动化加工技术, 2021(9): 38-41.
WU N, WANG Z. Bearing fault diagnosis based on the combination of one-dimensional CNN and Bi-LSTM[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2021(9): 38-41.
[42] CHEN X, ZHANG B, GAO D. Bearing fault diagnosis base on multi-scale CNN and LSTM model[J]. Journal of Intelligent Manufacturing, 2021, 32: 971-987.
[43] HAN S, JEONG J. An weighted CNN ensemble model with small amount of data for bearing fault diagnosis[J]. Procedia Computer Science, 2020, 175: 88-95.
[44] SONG X, CONG Y, SONG Y, et al. A bearing fault diagnosis model based on CNN with wide convolution kernels[J]. Journal of Ambient Intelligence and Humanized Computing, 2022,13(8): 4041-4056.
[45] PINEDO-SANCHEZ L A, MERCADO-RAVELL D A, CARBALLO-MONSIVAIS C A. Vibration analysis in bearings for failure prevention using CNN[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2020, 42(12): 628.
[46] PLAKIAS S, BOUTALIS Y S. Fault detection and identification of rolling element bearings with attentive dense CNN[J]. Neurocomputing, 2020, 405: 208-217.
[47] MAO W, FENG W, LIU Y, et al. A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2021, 150: 107233.
[48] HE Z, SHAO H, JING L, et al. Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder[J]. Measurement, 2020, 152: 107393.
[49] XIONG X, JIANG H, LI X, et al. A Wasserstein gradient-penalty generative adversarial network with deep auto-encoder for bearing intelligent fault diagnosis[J]. Measurement Science and Technology, 2020, 31(4): 045006.
[50] ZHANG Y, LI X, GAO L, et al. Intelligent fault diagnosis of rotating machinery using a new ensemble deep auto-encoder method[J]. Measurement, 2020, 151: 107232.
[51] TONG J, LUO J, ZHENG J. Rolling bearing fault diagnosis method based on enhanced deep auto-encoder network[J]. China Mechanical Engineering, 2021, 32(21): 2617.
[52] KONG X, MAO G, WANG Q, et al. A multi-ensemble method based on deep auto-encoders for fault diagnosis of rolling bearings[J]. Measurement, 2020, 151: 107132.
[53] 刘权, 裴未迟. 基于自注意力机制条件残差生成对抗网络的滚动轴承故障诊断[J]. 轴承, 2022(11): 68-75.
LIU Q, PEI W C. Fault diagnosis for rolling bearings based on conditional residual generative adversarial networks of self-attention mechanism[J]. Bearing, 2022(11): 68-75.
[54] 邢晓松, 郭伟. 基于改进半监督生成对抗网络的少量标签轴承智能诊断方法[J]. 振动与冲击, 2022, 41(22): 184-192.
XING X S, GUO W. Intelligent diagnosis method for bearings with few labelled samples based on an improved semi-supervised learning-based generative adversarial network[J]. Journal of Vibration and Shock, 2022, 41(22): 184-192.
[55] GAO Y, LIU X, XIANG J. FEM simulation-based generative adversarial networks to detect bearing faults[J]. IEEE Transactions on Industrial Informatics, 2020, 16(7): 4961-4971.
[56] LIU S, JIANG H, WU Z, et al. Rolling bearing fault diagnosis using variational autoencoding generative adversarial networks with deep regret analysis[J]. Measurement, 2021, 168: 108371.
[57] GUO Q, LI Y, SONG Y, et al. Intelligent fault diagnosis method based on full 1-D convolutional generative adversarial network[J]. IEEE Transactions on Industrial Informatics, 2019, 16(3): 2044-2053.
[58] 毛文涛, 施华东, 张艳娜, 等. 轴承在线早期故障检测的无监督张量深度迁移学习方法[J/OL]. 控制与决策 [2023-02-15]. https://doi.org/10.13195/j.kzyjc.2022.1101.
MAO W T, SHI H D, ZHANG Y N, et al. Research on unsupervised tensor-based deep transfer learning for online early fault detection of bearing[J/OL]. Control and Decision[2023-02-15]. https://doi.org/10.13195/j.kzyjc.2022.1101.
[59] 贾峰, 李世豪, 沈建军, 等. 采用深度迁移学习与自适应加权的滚动轴承故障诊断[J]. 西安交通大学学报, 2022, 56(8): 1-10.
JIA F, LI S H, SHEN J J, et al. Fault diagnosis of rolling bearings using deep transfer learning and adaptive weighting[J]. Journal of Xi??an Jiaotong University, 2022, 56(8): 1-10.
[60] 施杰, 伍星, 柳小勤, 等. 变分模态分解结合深度迁移学习诊断机械故障[J]. 农业工程学报, 2020, 36(14): 129-137.
SHI J, WU X, LIU X Q, et al. Mechanical fault diagnosis based on variational mode decomposition combined with deep transfer learning[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(14): 129-137.
[61] XU Y, SUN Y, LIU X, et al. A digital-twin-assisted fault diagnosis using deep transfer learning[J]. IEEE Access, 2019, 7: 19990-19999.
[62] MA P, ZHANG H, FAN W, et al. A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network[J]. Measurement Science and Technology, 2019, 30(5): 055402.
[63] LIN C T, HSIEH T Y, LIU Y T, et al. Minority oversampling in kernel adaptive subspaces for class imbalanced datasets[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(5): 950-961.
[64] MALDONADO S,WEBER R, FAMILI F. Feature selection for high-dimensional class-imbalanced data sets using support vector machines[J]. Information Sciences, 2014, 286: 228-246.
[65] BATUWITA R, PALADE V. FSVM-CIL: fuzzy support vector machines for class imbalance learning[J]. IEEE Transactions on Fuzzy Systems, 2010, 18(3): 558-571.
[66] TANG H, LU S, QIAN G, et al. IoT-based signal enhancement and compression method for efficient motor bearing fault diagnosis[J]. IEEE Sensors Journal, 2020, 21(2): 1820-1828.
[67] WEI J, HUANG H, YAO L, et al. New imbalanced bearing fault diagnosis method based on sample-characteristic over-sampling technique (SCOTE) and multi-class LSSVM[J]. Applied Soft Computing, 2021, 101: 107043.
[68] ISLAM M M M, KIM J M. Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector machines[J]. Reliability Engineering & System Safety, 2019, 184: 55-66.
[69] 李梦男, 李琨, 叶震, 等. 结合SE-VAE与M1DCNN的小样本数据下轴承故障诊断[J/OL]. 机械科学与技术 [2023-02-15]. https://doi.org/10.13433/j.cnki.1003-8728.20230036.
LI M N, LI K, YE Z, et al. Bearing fault diagnosis based on small sample data of SE-VAE and M1DCNN[J/OL]. Mechanical Science and Technology for Aerospace Engineering [2023-02-15]. https://doi.org/10.13433/j.cnki.1003-8728.20230036.
[70] 徐卓飞, 李旭东, 张婵婵, 等. 基于孪生网络的小样本滚动轴承故障诊断研究[J]. 仪器仪表学报, 2022, 43(10): 241-251.
XU Z F, LI X D, ZHANG C C, et al. Fault diagnosis of rolling bearings with limited samples based on siamese network[J]. Chinese Journal of Scientific Instrument, 2022, 43(10): 241-251.
[71] 尚志武, 钱仕淇. 基于改进孪生胶囊网络的小样本轴承故障诊断[J/OL]. 轴承 [2023-02-15]. http://kns.cnki.net/kcms/detail/41.1148.TH.20221013.0801.002.html.
SHANG Z W, QIAN S Q. Rolling bearing fault diagnosis based on improved siamese capsule network under small samples[J/OL]. Bearing [2023-02-15]. http://kns.cnki.net/kcms/detail/41.1148.TH.20221013.0801.002.html.
[72] 简献忠, 张韬. 一种不平衡数据下的新型轻量级轴承故障诊断模型[J/OL]. 控制工程[2023-02-15]. https://doi.org/10.14107/j.cnki.kzgc.20211071.
JIAN X Z, ZHANG T. A new lightweight bearing fault diagnosis model under unbalanced data[J/OL]. Control Engineering of China [2023-02-15]. https://doi.org/10.14107/j.cnki.kzgc.20211071.
[73] ZHAO C, SUN J, LIN S, et al. Fault diagnosis method for rolling mill multi row bearings based on AMVMD-MC1DCNN under unbalanced dataset[J]. Sensors, 2021, 21(16): 5494.
[74] HANG Q, YANG J, XING L. Diagnosis of rolling bearing based on classification for high dimensional unbalanced data[J]. IEEE Access, 2019, 7: 79159-79172.
[75] ZHOU F, YANG S, FUJITA H, et al. Deep learning fault diagnosis method based on global optimization GAN for unba-lanced data[J]. Knowledge-Based Systems, 2020, 187: 104837.
[76] WANG R, ZHANG S, LIU S, et al. A bearing fault diagnosis method for high-noise and unbalanced dataset[J]. Smart and Resilient Transportation, 2023, 5(1): 28-45.
[77] 胡向东, 梁川, 杨希. 基于时频增强的滚动轴承少样本故障诊断方法[J]. 计量学报, 2023, 44(1): 12-20.
HU X D, LIANG C, YANG X. A method of fault diagnosis with few samples for rolling bearing based on time-frequency enhancement[J]. Acta Metrologica Sinica, 2023, 44(1): 12-20.
[78] 康玉祥, 陈果, 潘文平, 等. 不平衡数据下基于双经验池深度强化学习的滚动轴承故障诊断[J/OL]. 航空动力学报[2023-02-15]. https://doi.org/10.13224/j.cnki.jasp.20220417.
KANG Y X, CHEN G, PAN W P, et al. Fault diagnosis of rolling bearing based on dual-experience pool deep reinforcement learning under unbalanced data[J/OL]. Journal of Aerospace Power [2023-02-15]. https://doi.org/10.13224/j.cnki.jasp.20220417.
[79] 雷春丽, 夏奔锋, 薛林林, 等. 小样本下自校正卷积神经网络的滚动轴承故障识别方法[J]. 仪器仪表学报, 2022, 43(9): 122-130.
LEI C L, XIA B F, XUE L L, et al. Fault identification for rolling bearing by self-calibrated convolutional neural network under small samples conditions[J]. Chinese Journal of Scientific Instrument, 2022, 43(9): 122-130.
[80] ZHAO Y, ZHANG X, WANG J, et al. A new data fusion driven-sparse representation learning method for bearing intelligent diagnosis in small and unbalanced samples[J]. Engineering Applications of Artificial Intelligence, 2023, 117: 105513.
[81] XU K, LI S, JIANG X, et al. A renewable fusion fault diagnosis network for the variable speed conditions under unbalanced samples[J]. Neurocomputing, 2020, 379: 12-29.
[82] ZHAO K, JIA F, SHAO H. Unbalanced fault diagnosis of rolling bearing by using transfer adaptive Boosting with squeeze-and-excitation attention convolutional neural network[J]. Measurement Science and Technology, 2023, 34(4): 044006.
[83] 陶新民, 李晨曦, 李青, 等. 不均衡最大软间隔SVDD轴承故障检测模型[J]. 振动工程学报, 2019, 32(4): 718-729.
TAO X M, LI C X, LI Q, et al. Rolling bearings fault detection model using imbalanced maximum soft margin support vector domain description[J]. Journal of Vibration Engineering, 2019, 32(4): 718-729.
[84] 李青, 李丽君, 董增寿. 基于1DMCNN_BIGRU_SAM的轴承数据不平衡故障诊断[J]. 现代制造工程, 2022(12): 117-124.
LI Q, LI L J, DONG Z S. Fault diagnosis of bearing data imbalance based on 1DMCNN_BIGRU_SAM[J]. Modern Manufacturing Engineering, 2022(12): 117-124.
[85] LI F, CHEN J, HE S, et al. Layer regeneration network with parameter transfer and knowledge distillation for intelligent fault diagnosis of bearing using class unbalanced sample[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-10.
[86] WANG P, XIONG H, HE H. Bearing fault diagnosis under various conditions using an incremental learning-based multi-task shared classifier[J]. Knowledge-Based Systems, 2023, 266: 110395.
[87] ZHANG X, LI J, WU W, et al. Multi-fault classification and diagnosis of rolling bearing based on improved convolution neural network[J]. Entropy, 2023, 25(5): 737.
[88] CHEN Z, YU W, KONG C, et al. A dual-view network for fault diagnosis in rotating machinery using unbalanced data[J]. Measurement Science and Technology, 2023, 34(11): 115107.
[89] DUPREE E J, JAYATHIRTHA M, YORKEY H, et al. A critical review of bottom-up proteomics: the good, the bad, and the future of this field[J]. Proteomes, 2020, 8(3): 14.
[90] RAI V K, MOHANTY A R. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing, 2007, 21(6): 2607-2615.
[91] 杨洁祎, 董一鸿, 钱江波. 基于图神经网络的小样本学习方法研究进展[J/OL]. 计算机研究与发展 [2023-07-30]. http://kns.cnki.net/kcms/detail/11.1777.TP.20230720.1411.020.html.
YANG J Y, DONG Y H, QIAN J B. Research progress of few-shot learning methods based on graph neural networks[J/OL]. Journal of Computer Research and Development [2023-07-30]. http://kns.cnki.net/kcms/detail/11.1777.TP.20230720. 1411.020.html.
[92] 胡春生, 李国利, 赵勇, 等. 变工况滚动轴承故障诊断方法综述[J]. 计算机工程与应用, 2022, 58(18): 26-42.
HU C S, LI G L, ZHAO Y, et al. Summary of fault diagnosis methods for rolling bearings under variable working conditions[J]. Computer Engineering and Applications, 2022, 58(18): 26-42.
[93] WANG X, MAO D, LI X. Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network[J]. Measurement, 2021, 173: 108518.
[94] 张永芳, 王霞, 邢志国, 等. 面向机械装备健康监测的振动传感器研究现状[J]. 材料导报, 2020, 34(13): 13121-13130.
ZHANG Y F, WANG X, XING Z G, et al. Research on vibration sensors for health monitoring of mechanical equipment[J]. Materials Reports, 2020, 34(13): 13121-13130.
[95] 房芳, 郑辉, 汪玉, 等. 机械结构健康监测综述[J]. 机械工程学报, 2021, 57(16): 269-292.
FANG F, ZHENG H, WANG Y, et al. Mechanical structural health monitoring: a review[J]. Journal of Mechanical Engineering, 2021, 57(16): 269-292.
[96] CHOUDHARY A, MIAN T, FATIMA S, et al. Passive thermography based bearing fault diagnosis using transfer learning with varying working conditions[J]. IEEE Sensors Journal, 2022, 23(5): 4628-4637.
[97] WANG X, SHEN C, XIA M, et al. Multi-scale deep intra-class transfer learning for bearing fault diagnosis[J]. Reliability Engineering & System Safety, 2020, 202: 107050.
[98] LI Y, JIANG W, ZHANG G, et al. Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data[J]. Renewable Energy, 2021, 171: 103-115.
[99] VERBRAEKEN J, WOLTING M, KATZY J, et al. A survey on distributed machine learning[J]. ACM Computing Surveys , 2020, 53(2): 1-33.
[100] CHEN Z, XIAO F, GUO F, et al. Interpretable machine learning for building energy management: a state-of-the-art review[J]. Advances in Applied Energy, 2023: 100123.