RESEARCH ARTICLE

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

  • Jie LIU 1 ,
  • Kaibo ZHOU 2 ,
  • Chaoying YANG , 2 ,
  • Guoliang LU 3
Expand
  • 1. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 2. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
  • 3. School of Mechanical Engineering, Shandong University, Jinan 250061, China

Received date: 26 Mar 2021

Accepted date: 20 Jul 2021

Published date: 15 Dec 2021

Copyright

2021 Higher Education Press 2021.

Abstract

Existing fault diagnosis methods usually assume that there are balanced training data for every machine health state. However, the collection of fault signals is very difficult and expensive, resulting in the problem of imbalanced training dataset. It will degrade the performance of fault diagnosis methods significantly. To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning is proposed in this paper. Unsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector as the node attribute in the SuperGraph. And the edge connections in the graph depend on the relationship between signals. On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced training dataset fault diagnosis for rotating machinery. Comprehensive experiments are conducted on a benchmarking publicized dataset and a practical experimental platform, and the results show that the proposed method can effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature learning.

Cite this article

Jie LIU , Kaibo ZHOU , Chaoying YANG , Guoliang LU . Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning[J]. Frontiers of Mechanical Engineering, 2021 , 16(4) : 829 -839 . DOI: 10.1007/s11465-021-0652-4

Acknowledgement

This work was supported by the National Key R&D Program of China (Grant No. 2020YFB1711203).
1
Shao H, Jiang H, Wang F. An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowledge-Based Systems, 2017, 119 : 200– 220

DOI

2
Han T, Liu C, Wu L. An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems. Mechanical Systems and Signal Processing, 2019, 117 : 170– 187

DOI

3
Lei Y, Jia F, Lin J. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical Big Data. IEEE Transactions on Industrial Electronics, 2016, 63( 5): 3137– 3147

DOI

4
Chen Z, Mauricio A, Li W. A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks. Mechanical Systems and Signal Processing, 2020, 140 : 106683–

DOI

5
Han T, Li Y, Qian M. A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions. IEEE Transactions on Instrumentation and Measurement, 2021, 70 : 1– 11

DOI

6
Guo X, Chen L, Shen C. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement, 2016, 93 : 490– 502

DOI

7
Ren H, Liu W, Shan M. A new wind turbine health condition monitoring method based on VMD-MPE and feature-based transfer learning. Measurement, 2019, 148 : 106906–

DOI

8
Razavi-Far R, Hallaji E, Farajzadeh-Zanjani M. A semi-supervised diagnostic framework based on the surface estimation of faulty distributions. IEEE Transactions on Industrial Informatics, 2019, 15( 3): 1277– 1286

DOI

9
Chen F, Tang B, Chen R. A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm. Measurement, 2013, 46( 1): 220– 232

DOI

10
Chen Z, Mauricio A, Li W. A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks. Mechanical Systems and Signal Processing, 2020, 140 : 106683–

DOI

11
Mao W, He L, Yan Y. Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine. Mechanical Systems and Signal Processing, 2017, 83 : 450– 473

DOI

12
Dai X, Gao Z. From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis. IEEE Transactions on Industrial Informatics, 2013, 9( 4): 2226– 2238

DOI

13
Zhang Y, Li X, Gao L. Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning. Journal of Manufacturing Systems, 2018, 48 : 34– 50

DOI

14
Barandela R, Valdovinos R M, Sánchez J S, et al. The imbalanced training sample problem: Under or over sampling? In: Fred A, Caelli T M, Duin R P W, et al., eds. Structural, Syntactic, and Statistical Pattern Recognition. Berlin: Springer, 2004, 806–814

15
Guo L, Lei Y, Xing S. Deep convolutional transfer learning network: a new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Transactions on Industrial Electronics, 2019, 66( 9): 7316– 7325

DOI

16
Yang B, Lei Y, Jia F. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mechanical Systems and Signal Processing, 2019, 122 : 692– 706

DOI

17
Li X, Zhang W, Ding Q. A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning. Neurocomputing, 2018, 310 : 77– 95

DOI

18
Mariani G, Scheidegger F, Istrate R, et al. BAGAN: Data Augmentation with Balancing GAN. 2018, arXiv:1803.09655

19
Zhang W, Li X, Jia X. Machinery fault diagnosis with imbalanced data using deep generative adversarial networks. Measurement, 2020, 152 : 107377–

DOI

20
Gao X, Deng F, Yue X. Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty. Neurocomputing, 2020, 396 : 487– 494

DOI

21
Zhao X, Jia M, Lin M. Deep Laplacian auto-encoder and its application into imbalanced fault diagnosis of rotating machinery. Measurement, 2020, 152 : 107320–

DOI

22
Jia F, Lei Y, Lu N. Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization. Mechanical Systems and Signal Processing, 2018, 110 : 349– 367

DOI

23
Wang T, Liu Z, Lu G. Temporal-spatio graph based spectrum analysis for bearing fault detection and diagnosis. IEEE Transactions on Industrial Electronics, 2021, 68( 3): 2598– 2607

DOI

24
Lo C H, Wong Y K, Rad A B. Fusion of qualitative bond graph and genetic algorithms: a fault diagnosis application. ISA Transactions, 2002, 41( 4): 445– 456

DOI

25
Wang T, Lu G, Yan P. A novel statistical time-frequency analysis for rotating machine condition monitoring. IEEE Transactions on Industrial Electronics, 2020, 67( 1): 531– 541

DOI

26
Gao Y, Yu D. Total variation on horizontal visibility graph and its application to rolling bearing fault diagnosis. Mechanism and Machine Theory, 2020, 147 : 103768–

DOI

27
Yang C, Zhou K, Liu J. SuperGraph: Spatial-temporal graph-based feature extraction for rotating machinery diagnosis. IEEE Transactions on Industrial Electronics, 2021 (in press)

28
Zhang D, Stewart E, Entezami M. Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network. Measurement, 2020, 156 : 107585–

DOI

29
Wang S, Xing S, Lei Y. Vibration indicator-based graph convolutional network for semi-supervised bearing fault diagnosis. IOP Conference Series. Materials Science and Engineering, 2021, 1043( 5): 052026–

DOI

30
Wang Y, Gao L, Gao Y. A new graph-based semi-supervised method for surface defect classification. Robotics and Computer-Integrated Manufacturing, 2021, 68 : 102083–

DOI

31
Liu J, Hu Y, Wang Y. An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis. Measurement Science & Technology, 2018, 29( 5): 055103–

DOI

32
Tran V T, Althobiani F, Ball A. An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks. Expert Systems with Applications, 2014, 41( 9): 4113– 4122

DOI

33
Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35( 8): 1798– 1828

DOI

34
Erhan D, Bengio Y, Courville A. Why does unsupervised pre-training help deep learning?. Journal of Machine Learning Research, 2010, 11 : 625– 660

DOI

35
Shuman D I, Narang S K, Frossard P. The emerging field of signal processing on graphs: extending highdimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine, 2013, 30( 3): 83– 98

DOI

36
Song T, Zheng W, Song P. Eeg emotion recognition using dynamical graph convolutional neural networks. IEEE Transactions on Affective Computing, 2020, 11( 3): 532– 541

DOI

37
Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in Neural Information Processing Systems, 2016, 29 : 3844– 3852

38
Shao S, McAleer S, Yan R. Highly accurate machine fault diagnosis using deep transfer learning. IEEE Transactions on Industrial Informatics, 2019, 15( 4): 2446– 2455

DOI

39
Shao H, Jiang H, Zhao H. A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing, 2017, 95 : 187– 204

DOI

40
Li T, Zhao Z, Sun C, et al. Multi-receptive field graph convolutional networks for machine fault diagnosis. IEEE Transactions on Industrial Electronics, 2020 (in press)

Outlines

/