计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 93-102.doi: 10.11896/jsjkx.220500197
黄迅迪, 庞雄文
HUANG Xundi, PANG Xiongwen
摘要: 智能设备故障诊断技术(Intelligent Fault Diagnosis,IFD)将深度学习理论应用于设备故障诊断,能自动识别设备的健康状态和故障类型,在设备故障诊断领域引起了广泛关注。智能设备故障诊断通过构建端到端的AI模型和算法将设备监测数据与机器健康状态关联以实现设备故障诊断,但设备故障诊断的模型和算法较多且相互之间并不通用,采用与监测数据不相符的模型进行故障诊断会导致诊断准确率大幅度下滑。为解决这一问题,在全面调查设备故障诊断相关文献的基础上,首先简述深度设备故障诊断的模型框架,再根据具体应用场景和设备监测数据类型对模型算法进行分类介绍、列表对比及总结,最后针对存在的问题分析了未来的发展方向。本综述有望为智能设备故障诊断的研究提供有益的参考。
中图分类号:
[1]ZHANG S,ZHANG S B,WANG B N,et al.Deep learning algorithms for bearing fault diagnostics-A comprehensive review[J].IEEE Access,2020,8:29857-29881. [2]LEI Y,YANG B,JIANG X,et al.Applications of machine lear-ning to machine fault diagnosis:A review and roadmap[J].Mechanical Systems and Signal Processing,2020,138:106587. [3]RUMELHART D E,HINTON G E,WILLIAMS R J.Learning representations by back-propagating errors[J].Nature,1986,323(6088):533-536. [4]HINTON G E.Training products of experts by minimizing con-trastive divergence[J].Neural Computation,2002,14(8):1771-1800. [5]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenet classification with deep convolutional neural networks[J/OL].https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf. [6]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [7]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Ge-nerative adversarial nets[J/OL].https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf. [8]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 Proces-sing,2022,168:108653. [9]PAN S J,YANG Q.A survey on transfer learning[J].IEEE Transactions on knowledge and data engineering,2009,22(10):1345-1359. [10]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[J/OL].https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf. [11]BAI Y,YANG J,WANG J,et al.Image representation of vibration signals and its application in intelligent compound fault diag-nosis in railway vehicle wheelset-axlebox assemblies[J].Mechanical Systems and Signal Processing,2021,152:107421. [12]KIM Y,NA K,YOUN B D.A health-adaptive time-scale representation(HTSR) embedded convolutional neural network for gearbox fault diagnostics[J].Mechanical Systems and Signal Processing,2022,167:108575. [13]SHI J,PENG D,PENG Z,et al.Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks[J].Mechanical Systems and Signal Processing,2022,162:107996. [14]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].Mechanical Systems and Signal Processing,2022,168:108616. [15]XU Y,LI Z,WANG S,et al.A hybrid deep-learning model for fault diagnosis of rolling bearings[J].Measurement,2021,169:108502. [16]ZHAO M,ZHONG S,FU X,et al.Deep residual networks with adaptively parametric rectifier linear units for fault diagnosis[J].IEEE Transactions on Industrial Electronics,2020,68(3):2587-2597. [17]LIU S,JIANG H,WU Z,et al.Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis[J].Mechanical Systems and Signal Processing,2022,163:108139. [18]LIU J,ZHANG C,JIANG X.Imbalanced fault diagnosis of rol-ling bearing using improved MsR-GAN and feature enhancement-driven CapsNet[J].Mechanical Systems and Signal Processing,2022,168:108664. [19]PAN T,CHEN J,XIE J,et al.Deep feature generating network:A new method for intelligent fault detection of mechanical systems under class imbalance[J].IEEE Transactions on Industrial Informatics,2020,17(9):6282-6293. [20]WU X,ZHANG Y,CHENG C,et al.A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machi-nery[J].Mechanical Systems and Signal Processing,2021,149:107327. [21]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. [22]ZHANG K,TANG B,DENG L,et al.A fault diagnosis method for wind turbines gearbox based on adaptive loss weighted meta-ResNet under noisy labels[J].Mechanical Systems and Signal Processing,2021,161:107963. [23]YU X,TANG B,ZHANG K.Fault diagnosis of wind turbinegearbox using a novel method of fast deep graph convolutional networks[J].IEEE Transactions on Instrumentation and Mea-surement,2021,70:1-14. [24]ZHANG D,STEWART E,ENTEZAMI M,et al.Intelligentacoustic-based fault diagnosis of roller bearings using a deep graph convolutional network[J].Measurement,2020,156:107585. [25]LI C,MO L,YAN R.Rolling bearing fault diagnosis based on horizontal visibility graph and graph neural networks[C]//2020 International Conference on Sensing,Measurement & Data Analytics in the Era of Artificial Intelligence(ICSMD).IEEE,2020:275-279. [26]YANG C,ZHOU K,LIU J.SuperGraph:Spatial-temporalgraph-based feature extraction for rotating machinery diagnosis[J].IEEE Transactions on Industrial Electronics,2021,69(4):4167-4176. [27]LI T,ZHAO Z,SUN C,et al.Multireceptive field graph convolutional networks for machine fault diagnosis[J].IEEE Transactions on Industrial Electronics,2020,68(12):12739-12749. [28]ZHAO X,JIA M,LIU Z.Semisupervised graph convolution deep belief network for fault diagnosis of electormechanical system with limited labeled data[J].IEEE Transactions on Industrial Informatics,2020,17(8):5450-5460. [29]LI Q,SHEN C,CHEN L,et al.Knowledge mapping-based adversarial domain adaptation:A novel fault diagnosis method with high generalizability under variable working conditions[J].Mechanical Systems and Signal Processing,2021,147:107095. [30]XIA Y,SHEN C,WANG D,et al.Moment matching-based intra-class multisource domain adaptation network for bearing fault diagnosis[J].Mechanical Systems and Signal Processing,2022,168:108697. [31]SHI Y,DENG A,DING X,et al.Multisource domain factorization network for cross-domain fault diagnosis of rotating machinery:An unsupervised multisource domain adaptation method[J].Mechanical Systems and Signal Processing,2022,164:108219. [32]ZHU J,CHEN N,SHEN C.A new multiple source domain ada-ptation fault diagnosis method between different rotating machines[J].IEEE Transactions on Industrial Informatics,2020,17(7):4788-4797. [33]XIA P,HUANG Y,LI P,et al.Fault Knowledge Transfer Assisted Ensemble Method for Remaining Useful Life Prediction[J].IEEE Transactions on Industrial Informatics,2021,18(3):1758-1769. [34]QIN Y,YAO Q,WANG Y,et al.Parameter sharing adversarial domain adaptation networks for fault transfer diagnosis of plane-tary gearboxes[J].Mechanical Systems and Signal Processing,2021,160:107936. [35]LEI Z,WEN G,DONG S,et al.An intelligent fault diagnosis method based on domain adaptation and its application for bea-rings under polytropic working conditions[J].IEEE Transactions on Instrumentation and Measurement,2020,70:1-14. [36]LI Y,SONG Y,JIA L,et al.Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learning[J].IEEE Transactions on Industrial Informatics,2020,17(4):2833-2841. [37]LI W,CHEN Z,HE G.A novel weighted adversarial transfer network for partial domain fault diagnosis of machinery[J].IEEE Transactions on Industrial Informatics,2020,17(3):1753-1762. [38]JIAO J,ZHAO M,LIN J.Multi-Weight Domain AdversarialNetwork for Partial-Set Transfer Diagnosis[J].IEEE Transactions on Industrial Electronics,2021,69(4):4275-4284. [39]YANG B,XU S,LEI Y,et al.Multi-source transfer learning network to complement knowledge for intelligent diagnosis of machines with unseen faults[J].Mechanical Systems and Signal Processing,2022,162:108095. [40]FENG L,ZHAO C.Fault description based attribute transfer for zero-sample industrial fault diagnosis[J].IEEE Transactions on Industrial Informatics,2020,17(3):1852-1862. |
|