BEARING FAULT DIAGNOSIS METHOD FOR WIND TURBINE CONSIDERING INSUFFICIENT DATA AND BASED ON COOPERATIVE GAME MODEL FUSION

Li Junqing; Hu Xiaodong; Wang Luo; Ma Yapeng; He Yuling

doi:10.19912/j.0254-0096.tynxb.2022-1489

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (1) : 234-241. DOI: 10.19912/j.0254-0096.tynxb.2022-1489

BEARING FAULT DIAGNOSIS METHOD FOR WIND TURBINE CONSIDERING INSUFFICIENT DATA AND BASED ON COOPERATIVE GAME MODEL FUSION

Li Junqing¹, Hu Xiaodong¹, Wang Luo², Ma Yapeng¹, He Yuling³

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Abstract

Auxiliary classifier generative adversarial network （ACGAN） based on particle swarm optimization （PSO） was proposed to solve the problem of insufficient fault data caused by high cost of wind turbine bearing fatigue experiments. The parameters of ACGAN were optimized by PSO, and then ACGAN was used to generate new samples that were highly similar to the original samples. In view of the low accuracy of a single model for wind turbine bearing fault diagnosis, the cooperative game theory was introduced to fuse the diagnostic results of multiple sub-models, and the diagnostic probability matrix of each sub-model was fused by the cooperative game theory and the final diagnostic results were output. Experimental results show that the optimized ACGAN model and the model fusion based on cooperative game can effectively improve the accuracy of bearing fault diagnosis.

Key words

wind turbines / bearings / generative adversarial network / fault diagnosis / model fusion / cooperative game

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Li Junqing, Hu Xiaodong, Wang Luo, Ma Yapeng, He Yuling. BEARING FAULT DIAGNOSIS METHOD FOR WIND TURBINE CONSIDERING INSUFFICIENT DATA AND BASED ON COOPERATIVE GAME MODEL FUSION[J]. Acta Energiae Solaris Sinica. 2024, 45(1): 234-241 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1489

References

[1] 孙庞博, 符琦, 陈安华, 等. 基于组合预测模型的小样本轴承故障分类诊断[J]. 计算机工程与科学, 2021, 43(9): 1684-1691.
SUN P B, FU Q, CHEN A H, et al.Small sample bearing fault diagnosis based on combined prediction model[J]. Computer engineering & science, 2021, 43(9): 1684-1691.
[2] 宋凯旋. 滚动轴承故障诊断系统研究[D]. 徐州: 中国矿业大学, 2021.
SONG K X.Research on fault diagnosis system of rolling bearing[D]. Xuzhou: China University of Mining and Technology, 2021.
[3] 陈浈斐, 章黄勇, 马宏忠, 等. 基于深度学习的电力设备故障诊断方法研究综述[J]. 电气自动化, 2022, 44(1): 1-2, 6.
CHEN Z F, ZHANG H Y, MA H Z, et al.Summary of research on fault diagnosis methodsof power equipment based on deep learning[J]. Electrical automation, 2022, 44(1): 1-2, 6.
[4] 杨淑洁, 周杨. 基于EEMD-PCA-LSTM滚动轴承故障识别与分类方法的研究[J]. 机械工程师, 2021(11): 28-33.
YANG S J, ZHOU Y.Research on rolling bearing fault identification and classification based on EEMD-PCA-LSTM[J]. Mechanical engineer, 2021(11): 28-33.
[5] 张珂, 王竞禹, 石怀涛, 等. 基于CNN的变工况滚动轴承故障诊断研究[J]. 控制工程, 2022, 29(2): 254-262.
ZHANG K, WANG J Y, SHI H T, et al.Research on rolling bearing fault diagnosis under variable working conditions based on CNN[J]. Control engineering of China, 2022, 29(2): 254-262.
[6] 吴一斐, 黄伟, 李晓锋, 等. 基于CNN-GRU的风力发电变流器故障诊断技术[J]. 科技与创新, 2022(2): 168-170.
WU Y F, HUANG W, LI X F, et al.Fault diagnosis technology of wind power converter based on CNN-GRU[J]. Science and technology & innovation, 2022(2): 168-170.
[7] 叶祎旎, 李艳婷. 基于CNN-集成学习的多风电机组故障诊断[J]. 工业工程, 2022, 25(1): 136-143.
YE Y N, LI Y T.Fault diagnosis of multi wind turbine based on CNN-ensemble learning[J]. Industrial engineering journal, 2022, 25(1): 136-143.
[8] GAO X, DENG F, YUE X H.Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty[J]. Neurocomputing, 2020, 396: 487-494.
[9] 滕伟, 丁显, 史秉帅, 等. 基于WGAN-GP的风电机组传动链故障诊断[J]. 电力系统自动化, 2021, 45(22): 167-173.
TENG W, DING X, SHI B S, et al.Fault diagnosis of wind turbine drivetrain based on Wasserstein generative adversarial network-gradient penalty[J]. Automation of electric power systems, 2021, 45(22): 167-173.
[10] 李俊卿, 李斯璇, 陈雅婷, 等. 一种基于CGAN-CNN的同步电机转子绕组匝间短路故障诊断方法[J]. 电力自动化设备, 2021, 41(8): 169-174.
LI J Q, LI S X, CHEN Y T, et al.Fault diagnosis method of inter-turn short circuit of rotor winding of synchronous motor based on CGAN-CNN[J]. Electric power automation equipment, 2021, 41(8): 169-174.
[11] WANG Z W, QIN Y, CHEN W W.Vision measurement of gear pitting based on DCGAN and U-Net[J]. Journal of mechanical science and technology, 2021, 35(7): 2771-2779.
[12] 杨青, 陆见光, 唐向红, 等. 基于infoGAN和CNN的轴承小样本故障诊断[J]. 兵器装备工程学报, 2021, 42(11): 235-240.
YANG Q, LU J G, TANG X H, et al.Bearing small sample fault diagnosis based on InfoGAN and CNN[J]. Journal of ordnance equipment engineering, 2021, 42(11): 235-240.
[13] 侯柯冰, 程晓荣. 基于生成对抗网络的ACGAN模型[J]. 信息技术与信息化, 2020(5): 195-198.
HOU K B, CHENG X R.ACGAN model based on generating countermeasure network[J]. Information technology and informatization, 2020(5): 195-198.
[14] 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.
[15] 黄文康, 杨苏杭, 范梦婷, 等. 基于投票法的密度峰聚类算法[J]. 太赫兹科学与电子信息学报, 2021, 19(3): 517-522.
HUANG W K, YANG S H, FAN M T, et al.Density peak clustering based on voting method[J]. Journal of terahertz science and electronic information technology, 2021, 19(3): 517-522.
[16] 崔树银, 汪昕杰. 基于最大信息系数和多目标Stacking集成学习的综合能源系统多元负荷预测[J]. 电力自动化设备, 2022, 42(5): 32-39, 81.
CUI S Y, WANG X J.Multivariate load forecasting in integrated energy system based on maximal information coefficient and multi-objective Stacking ensemble learning[J]. Electric power automation equipment, 2022, 42(5): 32-39, 81.
[17] 张展, 韩华, 崔晓钰, 等. 基于多种模型集成学习的制冷系统故障诊断[J]. 热能动力工程, 2020, 35(5): 153-162.
ZHANG Z, HAN H, CUI X Y, et al.Fault diagnosis of refrigeration system based on integrated learning of multiple models[J]. Journal of engineering for thermal energy and power, 2020, 35(5): 153-162.
[18] 叶远波, 黄太贵, 谢民, 等. 基于多模型融合集成学习的智能变电站二次设备状态评估[J]. 电力系统保护与控制, 2021, 49(12): 148-157.
YE Y B, HUANG T G, XIE M, et al.A state assessment method for intelligent substation secondary equipment based on multi-model ensemble learning[J]. Power system protection and control, 2021, 49(12): 148-157.
[19] 吕柏行, 郭志光, 赵韦皓, 等. 标准粒子群算法的优化方式综述[J]. 科学技术创新, 2021(28): 33-37.
LYU B X, GUO Z G, ZHAO W H, et al.A review on optimization methods of standard particle swarm optimization[J]. Scientific and technological innovation, 2021(28): 33-37.
[20] 冯茜, 李擎, 全威, 等. 多目标粒子群优化算法研究综述[J]. 工程科学学报, 2021, 43(6): 745-753.
FENG Q, LI Q, QUAN W, et al.Overview of multiobjective particle swarm optimization algorithm[J]. Chinese journal of engineering, 2021, 43(6): 745-753.
[21] 谢沛松, 胡黄水, 张金栋. 改进LeNet-5网络模型图像分类[J]. 长春工业大学学报, 2021, 42(5): 455-461.
XIE P S, HU H S, ZHANG J D.Image dlassification based on improved LeNet-5 network model[J]. Journal of Changchun University of Technology, 2021, 42(5): 455-461.
[22] 王博文, 王景升, 王统一, 等. 基于卷积神经网络与门控循环单元的交通流预测模型[J]. 重庆大学学报, 2023, 46(8): 132-140.
WANG B W, WANG J S, WANG T Y, et al.Multivariable traffic flow prediction model based on convolutional neural network and gate recurrent unit[J]. Journal of Chongqing University, 2023, 46(8): 132-140.
[23] 肖雄, 王健翔, 张勇军, 等. 一种用于轴承故障诊断的二维卷积神经网络优化方法[J]. 中国电机工程学报, 2019, 39(15): 4558-4568.
XIAO X, WANG J X, ZHANG Y J, et al.A two-dimensional convolutional neural network optimization method for bearing fault diagnosis[J]. Proceedings of the CSEE, 2019, 39(15): 4558-4568.
[24] WANG B, LEI Y G, LI N P, et al.A hybrid prognostics approach for estimating remaining useful life of rolling element bearings[J]. IEEE transactions on reliability, 2020, 69(1): 401-412.