Fault Diagnosis of Wind Turbine Gearbox Based on Convolutional Dual Channel Multilayer Perceptron Mixer and Weighted Voting Mechanism

doi:10.3969/j.issn.1006-2475.2025.12.003

Abstract

Abstract: Abstract: The gearbox is one of the key and vulnerable components in wind turbines， and the fault diagnosis of its health condition is of great significance to reduce operation and maintenance costs and improve cost efficiency. Therefore， a fault diagnosis method of wind turbine gearbox based on convolutional double-channel multi-layer perceptron mixer and weighted voting mechanism is proposed. Firstly， the original vibration signal is transformed into two-dimensional time-frequency image by continuous wavelet transform. Then， the local features of 2D time-frequency images are extracted by using a two-dimensional convolutional network， and the time-frequency image data is divided into non-overlapping patches. A two-channel multi-layer perceptron mixer network is constructed to extract the global features. Finally， the extracted two global feature vectors are weighted to get the final feature representation， and the final fault diagnosis result is obtained through the full connection layer classification. The test results of UConn gearbox dataset show that the proposed method has higher diagnostic performance than other traditional methods， and achieves the highest diagnostic accuracy of 100%.

Key words: Key words： wind turbine gearbox, fault diagnosis, convolutional network, dual channel multi-layer perceptron mixer, weighted voting mechanism

CLC Number:

中图分类号：TP202

WANG Wanglong1, XU Junyang2, SU Peng1, FU Wenlong3. Fault Diagnosis of Wind Turbine Gearbox Based on Convolutional Dual Channel Multilayer Perceptron Mixer and Weighted Voting Mechanism[J]. Computer and Modernization, 2025, 0(12): 19-25.

References

［1］ LEBRANCHU A， CHARBONNIER S， BÉRENGUER C， et al. A combined mono-and multi-turbine approach for fault indicator synthesis and wind turbine monitoring using SCADA data［J］. ISA Transactions， 2019，87:272-281.
［2］ ZHU Y C， ZHU C C， TAN J J， et al. Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis［J］. Renewable Energy， 2022，181:1167-1176.
［3］ XU Z， LI C， YANG Y. Fault diagnosis of rolling bearings using an improved multi-scale convolutional neural network with feature attention mechanism［J］. ISA Transactions， 2021，110:379-393.
［4］ WANG L， CAI G G， WANG J， et al. Dual-enhanced sparse decomposition for wind turbine gearbox fault diagnosis［J］. IEEE Transactions on Instrumentation and Measurement， 2018，68（2）:450-461.
［5］ WEI Y， YANG Y T， XU M Q， et al. Intelligent fault diagnosis of planetary gearbox based on refined composite hierarchical fuzzy entropy and random forest［J］. ISA Transactions， 2021，109:340-351.
［6］蔡昌春，何捷，承敏钢，等. 基于改进VMD-MCKD和深度残差网络的风机齿轮箱故障诊断［J］. 山东电力技术， 2024，51（2）:67-78.
［7］ FENG Z P， QIN S F， LIANG M. Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions［J］. Renewable Energy， 2016，85:45-56.
［8］闫君杰，苏晨. 基于人工神经网络的煤矿机械齿轮故障诊断研究［J］. 煤炭技术， 2017，36 （9）:266-268.
［9］王璞，孙洁，张怡. 基于LSTM-SVM的风电机组齿轮箱故障诊断［J］. 机床与液压， 2023，51（16）:211-214.
［10］林永君，张世成，杨凯，等. 基于时钟循环神经网络的光伏故障诊断［J］. 山东电力技术， 2024，51（1）: 52-58.
［11］钱荣荣，谭涛. 基于改进卷积深度信念网络的风电机组行星齿轮箱故障诊断方法［J］. 电力需求侧管理， 2022，24（2）:27-33.
［12］杨欢，刘德洋，彭利平. DTCWT多尺度联合熵和CNN的行星齿轮故障诊断方法［J］. 机械设计与制造， 2022（12）:127-130.
［13］姚立，孙见君，马晨波. 基于格拉姆角场和CNN-RNN的滚动轴承故障诊断方法［J］. 轴承， 2022（2）:61-67.
［14］ HE K M， ZHANG X Y， REN S Q， et al. Deep residual learning for image recognition［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE， 2016:770-778.
［15］ SZEGEDY C， VANHOUCKE V， IOFFE S， et al. Rethinking the inception architecture for computer vision［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE， 2016:2818-2826.
［16］ HUANG G， LIU Z， VAN DER MAATEN L， et al. Densely connected convolutional networks［C］// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. IEEE， 2017:4700-4708.
［17］ TOLSTIKHIN I O， HOULSBY N， KOLESNIKOV A， et al. MLP-Mixer: An all-MLP architecture for vision［C］// Proceedings of the 35th Conference on Neural Information Processing Systems. NeurIPS， 2021:24261-24272.
［18］ LIU R Y， LI Y H， TAO L M， et al. Are we ready for a new paradigm shift? A survey on visual deep MLP［J］. Patterns， 2022，3（7）. DOI: 10.1016/j.patter.2022.100520.
［19］ LUO J M， TANG Y Z， WANG J， et al. USMLP: U-shaped Sparse-MLP network for mass segmentation in mammograms［J］. Image and Vision Computing， 2023，137. DOI: 10.1016/j.imavis.2023.104761.
［20］ GUO J Y， TANG Y H， HAN K， et al. Hire-MLP: Vision MLP via hierarchical rearrangement［C］// Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE， 2022:826-836.
［21］卓识，战利伟，白晓峰，等. 基于CWT-AT-CNN的航空滚动轴承故障诊断方法［J/OL］. 轴承， 1-9［2024-09-30］. https://link.cnki.net/urlid/41.1148.TH.20231219.1640.006.
［22］陈代俊，陈里里，董绍江. 基于VMD-CWT-CNN的滚动轴承故障诊断［J］. 机械强度， 2023，45 （6）:1280-1285.
［23］江蕾，唐建，杨超越，等. 基于CWGAN-GP与CNN的轴承故障诊断方法［J］. 计算机与现代化， 2023（7）:1-6.
［24］卢一相，钱冬生，竺德，等. 基于多尺度小波包启发卷积网络的旋转机械故障诊断［J］. 振动与冲击， 2024，43（17）:203-213.
［25］窦唯，赵东方，张宏利，等. 基于Hessian局部线性嵌入和MLP-Mixer的液体火箭发动机涡轮泵轻量化故障诊断框架［J］. 振动与冲击， 2024，43 （2）:156-165.
［26］吴俊洁，王佳阳，朱萍，等. 基于MLP和多头自注意力特征融合的双模态情感计算模型［J］. 计算机应用， 2024，44（S1）:39-43.
［27］郑震，严迎建，刘燕江. 基于SVD和MLP的多分类能量信息泄漏评估方法［J］. 华中科技大学学报（自然科学版）， 2025，53（5）:52-57.
［28］ CAO P， ZHANG S L， TANG J. Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning［J］. IEEE Access， 2018，6:26241-26253.

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Fault Diagnosis of Wind Turbine Gearbox Based on Convolutional Dual Channel Multilayer Perceptron Mixer and Weighted Voting Mechanism

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