Adaptive adjustment graph representation learning method for rotating machinery fault diagnosis under noisy signals

Abstract

Recently, intelligent fault diagnosis methods have been employed in the condition monitoring of rotating machinery. Among them, graph neural networks are emerging as a new feature extraction tool that can mine the relationship characteristics between samples. However, many existing graph construction methods suffer from structural redundancy or missing node relationships, thus limiting the diagnosis accuracy of the models in practice. In this paper, an adaptive adjustment k-nearest neighbor graph-driven dynamic-weighted graph attention network (AAKNN-DWGAT) is proposed to address this problem. First, time-domain signals are transformed into frequency-domain features by using fast Fourier transformation. Subsequently, a frequency similarity evaluation method based on dynamic frequency warping is proposed, which enables the conversion of distance measurements into a frequency similarity matrix (FSM). Then, an adaptive edge construction operation is conducted on the basis of FSM, whereby the effective domain is captured for each node using an adaptive edge adjustment method, generating an AAKNN graph (AAKNNG). Next, the constructed AAKNNG is fed into a dynamic-weighted graph attention network (DWGAT) to extract the fault features of nodes layer by layer. In particular, the proposed DWGAT employs a dynamic-weighted strategy that can update the edge weight periodically using high-level output features, thereby eliminating the adverse impacts caused by noisy signals. Finally, the model outputs fault diagnosis results through a softmax classifier. Two case studies verified the effectiveness and the superiority of the proposed method compared with other graph neural networks and graph construction methods.

Graphical abstract

Keywords

rotating machinery / fault diagnosis / graph neural network / adaptive adjustment

References

[1] Shao H D, Zhou X D, Lin J, Liu B. Few-shot cross-domain fault diagnosis of bearing driven by task-supervised ANIL. IEEE Internet of Things Journal, 2024, 11(13): 22892–22902

[2] Lei Y G, Yang B, Jiang X W, Jia F, Li N P, Nandi A K. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mechanical Systems and Signal Processing, 2020, 138: 106587

[3] Liu Z P, Zhang L. A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings. Measurement, 2020, 149: 107002

[4] Tang S N, Yuan S Q, Zhu Y. Deep learning-based intelligent fault diagnosis methods toward rotating machinery. IEEE Access, 2020, 8: 9335–9346

[5] ZhangT C, Chen J L, PanT Y, ZhouZ T. Towards intelligent fault diagnosis under small sample condition via a signals augmented semi-supervised learning framework. In: Proceedings of 2020 IEEE 18th International Conference on Industrial Informatics. Warwick: IEEE, 2020, 669–672

[6] Luo J J, Shao H D, Lin J, Liu B. Meta-learning with elastic prototypical network for fault transfer diagnosis of bearings under unstable speeds. Reliability Engineering & System Safety, 2024, 245: 110001

[7] Fu D X, Liu J, Zhong H, Zhang X, Zhang F. A novel self-supervised representation learning framework based on time-frequency alignment and interaction for mechanical fault diagnosis. Knowledge-Based Systems, 2024, 295: 111846

[8] Fu Y, Liu Y, Yang Y. Multi-sensor GA-BP algorithm based gearbox fault diagnosis. Applied Sciences, 2022, 12(6): 3106

[9] YangZ X, Wang X B, WongP K, ZhongJ H. ELM based representational learning for fault diagnosis of wind turbine equipment. In: Proceedings of ELM-2015 Volume 2. Cham: Springer, 2016, 169–178

[10] Kadam V, Kumar S, Bongale A, Wazarkar S, Kamat P, Patil S. Enhancing surface fault detection using machine learning for 3d printed products. Applied System Innovation, 2021, 4(2): 34

[11] Abdul Z K, Al-Talabani A K. Highly accurate gear fault diagnosis based on support vector machine. Journal of Vibration Engineering & Technologies, 2023, 11(7): 3565–3577

[12] Zhang X, Wang H F, Wu B, Zhou Q, Hu Y M. A novel data-driven method based on sample reliability assessment and improved cnn for machinery fault diagnosis with non-ideal data. Journal of Intelligent Manufacturing, 2023, 34(5): 2449–2462

[13] Li W H, Lan H, Chen J B, Feng K, Huang R Y. WavCapsNet: an interpretable intelligent compound fault diagnosis method by backward tracking. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1–11

[14] Xing S B, Lei Y G, Wang S H, Jia F. Distribution-invariant deep belief network for intelligent fault diagnosis of machines under new working conditions. IEEE Transactions on Industrial Electronics, 2021, 68(3): 2617–2625

[15] Tang Y, Zhang X F, Zhai Y J, Qin G J, Song D Y, Huang S D, Long Z. Rotating machine systems fault diagnosis using semisupervised conditional random field-based graph attention network. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1–10

[16] ChenH, Wang X B, YangZ X. Adaptive semi-supervise graph neural network for fault diagnosis of tunnel ventilation systems. In: Proceedings of 5th International Conference on System Reliability and Safety. Palermo: IEEE, 2021, 53–57

[17] Yang C Y, Zhou K B, Liu J. SuperGraph: spatial-temporal graph-based feature extraction for rotating machinery diagnosis. IEEE Transactions on Industrial Electronics, 2022, 69(4): 4167–4176

[18] Wu Z H, Pan S R, Chen F W, Long G D, Zhang C Q, Yu P S. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(1): 4–24

[19] Li D P, Chen J X, Huang R Y, Chen Z Y, Li W H. Sensor-aware CapsNet: towards trustworthy multisensory fusion for remaining useful life prediction. Journal of Manufacturing Systems, 2024, 72: 26–37

[20] Salamat A, Luo X, Jafari A. HeteroGraphRec: a heterogeneous graph-based neural networks for social recommendations. Knowledge-Based Systems, 2021, 217: 106817

[21] LiQ M, Han Z C, WuX M. Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. New Orleans: AAAI, 2018, 3538–3545

[22] Zhang D C, Stewart E, Entezami M, Roberts C, Yu D J. Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network. Measurement, 2020, 156: 107585

[23] Chen Z W, Xu J M, Peng T, Yang C H. Graph convolutional network-based method for fault diagnosis using a hybrid of measurement and prior knowledge. IEEE Transactions on Cybernetics, 2022, 52(9): 9157–9169

[24] Zhao L, Song Y J, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H F. A temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(9): 3848–3858

[25] Chen Z W, Ke H B, Xu J M, Peng T, Yang C H. Multichannel domain adaptation graph convolutional networks-based fault diagnosis method and with its application. IEEE Transactions on Industrial Informatics, 2023, 19(6): 7790–7800

[26] Ai Z R, Cao H, Wang J H, Cui Z C, Wang L D, Jiang K. Research method for ship engine fault diagnosis based on multi-head graph attention feature fusion. Applied Sciences, 2023, 13(22): 12421

[27] Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. Graph attention networks. , 2017,

[28] Jiang L, Li X J, Wu L, Li Y B. Bearing fault diagnosis method based on a multi-head graph attention network. Measurement Science & Technology, 2022, 33(7): 075012

[29] Ding C Y, Sun S L, Zhao J. MST-GAT: a multimodal spatial-temporal graph attention network for time series anomaly detection. Information Fusion, 2023, 89: 527–536

[30] Zhang X, Liu J, Zhang X, Lu Y L. Multiscale channel attention-driven graph dynamic fusion learning method for robust fault diagnosis. IEEE Transactions on Industrial Informatics, 2024, 20(9): 11002–11013

[31] Tao H F, Shi H J, Qiu J E, Jin G H, Stojanovic V. Planetary gearbox fault diagnosis based on FDKNN-DGAT with few labeled data. Measurement Science & Technology, 2024, 35(2): 025036

[32] Yu X X, Tang B P, Deng L. Fault diagnosis of rotating machinery based on graph weighted reinforcement networks under small samples and strong noise. Mechanical Systems and Signal Processing, 2023, 186: 109848

[33] Xie Z L, Chen J L, Feng Y, He S L. Semi-supervised multi-scale attention-aware graph convolution network for intelligent fault diagnosis of machine under extremely-limited labeled samples. Journal of Manufacturing Systems, 2022, 64: 561–577

[34] Zhang X, Jiang L, Wang L, Zhang T A, Zhang F. A pruned-optimized weighted graph convolutional network for axial flow pump fault diagnosis with hydrophone signals. Advanced Engineering Informatics, 2024, 60: 102365

[35] Xiao Y M, Shao H D, Wang J, Yan S, Liu B. Bayesian variational transformer: a generalizable model for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing, 2024, 207: 110936

[36] Zhang X, Hu Y M, Liu J, Zhang X, Wu B. Robust rotating machinery diagnosis using a dynamic-weighted graph updating strategy. Measurement, 2022, 202: 111895

[37] Li T F, Zhao Z B, Sun C, Yan R Q, Chen X F. Multireceptive field graph convolutional networks for machine fault diagnosis. IEEE Transactions on Industrial Electronics, 2021, 68(12): 12739–12749

[38] Ince T, Kiranyaz S, Eren L, Askar M, Gabbouj M. Real-time motor fault detection by 1-D convolutional neural networks. IEEE Transactions on Industrial Electronics, 2016, 63(11): 7067–7075

[39] Li T F, Zhou Z, Li S N, Sun C, Yan R Q, Chen X F. The emerging graph neural networks for intelligent fault diagnostics and prognostics: a guideline and a benchmark study. Mechanical Systems and Signal Processing, 2022, 168: 108653

RIGHTS & PERMISSIONS

The Author(s). This article is published with open access at link.springer.com and journal.hep.com.cn