WIND TURBINE GEARBOX FAULT DIAGNOSIS BASED ON INFORMATION FUSION USING NRP-ALEXNET-SENET

Long Xiafei; He Zhicheng; Zeng Jinhui; Zhou Ling; Liang Kai; Wu Xiwen

doi:10.19912/j.0254-0096.tynxb.2024-0848

PDF(1609 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (9) : 143-151. DOI: 10.19912/j.0254-0096.tynxb.2024-0848

WIND TURBINE GEARBOX FAULT DIAGNOSIS BASED ON INFORMATION FUSION USING NRP-ALEXNET-SENET

Long Xiafei¹, He Zhicheng¹, Zeng Jinhui¹, Zhou Ling¹, Liang Kai¹, Wu Xiwen²

Author information +

History +

Abstract

Aiming at the issues of nonlinearity, aliasing of characteristic information, and low diagnostic accuracy of vibration signals in the wind turbine gearbox, by integrating time-domain characteristic analysis and multi-sensor information fusion technology, an early fault identification approach based on non-threshold recurrence plot （NRP） and deep learning was proposed. First, the time-domain indices is used as the feature parameter, and the feature-level and data-level fusion technology is used to construct the dataset. Secondly, NRP converts one-dimensional data into two-dimensional colored visual feature maps. Then, an AlexNet-SENet network structure is built, and the improved AlexNet embedded with SENet's attention mechanism makes it adaptive to focus on the key structural and nodal feature. Finally, taking the vibration data set collected by the planetary gearbox dynamics test platform of Huazhong University of Science and Technology as an example, the results show that the diagnosis accuracy of this method is 99%, which can extract fault characteristics more effectively and has higher classification and diagnosis accuracy.

Key words

wind turbine gearbox / fault diagnosis / deep learning / information fusion / non-threshold recurrence plot / attention mechanism

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Long Xiafei, He Zhicheng, Zeng Jinhui, Zhou Ling, Liang Kai, Wu Xiwen. WIND TURBINE GEARBOX FAULT DIAGNOSIS BASED ON INFORMATION FUSION USING NRP-ALEXNET-SENET[J]. Acta Energiae Solaris Sinica. 2025, 46(9): 143-151 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0848

References

[1] 张振海, 王维庆, 王海云, 等. 基于HCS-GWO-MSVM的风电机组齿轮箱复合故障诊断研究[J]. 太阳能学报, 2021, 42(10): 176-182.
ZHANG Z H, WANG W Q, WANG H Y, et al.Research on compound fault diagnosis of wind turbine gearbox based on HCS-GWO-MSVM[J]. Acta energiae solaris sinica, 2021, 42(10): 176-182.
[2] 吴岚, 柳亦兵, 吴仕明, 等. 基于VMD倒频变换的风电机组齿轮箱复合故障诊断[J]. 振动与冲击, 2023, 42(24): 221-227, 256.
WU L, LIU Y B, WU S M, et al.Composite fault diagnosis of wind turbine gearboxes based on VMD cepstral transform[J]. Journal of vibration and shock, 2023, 42(24): 221-227, 256.
[3] 谭启瑜, 马萍, 张宏立, 等. 基于图注意力网络的风力发电机齿轮箱故障诊断[J]. 太阳能学报, 2024, 45(1): 265-274.
TAN Q Y, MA P, ZHANG H L, et al.Fault diagnosis for wind turbine gearbox based on graph attention networks[J]. Acta energiae solaris sinica, 2024, 45(1): 265-274.
[4] TANG M Z, ZHAO Q, WU H W, et al.Review and perspectives of machine learning methods for wind turbine fault diagnosis[J]. Frontiers in energy research, 2021, 9: 751066.
[5] 张品杨, 陈长征. 基于U-net和随机森林的齿轮箱振动时频分析和故障诊断[J]. 机电工程, 2023, 40(8): 1210-1217.
ZHANG P Y, CHEN C Z.Time-frequency analysis and fault identification of wind turbine gearbox based on U-net and random forest[J]. Journal of mechanical & electrical engineering, 2023, 40(8): 1210-1217.
[6] 陈萱, 杨永超, 袁博洋, 等. NGO-VMD和SSNGO-RF算法在风机齿轮箱故障诊断中的应用[J]. 湖北民族大学学报(自然科学版), 2023, 41(4): 520-529.
CHEN X, YANG Y C, YUAN B Y, et al.Application of NGO-VMD and SSNGO-RF algorithms in fault diagnosis of wind turbine gearboxes[J]. Journal of Hubei Minzu University (natural science edition), 2023, 41(4): 520-529.
[7] 王浩楠, 崔宝珍, 彭智慧, 等. 应用ICEEMDAN和SVM的行星齿轮箱故障诊断[J]. 机械科学与技术, 2023, 42(1): 24-30.
WANG H N, CUI B Z, PENG Z H, et al.Fault diagnosis of planetary gearbox using ICEEMDAN and SVM[J]. Mechanical science and technology for aerospace engineering, 2023, 42(1): 24-30.
[8] 刘秀丽, 王鸽, 吴国新, 等. VMD及PSO优化SVM的行星齿轮箱故障诊断[J]. 电子测量与仪器学报, 2022, 36(1): 54-61.
LIU X L, WANG G, WU G X, et al.Fault diagnosis method of planetary gear box based on variational modal decomposition and particle swarm optimization support vector machine[J]. Journal of electronic measurement and instrumentation, 2022, 36(1): 54-61.
[9] 张龙, 胡燕青, 赵丽娟, 等. 采用递归图编码技术与残差网络的滚动轴承故障诊断[J]. 西安交通大学学报, 2023, 57(2): 110-120.
ZHANG L, HU Y Q, ZHAO L J, et al.Fault diagnosis of rolling bearings using recurrence plot coding technique and residual network[J]. Journal of Xi'an Jiaotong University, 2023, 57(2): 110-120.
[10] 常淼, 沈艳霞. 基于贝叶斯优化CNN的风电轴承故障诊断策略[J]. 噪声与振动控制, 2021, 41(6): 77-83.
CHANG M, SHEN Y X.Fault diagnosis strategy of wind turbine bearings based on Bayesian optimized CNN[J]. Noise and vibration control, 2021, 41(6): 77-83.
[11] 赵志宏, 李春秀, 窦广鉴, 等. 基于MTF-CNN的轴承故障诊断研究[J]. 振动与冲击, 2023, 42(2): 126-131.
ZHAO Z H, LI C X, DOU G J, et al.Bearing fault diagnosis method based on MTF-CNN[J]. Journal of vibration and shock, 2023, 42(2): 126-131.
[12] WANG D F, GUO Y, WU X, et al.Planetary-gearbox fault classification by convolutional neural network and recurrence plot[J]. Applied sciences, 2020, 10(3): 932.
[13] 韩亮, 罗统军, 蒲秀娟, 等. 使用多尺度递归图和视觉转换器的心律失常分类[J]. 仪器仪表学报, 2022, 43(12): 149-157.
HAN L, LUO T J, PU X J, et al.Arrhythmia classification using multi-scale recurrence plot and vision transformer[J]. Chinese journal of scientific instrument, 2022, 43(12): 149-157.
[14] WANG M H, HUNG C C, LU S D, et al.Fault diagnosis for PV modules based on AlexNet and symmetrized dot pattern[J]. Energies, 2023, 16(22): 7563.
[15] 吴春志, 江鹏程, 冯辅周, 等. 基于一维卷积神经网络的齿轮箱故障诊断[J]. 振动与冲击, 2018, 37(22): 51-56.
WU C Z, JIANG P C, FENG F Z, et al.Faults diagnosis method for gearboxes based on a 1-D convolutional neural network[J]. Journal of vibration and shock, 2018, 37(22): 51-56.
[16] 雷春丽, 薛林林, 焦孟萱, 等. 结合改进ResNet与迁移学习的风力机滚动轴承故障诊断方法[J]. 太阳能学报, 2023, 44(6): 436-444.
LEI C L, XUE L L, JIAO M X, et al.Fault diagnosis method of wind turbines rolling bearing based on improved resnet and transfer learning[J]. Acta energiae solaris sinica, 2023, 44(6): 436-444.