Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (15): 42-54.DOI: 10.3778/j.issn.1002-8331.2401-0112
• Research Hotspots and Reviews • Previous Articles Next Articles
LIU Junfu, CEN Jian, HUANG Hankun, LIU Xi, ZHAO Bichuang, SI Weiwei
Online:
2024-08-01
Published:
2024-07-30
刘俊孚,岑健,黄汉坤,刘溪,赵必创,司伟伟
LIU Junfu, CEN Jian, HUANG Hankun, LIU Xi, ZHAO Bichuang, SI Weiwei. Review on Zero or Few Sample Rotating Machinery Fault Diagnosis[J]. Computer Engineering and Applications, 2024, 60(15): 42-54.
刘俊孚, 岑健, 黄汉坤, 刘溪, 赵必创, 司伟伟. 零小样本旋转机械故障诊断综述[J]. 计算机工程与应用, 2024, 60(15): 42-54.
Add to citation manager EndNote|Ris|BibTeX
URL: http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2401-0112
[1] RANE N. YOLO and faster R-CNN object detection for smart industry 4.0 and industry 5.0: applications, challenges, and opportunities[J]. Available at SSRN 4624206, 2023. [2] ZHANG K, XU Y, LIAO Z, et al. A novel fast entrogram and its applications in rolling bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2021, 154: 107582. [3] 薄翠梅, 韩晓春, 易辉, 等.基于聚类选择[k]近邻的LLE算法及故障检测[J].化工学报, 2016, 67(3): 925-930. BO C M, HAN X C, YI H, et al. Neighborhood selection of LLE based on cluster for fault detection[J]. CIESC Journal, 2016, 67(3): 925-930. [4] ROGERS A P, GUO F, RASMUSSEN B P. A review of fault detection and diagnosis dethods for residential air conditioning systems[J]. Building and Environment, 2019, 161: 106236. [5] MIRNAGHI M S, HAGHIGHAT F. Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: a comprehensive review[J]. Energy and Buildings, 2020, 229: 110492. [6] LIU Q, ZHU Q, QIN S J, et al. Dynamic concurrent kernel CCA for Strip-thickness relevant fault diagnosis of continuous snnealing processes[J]. Journal of Process Control, 2018, 67: 12-22. [7] GAO X, HOU J. An improved SVM integrated GS-PCA fault diagnosis spproach of tennessee eastman process[J]. Neurocomputing, 2016, 174: 906-911. [8] DENG X, TIAN X, HU X. Nonlinear process fault fiagnosis based on slow feature analysis[C]//Proceedings of the 10th World Congress on Intelligent Control and Automation, 2012: 3152-3156. [9] CHEN W, QIU Y, FENG Y, et al. Diagnosis of wind turbine faults with transfer learning algorithms[J]. Renewable Energy, 2021, 163: 2053-2067. [10] 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. [11] HYERS R W, MCGOWAN J G, SULLIVAN K L, et al. Condition monitoring and prognosis of utility scale wind turbines[J]. Energy Materials, 2006, 1(3): 187-203. [12] PORTOS J, GARNER K D, PARKER B, et al. Most common mechanisms and reasons for electric motor failures in petrochemical industry[C]//Proceedings of the 2015 IEEE Petroleum and Chemical Industry Committee Conference (PCIC), 2015: 1-11. [13] GARCIA M, PANAGIOTOU P A, ANTONINO-DAVIU J A, et al. Efficiency assessment of induction motors operating under different faulty conditions[J]. IEEE Transactions on Industrial Electronics, 2018, 66(10): 8072-8081. [14] LIU Z, ZHANG L. A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings[J]. Measurement, 2020, 149: 107002. [15] XIAN Y, SCHIELE B, AKATA Z. Zero-shot learning—the good, the bad and the ugly[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4582-4591. [16] SUN X, GU J, SUN H. Research progress of zero-shot learning[J]. Applied Intelligence, 2021, 51: 3600-3614. [17] POURPANAH F, ABDAR M, LUO Y, et al. A review of generalized zero-shot learning methods[J]. IEEE Transactions on Pattern Snalysis and Machine Intelligence, 2023, 45(4): 4051-4070. [18] SOYSAL O A, GUZEL M S. An introduction to zero-shot learning: an essential review[C]//Proceedings of the 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2020: 1-4. [19] FENG L, ZHAO J, ZHAO C. A systematic evaluation and benchmark for embedding-aware generative models: features, models, and any-shot scenarios[J]. arXiv: 2302.04060, 2023. [20] LU J, GONG P, YE J, et al. Learning from very few samples: a survey[J]. arXiv: 2009.02653, 2020. [21] CHEN W Y, LIU Y C, KIRA Z, et al. A closer look at few-shot classification[J]. arXiv: 1904.04232, 2019. [22] WANG Y, YAO Q, KWOK J T, et al. Generalizing from a few examples: a survey on few-shot learning[J]. ACM Computing Surveys (CSUR), 2020, 53(3): 1-34. [23] 司伟伟, 岑健, 伍银波, 等.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 W W, CEN J, WU Y B, et al. Review of research on bearing fault diagnosis with small samples[J]. Computer Engineering and Applications, 2023, 59(6): 45-56. [24] FEI-FEI L, FERGUS R, PERONA P. One-shot learning of object categories[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 594-611. [25] ROMERA-PAREDES B, TORR P. An embarrassingly simple approach to zero-shot learning[C]//Proceedings of the International Conference on Machine Learning, 2015: 2152-2161. [26] LONGADGE R, DONGRE S. Class imbalance problem in data mining review[J]. arXiv: 1305.1707, 2013. [27] YAGUO L E I, FENG J I A, DETONG K, et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of Mechanical Engineering, 2018, 54(5): 94-104. [28] SU H, XIANG L, HU A, et al. A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions[J]. Mechanical Systems and Signal Processing, 2022, 169: 108765. [29] DING Y, MA L, MA J, et al. A generative adversarial network-based intelligent fault diagnosis method for rotating machinery under small sample size conditions[J]. IEEE Access, 2019, 7: 149736-149749. [30] 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. [31] MENG Z, LI Q, SUN D, et al. An intelligent fault diagnosis method of small sample bearing based on improved auxiliary classification generative adversarial network[J]. IEEE Sensors Journal, 2022, 22(20): 19543-19555. [32] ZHAO X, MA M, SHAO F. Bearing fault diagnosis method based on improved siamese neural network with small sample[J]. Journal of Cloud Computing, 2022, 11(1): 1-17. [33] VINYALS O, TOSHEV A, BENGIO S, et al. Show and tell: a neural image caption generator[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3156-3164. [34] REN Z, LIN T, FENG K, et al. A systematic review on imbalanced learning methods in intelligent fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-35. [35] VILALTA R, DRISSI Y. A perspective view and survey of meta-learning[J]. Artificial Intelligence Review, 2002, 18: 77-95. [36] LAMPERT C H, NICKISCH H, HARMELING S. Learning to detect unseen object classes by between-class attribute transfer[C]//Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009: 951-958. [37] ZHUO Y, GE Z. Auxiliary information-guided industrial data augmentation for any-shot fault learning and diagnosis[J]. IEEE Transactions on Industrial Informatics, 2021, 17(11): 7535-7545. [38] LV H, CHEN J, PAN T, et al. Hybrid attribute conditional adversarial denoising autoencoder for zero-shot classification of mechanical intelligent fault diagnosis[J]. Applied Soft Computing, 2020, 95: 106577. [39] XU J, ZHOU L, ZHAO W, et al. Zero-shot learning for compound fault diagnosis of bearings[J]. Expert Systems with Applications, 2022, 190: 116197. [40] CHEN G, LIU M, CHEN J. Frequency-temporal-logic-based bearing fault diagnosis and fault interpretation using Bayesian optimization with bayesian neural networks[J]. Mechanical Systems and Signal Processing, 2020, 145: 106951. [41] FENG L, ZHAO C. Transfer increment for generalized zero-shot learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 32(6): 2506-2520. [42] XIAN Y, LAMPERT C H, SCHIELE B, et al. Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 41(9): 2251-2265. [43] BENGIO Y, DUCHARME R, VINCENT P. A neural probabilistic language model[C]//Advances in Neural Information Processing Systems, 2000. [44] ZHANG S, WEI H L, DING J. An effective zero-shot learning approach for intelligent fault detection using 1D CNN[J]. Applied Intelligence, 2023, 53(12): 16041-16058. [45] LU N, ZHUANG G, MA Z, et al. A zero-shot intelligent fault diagnosis system based on EEMD[J]. IEEE Access, 2022, 10: 54197-54207. [46] QIAO R, LIU L, SHEN C, et al. Less is more: zero-shot learning from online textual documents with noise suppression[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2249-2257. [47] BUCHER M, HERBIN S, JURIE F. Improving semantic embedding consistency by metric learning for zero-shot classiffication[C]//Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, October 11-14, 2016: 730-746. [48] SHIGETO Y, SUZUKI I, HARA K, et al. Ridge regression, hubness, and zero-shot learning[C]//Proceedings of the Joint European Conference on Machinelearning and Knowledge Discovery in Databases, 2015: 135-151. [49] AKATA Z, PERRONNIN F, HARCHAOUI Z, et al. Label-embedding for attribute-based classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013: 819-826. [50] FU Y, HOSPEDALES T M, XIANG T, et al. Transductive multi-view embedding for zero-shot recognition and annotation[C]//Proceedings of the 13th European Conference on Computer Vision, Zurich, Switzerland, September 6-12, 2014: 584-599. [51] AKATA Z, REED S, WALTER D, et al. Evaluation of output embeddings for fine-grained image classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 2927-2936. [52] LEI BA J, SWERSKY K, FIDLER S. Predicting deep zero-shot convolutional neural networks using textual descriptions[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 4247-4255. [53] SUN X, GU J, WANG M, et al. Wheel hub defects image recognition base on zero-shot learning[J]. Applied Sciences, 2021, 11(4): 1529. [54] BUCHER M, HERBIN S, JURIE F. Generating visual representations for zero-zhot classification[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017: 2666-2673. [55] ALI F, IAN E, DEREK H, DAVID F. Describing objects by their attributes[C]//Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2009 : 1778-1785. [56] CHRISTOPH H L, HANNES N, STEFAN H. Attribute-based classification for zero-shot visual object categorization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(3): 453-465. [57] PATTERSON G, HAYS J. Sun atribute database: discovering, annotating, and recognizing scene attributes[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012: 2751-2758. [58] CATHERINE W, STEVE B, PETER W, et al.The Caltech-Ucsd Birds-200-2011 Dataset[Z].2011. [59] ZHANG L, XIANG T, GONG S. Learning a deep embedding model for zero-shot learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2021-2030. [60] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Advances In Neural Information Processing Systems, 2014. [61] KINGMA D P, WELLING M. Auto-encoding variational Bayes[J]. arXiv: 1312.6114, 2013. [62] XU J, LI K, FAN Y, et al. A label information vector generative zero-shot model for the diagnosis of compound faults[J]. Expert Systems with Applications, 2023, 233: 120875. [63] SARIYILDIZ M B, CINBIS R G. Gradient matching generative networks for zero-shot learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 2168-2178. [64] GORI M, MONFARDINI G, SCARSELLI F. A new model for learning in graph domains[C]//Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, 2005: 729-734. [65] LI X, MA J, YU J, et al. A structure-enhanced generative adversarial network for knowledge graph zero-shot relational learning[J]. Information Sciences, 2023, 629: 169-183. [66] SOHN K, LEE H, YAN X. Learning structured output representation using deep conditional generative models[C]//Advances in Neural Information Processing Systems, 2015. [67] MIRZA M, OSINDERO S. Conditional generative adversarial nets[J]. arXiv: 1411.1784, 2014. [68] GULL M, ARIF O. Generating visual representations for zero-shot learning via adversarial learning and variational autoencoders[J]. International Journal of General Systems, 2023, 52(5): 636-651. [69] WU L, WU C, GUO H, et al. A cross-modal alignment for zero-shot image classification[J]. IEEE Access, 2023, 11: 9067-9073. [70] LI X, ZHANG D, YE M, et al. Bidirectional generative transductive zero-shot learning[J]. Neural Computing and Applications, 2021, 33: 5313-5326. [71] GUPTA A, NARAYAN S, KHAN S, et al. Generative multi-label zero-shot learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(12): 14611-14624. [72] LOPEZ R, REGIER J, JORDAN M I, et al. Information constraints on auto-encoding variational Bayes[C]//Advances in Neural Information Processing Systems, 2018. [73] BAO J, CHEN D, WEN F, et al. CVAE-GAN: fine-grained image generation through asymmetric training[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2745-2754. [74] WANG T, WAN X. T-CVAE: transformer-based conditioned variational autoencoder for story completion[C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019: 5233-5239. [75] QIAN J, ELSHERIEF M, BELDING E, et al. Hierarchical CVAE for fine-grained hate speech classification[J]. arXiv: 1809.00088, 2018. [76] BIAN J, HUI X, SUN S, et al. A novel and efficient CVAE-GAN-based approach with informative manifold for semi-supervised anomaly detection[J]. IEEE Access, 2019, 7: 88903-88916. [77] SOCHER R, GANJOO M, MANNING C D, et al. Zero-shot learning through cross-modal transfer[C]//Advances in Neural Information Processing Systems, 2013. [78] XU J, ZHANG H, ZHOU L, et al. Zero-shot compound fault diagnosis method based on semantic learning and discriminative features[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-13. [79] LOTFOLLAHI M, NAGHIPOURFAR M, THEIS F J, et al. Conditional out-of-distribution generation for unpaired data using transfer VAE[J]. Bioinformatics, 2020, 36(S2): 610-617. [80] SOHL-DICKSTEIN J, WEISS E, MAHESWARANATHAN N, et al. Deep unsupervised learning using nonequilibrium thermodynamics[C]//Proceedings of the International Conference on Machine Learning, 2015: 2256-2265. [81] ROMBACH R, BLATTMANN A, LORENZ D, et al. High-resolution image synthesis with latent diffusion models[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 10684-10695. [82] LUO H, QIU H, GHANIME G, et al. Synthesized synchronous sampling technique for differential bearing damage detection[J]. Journal of Engineering for Gas Turbines and Power-Transactions of the Asme 2010, 132(7): 072501. [83] FARAJZADEH-ZANJANI M, RAZAVI-FAR R, SAIF M. Efficient sampling techniques for ensemble learning and diagnosing bearing defects under class imbalanced condition[C]//Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016: 1-7. [84] CEN J, YANG Z H, WUY B, et al.A mask self-supervised learning-based transformer for bearing fault diagnosis with limited labeled samples[J]. IEEE Sensors Journal, 2023, 23(10): 10359-10369. [85] 符杨, 周全, 贾锋, 等.基于SCADA数据图形化的海上风电机组故障预测[J].中国电机工程学报, 2022, 42(20): 7465-7475. FU Y, ZHOU Q, JIA F, et al. Fault prediction of offshore wind turbines based on graphical processing of SCADA data[J]. Proceedings of the CSEE, 2022, 42(20): 7465-7475. [86] CHEN Y, PENG G, XIE C, et al. ACDIN: bridging the gap between artificial and real bearing damages for bearing fault diagnosis[J]. Neurocomputing, 2018, 294: 61-71. [87] KIRANYAZ S, DEVECIOGLU O C, ALHAMS A, et al. Zero-shot motor health monitoring by blind domain transition[J]. Mechanical Systems and Signal Processing, 2024, 210: 111147. [88] CHEN Z, WU J, DENG C, et al. Deep attention relation network: a zero-shot learning method for bearing fault diagnosis under unknown domains[J]. IEEE Transactions on Reliability, 2022, 72(1): 79-89. [89] CHENG L, AN Z, GUO Y, et al. MMFSL: a novel multi-modal few-shot learning framework for fault diagnosis of industrial bearings[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-13. |
[1] | 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. |
[2] | LI Shasha, SHI Jie. Research on Deep Learning Method for Induction Motor Fault Diagnosis [J]. Computer Engineering and Applications, 2024, 60(14): 329-336. |
[3] | XIE Fengyun, LI Gang, WANG Linglan, LIU Hui, WANG Gan. Gearbox Fault Diagnosis Based on Improved Time Series Gray Scale Image and Deep Learning [J]. Computer Engineering and Applications, 2024, 60(13): 338-344. |
[4] | QIU Ling, ZHANG Ansi, ZHANG Yu, LI Shaobo, LI Chuanjiang, YANG Lei. Application Method of Knowledge Graph Construction for UAV Fault Diagnosis [J]. Computer Engineering and Applications, 2023, 59(9): 280-288. |
[5] | ZHOU Yurong, ZHANG Qiaoling, YU Guangzeng, XU Weiqiang. Review of Acoustic Signal-Based Industrial Equipment Fault Diagnosis [J]. Computer Engineering and Applications, 2023, 59(7): 51-63. |
[6] | 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. |
[7] | AN Xue, LI Shaobo, ZHANG Yizong, ZHANG Ansi. Review of Fault Diagnosis Techniques for UAV Flight Control Systems [J]. Computer Engineering and Applications, 2023, 59(24): 1-15. |
[8] | ZHAO Xiaoping, PENG Peng, ZHANG Yonghong, ZHANG Zhongyang. Application of Improved Siamese Neural Network in Small Sample Fault Diagnosis of Bearing [J]. Computer Engineering and Applications, 2023, 59(19): 294-304. |
[9] | DENG Jianfeng, WANG Tao, CHENG Lianglun. Research on Construction of Event Logic Knowledge Graph of Robot Fault Diagnosis [J]. Computer Engineering and Applications, 2023, 59(13): 139-148. |
[10] | WANG Tingxuan, LIU Tao, WANG Zhenya, PU Huijie. Applied Research on Bearing Fault Diagnosis Based on Knowledge Distillation and Transfer Learning [J]. Computer Engineering and Applications, 2023, 59(13): 289-297. |
[11] | CHI Yi, CHEN Guangwu. Real-Time Turnout Fault Diagnosis Based on One-Dimensional Convolutional Neural Network [J]. Computer Engineering and Applications, 2022, 58(20): 293-299. |
[12] | TAO Qisheng, PENG Cheng, MAN Junfeng, LIU Yi. Two-Step Transfer Learning Method for Bearing Fault Diagnosis [J]. Computer Engineering and Applications, 2022, 58(2): 303-312. |
[13] | HU Chunsheng, LI Guoli, ZHAO Yong, CHENG Fangjuan. Summary of Fault Diagnosis Methods for Rolling Bearings Under Variable Working Conditions [J]. Computer Engineering and Applications, 2022, 58(18): 26-42. |
[14] | CHEN Honghua, CEN Jian, LIU Xi, YANG Zhuohong. Research Progress of Deep Learning in Fault Diagnosis of Chemical Process Industry [J]. Computer Engineering and Applications, 2022, 58(13): 48-62. |
[15] | QIU Yingyu, ZHANG Ke, YANG Xinyi. Deep Manifold Transfer Learning Method for Fault Diagnosis of Rotating Machinery Under Different Working Conditions [J]. Computer Engineering and Applications, 2022, 58(12): 289-298. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||