计算机工程与应用 ›› 2023, Vol. 59 ›› Issue (6): 45-56.DOI: 10.3778/j.issn.1002-8331.2208-0139
司伟伟,岑健,伍银波,胡学良,何敏赞,杨卓洪,陈红花
出版日期:
2023-03-15
发布日期:
2023-03-15
SI Weiwei, CEN Jian, WU Yinbo, HU Xueliang, HE Minzan, YANG Zhuohong, CHEN Honghua
Online:
2023-03-15
Published:
2023-03-15
摘要: 随着数据时代的来临,基于数据驱动的轴承故障诊断方法表现出了优越的性能,但是此类方法依赖大量标记数据,而在实际生产过程中很难收集到大量的数据,因此小样本的轴承故障诊断具有很高的研究价值。对小样本条件下的轴承故障诊断方法进行了回顾,并将其分为两类:基于数据的方法和基于模型的方法。其中基于数据的方法是从数据角度对原始样本进行扩充;基于模型的方法是指利用模型优化特征提取或者提高分类精度等。总结了当前小样本条件下故障诊断方法的不足,并展望了小样本轴承故障诊断的未来。
司伟伟, 岑健, 伍银波, 胡学良, 何敏赞, 杨卓洪, 陈红花. 小样本轴承故障诊断研究综述[J]. 计算机工程与应用, 2023, 59(6): 45-56.
SI Weiwei, CEN Jian, WU Yinbo, HU Xueliang, HE Minzan, YANG Zhuohong, CHEN Honghua. Review of Research on Bearing Fault Diagnosis with Small Samples[J]. Computer Engineering and Applications, 2023, 59(6): 45-56.
[1] 陈强强,戴邵武,戴洪德,等.滚动轴承故障诊断方法综述[J].仪表技术,2019(9):1-4. CHEN Q Q,DAI S W,DAI H D,et al.Review on fault diagnosis on the rolling bearing[J].Instrumentation Technology,2019(9):1-4. [2] LEI Y,YANG B,JIANG X,et al.Applications of machine learning to machine fault diagnosis:a review and roadmap[J].Mechanical Systems and Signal Processing,2020,138:106587. [3] CEN J,YANG Z,LIU X,et al.A review of data-driven machinery fault diagnosis using machine learning algorithms[J].Journal of Vibration Engineering & Technologies,2022:1-27. [4] 赵凯琳,靳小龙,王元卓.小样本学习研究综述[J].软件学报,2021,32(2):349-369. ZHAO K L,JIN X L,WANG Y Z.Survey on few-shot learning[J].Journal of Software,2021,32(2):349-369. [5] WANG Y,YAO Q,KWOK J T,et al.Generalizing from a few examples:a survey on few-shot learning[J].ACM Computing Surveys,2020,53(3):1-34. [6] 宋闯,赵佳佳,王康,等.面向智能感知的小样本学习研究综述[J].航空学报,2020,41(S1):15-28. SONG C,ZHAO J J,WANG K,et al.A survey of few shot learning based on intelligent perception[J],Acta Aeronautica et Astronautica Sinica,2020,41(S1):15-28. [7] SAUFI S R,AHMAD Z A B,LEONG M S,et al.Gearbox fault diagnosis using a deep learning model with limited data sample[J].IEEE Transactions on Industrial Informatics,2020,16(10):6263-6271. [8] YANG X,LIU B,XIANG L,et al.A novel intelligent fault diagnosis method of rolling bearings with small samples[J].Measurement,2022,203:111899. [9] PAN T,CHEN J,ZHANG T,et al.Generative adversarial network in mechanical fault diagnosis under small sample:a systematic review on applications and future perspectives[J].ISA Transactions,2021,128:1-10. [10] ZHANG T,CHEN J,LI F,et al.Intelligent fault diagnosis of machines with small & imbalanced data:a state-of-the-art review and possible extensions[J].ISA Transactions,2022,119:152-171. [11] CHAWLA N V,BOWYER K W,HALL L O,et al.SMOTE:synthetic minority over-sampling technique[J].Journal of Artificial Intelligence Research,2002(16):321-357. [12] 刘云鹏,和家慧,许自强,等.基于SVM SMOTE的电力变压器故障样本均衡化方法[J].高电压技术,2020,46(7):2522-2529. LIU Y P,HE J H,XU Z Q,et al.Equalization method of power transformer fault sample based on SVM SMOTE[J].High Voltage Engineering,2020,46(7):2522-2529. [13] GUAN H,ZHANG Y,XIAN M,et al.SMOTE-WENN:solving class imbalance and small sample problems by oversampling and distance scaling[J].Applied Intelligence,2021,51(3):1394-1409. [14] WEI J,HUANG H,YAO L,et al.New imbalanced fault diagnosis framework based on Cluster-MWMOTE and MFO-optimized LS-SVM using limited and complex bearing data[J].Engineering Applications of Artificial Intelligence,2020,96:103966. [15] CHEN L,DONG P,SU W,et al.Improving classification of imbalanced datasets based on KM++ smote algorithm[C]//2019 2nd International Conference on Safety Produce Informatization(IICSPI),2019:300-306. [16] WEI J,HUANG H,YAO L,et al.New imbalanced bearing fault diagnosis method based on sample-characteristic oversampling technique(SCOTE) and multi-class LS-SVM[J].Applied Soft Computing,2021,101:107043. [17] GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial nets[C]//Advances in Neural Information Processing Systems,2014. [18] 王坤峰,苟超,段艳杰,等.生成式对抗网络GAN的研究进展与展望[J].自动化学报,2017,43(3):321-332. WANG K F,GOU C,DUAN Y J,et al.Generative adversarial networks:the state of the art and beyond[J].Acta Automatica Sinica,2017,43(3):321-332. [19] MIRZA M,OSINDERO S.Conditional generative adversarial nets[J].arXiv:1411.1784,2014. [20] YU L,ZHANG W,WANG J,et al.Seqgan:sequence generative adversarial nets with policy gradient[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2017. [21] SPRINGENBERG J T,DOSOVITSKIY A,BROX T,et al.Striving for simplicity:the all convolutional net[J].arXiv:1412.6806,2014. [22] CHEN X,DUAN Y,HOUTHOOFT R,et al.InfoGAN:interpretable representation learning by information maximizing generative adversarial nets[C]//Advances in Neural Information Processing Systems,2016. [23] YANG J,LIU J,XIE J,et al.Conditional GAN and 2-D CNN for bearing fault diagnosis with small samples[J].IEEE Transactions on Instrumentation and Measurement,2021,70:1-12. [24] 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. [25] DIXIT S,VERMA N K,GHOSH A K.Intelligent fault diagnosis of rotary machines:conditional auxiliary classifier GAN coupled with meta learning using limited data[J].IEEE Transactions on Instrumentation and Measurement,2021,70:1-11. [26] KINGMA D P,WELLING M.Auto-encoding variational bayes[J].arXiv:1312.6114,2013. [27] 翟正利,梁振明,周炜,等.变分自编码器模型综述[J].计算机工程与应用,2019,55(3):1-9. ZHAI Z L,LIANG Z M,ZHOU W,et al.Research overview of variational auto-encoders models[J].Computer Engineering and Applications,2019,55(3):1-9. [28] BOURLARD H,KAMP Y.Auto-association by multilayer perceptrons and singular value decomposition[J].Biological Cybernetics,1988,59(4):291-294. [29] DIXIT S,VERMA N K.Intelligent condition-based monitoring of rotary machines with few samples[J].IEEE Sensors Journal,2020,20(23):14337-14346. [30] ZHAO D,LIU S,GU D,et al.Enhanced data-driven fault diagnosis for machines with small and unbalanced data based on variational auto-encoder[J].Measurement Science and Technology,2019,31(3):035004. [31] WANG Y,SUN G,JIN Q.Imbalanced sample fault diagnosis of rotating machinery using conditional variational auto-encoder generative adversarial network[J].Applied Soft Computing,2020,92:106333. [32] 佘博,田福庆,梁伟阁.基于深度卷积变分自编码网络的故障诊断方法[J].仪器仪表学报,2018,39(10):27-35. SHE B,TIAN F Q,LIANG W G.Fault diagnosis based on a deep convolution variational autoencoder network[J].Chinese Journal of Scientific Instrument,2018,39(10):27-35. [33] PAN S J,YANG Q.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering,2009,22(10):1345-1359. [34] DAI W,JIN O,XUE G R,et al.Eigentransfer:a unified framework for transfer learning[C]//Proceedings of the 26th Annual International Conference on Machine Learning,2009:193-200. [35] SHEN F,CHEN C,YAN R,et al.Bearing fault diagnosis based on SVD feature extraction and transfer learning classification[C]//2015 Prognostics and System Health Management Conference(PHM),2015:1-6. [36] 陈仁祥,陈思杨,杨黎霞,等.改进TrAdaBoost多分类模型的滚动轴承故障诊断[J].振动与冲击,2019,38(15):36-41. CHEN R X,CHEN S Y,YANG L X,et al.Fault diagnosis of rolling bearing based on improved TrAdaBoost multi-classification algorithm[J].Journal of Vibration and Shock,2019,15(38):36-41. [37] XIAO D,HUANG Y,QIN C,et al.Transfer learning with convolutional neural networks for small sample size problem in machinery fault diagnosis[J].Proceedings of the Institution of Mechanical Engineers,Part C:Journal of Mechanical Engineering Science,2019,233(14):5131-5143. [38] ZHANG J Q,KONG X W,LI X Y,et al.Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout[J].Chinese Journal of Aeronautics,2022(10):301-312. [39] 黄南天,杨学航,蔡国伟,等.采用非平衡小样本数据的风机主轴承故障深度对抗诊断[J].中国电机工程学报,2020,40(2):563-574. HUANG N T,YANG X H,CAI G W,et al.A deep adversarial diagnosis method for wind turbine main bearing fault with imbalanced small sample scenarios[J].Proceedings of the CSEE,2020,40(2):563-574. [40] ZHU D,SONG X,YANG J,et al.A bearing fault diagnosis method based on L1 regularization transfer learning and LSTM deep learning[C]//2021 IEEE International Conference on Information Communication and Software Engineering(ICICSE),2021:308-312. [41] 李凡长,刘洋,吴鹏翔,等.元学习研究综述[J].计算机学报,2021,44(2):422-446. LI F C,LIU Y,WU P X,et al.A survey on recent advance in meta-learning[J].Chinese Journal of Computers,2021,44(2):422-446. [42] FINN C,ABBEEL P,LEVINE S.Model-agnostic meta-learning for fast adaptation of deep networks[C]//International Conference on Machine Learning,2017:1126-1135. [43] KOCH G,ZEMEL R,SALAKHUTDINOV R.Siamese neural networks for one-shot image recognition[C]//ICML Deep Learning Workshop,2015. [44] LI Y,HU G,WANG Y,et al.Dada:differentiable automatic data augmentation[J].arXiv:2003.03780,2020. [45] ZOPH B,LE Q V.Neural architecture search with reinforcement learning[J].arXiv:1611.01578,2016. [46] FENG Y,CHEN J,XIE J,et al.Meta-learning as a promising approach for few-shot cross-domain fault diagnosis:algorithms,applications,and prospects[J].Knowledge-Based Systems,2022,235:107646. [47] HU Y,LIU R,LI X,et al.Task-sequencing meta learning for intelligent few-shot fault diagnosis with limited data[J].IEEE Transactions on Industrial Informatics,2021,18(6):3894-3904. [48] 赵晓平,彭澎,张永宏,等.改进孪生网络在小样本轴承故障诊断中的应用[J/OL].计算机工程与应用:1-12[2022-07-17].http://kns.cnki.net/kcms/detail/11.2127.TP.20220705.1850. 014.html. ZHAO X P,PENG P,ZHANG Y H,et al.Application of Improved siamese neural Network in small sample fault diagnosis of bearing[J/OL].Computer Engineering and Applications:1-12[2022-07-17].http://kns.cnki.net/kcms/detail/11. 2127.TP.20220705.1850.014.html. [49] PAN S J,TSANG I W,KWOK J T,et al.Domain adaptation via transfer component analysis[J].IEEE Transactions on Neural Networks,2010,22(2):199-210. [50] LONG M,WANG J,DING G,et al.Transfer feature learning with joint distribution adaptation[C]//Proceedings of the IEEE International Conference on Computer Vision,2013:2200-2207. [51] LIU J,REN Y.A general transfer framework based on industrial process fault diagnosis under small samples[J].IEEE Transactions on Industrial Informatics,2020,17(9):6073-6083. [52] HAN T,LIU C,YANG W,et al.Deep transfer network with joint distribution adaptation:a new intelligent fault diagnosis framework for industry application[J].ISA Transactions,2020,97:269-281. [53] LU N,HU H,YIN T,et al.Transfer relation network for fault diagnosis of rotating machinery with small data[J].IEEE Transactions on Cybernetics,2022,52(11):11927-11941. [54] XI P P,ZHAO Y P,WANG P X,et al.Least squares support vector machine for class imbalance learning and their applications to fault detection of aircraft engine[J].Aerospace Science and Technology,2019,84:56-74. [55] WU Z,ZHANG H,GUO J,et al.Imbalanced bearing fault diagnosis under variant working conditions using cost-sensitive deep domain adaptation network[J].Expert Systems with Applications,2022,193:116459. [56] VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Advances in Neural Information Processing Systems,2017. [57] WANG H,LIU Z,PENG D,et al.Understanding and learning discriminant features based on multiattention 1DCNN for wheelset bearing fault diagnosis[J].IEEE Transactions on Industrial Informatics,2019,16(9):5735-5745. [58] YANG Z,CEN J,LIU X,et al.Research on bearing fault diagnosis method based on transformer neural network[J].Measurement Science and Technology,2022,38(3):085111. [59] DING Y,JIA M,MIAO Q,et al.A novel time-frequency transformer based on self-attention mechanism and its application in fault diagnosis of rolling bearings[J].Mechani- cal Systems and Signal Processing,2022,168:108616. [60] WANG R,CHEN Z,ZHANG S,et al.Dual-attention generative adversarial networks for fault diagnosis under the class-imbalanced conditions[J].IEEE Sensors Journal,2021,22(2):1474-1485. [61] ZHAO J,LIN X,ZHOU J,et al.Knowledge-based fine-grained classification for few-shot learning[C]//2020 IEEE International Conference on Multimedia and Expo(ICME),2020:1-6. [62] WU Z,PAN S,CHEN F,et al.A comprehensive survey on graph neural networks[J].IEEE Transactions on Neural Networks and Learning Systems,2020,32(1):4-24. [63] LI T,ZHOU Z,LI S,et al.The emerging graph neural networks for intelligent fault diagnostics and prognostics:a guideline and a benchmark study[J].Mechanical Systems and Signal Processing,2022,168:108653. [64] CHEN D,LIU R,HU Q,et al.Interaction-aware graph neural networks for fault diagnosis of complex industrial processes[J].IEEE Transactions on Neural Networks and Learning Systems,2021:1-14. |
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