FAULT DIAGNOSIS FOR WIND TURBINE GEARBOX BASED ON GRAPH ATTENTION NETWORKS

Wang Nini; Tan Qiyu; Ma Ping; Zhang Hongli

doi:10.19912/j.0254-0096.tynxb.2022-1501

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (1) : 265-274. DOI: 10.19912/j.0254-0096.tynxb.2022-1501

FAULT DIAGNOSIS FOR WIND TURBINE GEARBOX BASED ON GRAPH ATTENTION NETWORKS

Tan Qiyu, Ma Ping, Zhang Hongli, Wang Nini

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Abstract

Aiming at the issues of non-stationary, feature aliasing and low diagnostic accuracy of fault vibration signals from wind turbine gearbox, a fault diagnosis method for wind turbine gearbox based on graph attention networks （GAT） is proposed. Firstly, nodes and edges are defined by the frequency spectrum of the raw vibration signal to construct a visibility graph. Then, taking visibility graph data as input, the neighbor self-attention mechanism is embedded in GAT to adaptively extract node features and structure features of visibility graph signals. Finally, the classifier is used to classify and recognize the extracted node features. The experimental results of planetary gearbox dataset and wind turbine gearbox dataset show that the proposed method has higher accuracy, better robustness and noise immunity than machine learning, deep learning and other graph neural networks, which can effectively achieve end-to-end intelligent fault diagnosis.

Key words

wind turbines / gearbox / fault diagnosis / visibility graph / graph attention networks （GAT）

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Tan Qiyu, Ma Ping, Zhang Hongli, Wang Nini. FAULT DIAGNOSIS FOR WIND TURBINE GEARBOX BASED ON GRAPH ATTENTION NETWORKS[J]. Acta Energiae Solaris Sinica. 2024, 45(1): 265-274 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1501

References

[1] 孙文卿, 邓艾东, 邓敏强, 等. 基于模型融合的风电机组齿轮箱故障诊断[J]. 太阳能学报, 2022, 43(1): 64-72.
SUN W Q, DENG A D, DENG M Q, et al.Fault diagnosis of wind turbine gearbox based on model fusion[J]. Acta energiae solaris sinica, 2022, 43(1): 64-72.
[2] ZHAO Z B, LI T F, WU J Y, et al.Deep learning algorithms for rotating machinery intelligent diagnosis: an open source benchmark study[J]. ISA transactions, 2020, 107: 224-255.
[3] 丁承君, 冯玉伯, 王曼娜. 基于变分模态分解与深度卷积神经网络的滚动轴承故障诊断[J]. 振动与冲击, 2021, 40(2): 287-296.
DING C J, FENG Y B, WANG M N.Rolling bearing fault diagnosis using variational mode decomposition and deep convolutional neural network[J]. Journal of vibration and shock, 2021, 40(2): 287-296.
[4] ZHAO M H, KANG M, TANG B P, et al.Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes[J]. IEEE transactions on industrial electronics, 2018, 65(5): 4290-4300.
[5] 王妮妮, 马萍, 张宏立, 等. 基于多尺度深度卷积网络特征融合的滚动轴承故障诊断[J]. 太阳能学报, 2022, 43(4): 351-358.
WANG N N, MA P, ZHANG H L, et al.Fault diagnosis of rolling bearing based on feature fusion of multi-scale deep convolutional network[J]. Acta energiae solaris sinica, 2022, 43(4): 351-358.
[6] WEN L, LI X Y, GAO L, et al.A new convolutional neural network-based data-driven fault diagnosis method[J]. IEEE transactions on industrial electronics, 2018, 65(7): 5990-5998.
[7] ZHANG W, PENG G L, LI C H, et al.A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals[J]. Sensors, 2017, 17(2): 425.
[8] FANG H R, DENG J, ZHAO B, et al.LEFE-net: a lightweight efficient feature extraction network with strong robustness for bearing fault diagnosis[J]. IEEE transactions on instrumentation and measurement, 2021, 70: 1-11.
[9] SHUMAN D I, NARANG S K, FROSSARD P, et al.The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains[J]. IEEE signal processing magazine, 2013, 30(3): 83-98.
[10] QIU J Z, TANG J, MA H, et al.DeepInf: social influence prediction with deep learning[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, United Kingdom, 2018: 2110-2119.
[11] 王婷, 朱小飞, 唐顾. 基于知识增强的图卷积神经网络的文本分类[J]. 浙江大学学报(工学版), 2022, 56(2): 322-328.
WANG T, ZHU X F, TANG G.Knowledge-enhanced graph convolutional neural networks for text classification[J]. Journal of Zhejiang University (engineering science), 2022, 56(2): 322-328.
[12] LI T F, ZHOU Z, LI S N, 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.
[13] YU X X, TANG B P, ZHANG K.Fault diagnosis of wind turbine gearbox using a novel method of fast deep graph convolutional networks[J]. IEEE transactions on instrumentation and measurement, 2021, 70: 6502714.
[14] BRUNA J,ZAREMBA W,SZLAM A,et al.Spectral networks and deep locally connected networks on graphs[C]//2nd International Conference on Learning Representations, Banff, Canada, 2014.
[15] DEFFERRARD M, BRESSON X, VANDERGHEYNST P.Convolutional neural networks on graphs with fast localized spectral filtering[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, 2016: 3844-3852.
[16] KIPF T N, WELLING M.Semi-supervised classification with graph convolutional networks[C]//Proceedings of the International Conference on Learning Representations, Toulon, France, 2017.
[17] VELIČKOVIČ P, CUCURULL G, CASANOVA A, et al. Graph attention networks[C]//Proceedings of the International Conference on Learning Representations, Vancouver, Canada, 2018.
[18] SHAO S Y, MCALEER S, YAN R Q, et al.Highly accurate machine fault diagnosis using deep transfer learning[J]. IEEE transactions on industrial informatics, 2019, 15(4): 2446-2455.
[19] SAUFI S R, BIN AHMAD Z A, 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.