WIND TURBINE GEARBOX FAULT DIAGNOSIS METHOD FOR OPTIMIZED WGAN WITH UNBALANCED DATA SETS

Su Yuanhao; Meng Liang; Xu Tongle; Kong Xiaojia; Lan Xiaosheng; Li Yunfeng

doi:10.19912/j.0254-0096.tynxb.2021-1479

PDF(4632 KB)

Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (11) : 148-155. DOI: 10.19912/j.0254-0096.tynxb.2021-1479

WIND TURBINE GEARBOX FAULT DIAGNOSIS METHOD FOR OPTIMIZED WGAN WITH UNBALANCED DATA SETS

Su Yuanhao, Meng Liang, Xu Tongle, Kong Xiaojia, Lan Xiaosheng, Li Yunfeng

Author information +

History +

Abstract

To solve the problem of low-fault diagnostic accuracy and unclear fault characteristics of wind turbine gearboxes under unbalanced data sets, a fault diagnosis method of Wasserstein generative adversarial networks optimized by kurtosis label and genetic algorithm is proposed in this paper. Firstly, the kurtosis label is mapped to the convolution layer as a semantic label to normalize the fault features. Secondly, the metagenomics is binary coded and the weights are initialized in deconvolution networks. Then, multi-point crossover and gaussian approximate variation are performed on the unbalanced sample sets to search for local faults. Finally, kurtosis is inputted to the discriminator network as labeled negative cases, and deconvolution and VGG neural networks are reconstructed to improve weight cutting. The WGAN network becomes a semi-supervised learning model, which can update weight forward judgment and output diagnostic results. Experimental results showed that the diagnostic accuracy of the proposed method could reach 98.69% under unbalanced data sets, indicating that it has a higher generalization ability and feature extraction ability, which can enhance fault features.

Key words

wind turbines / gearboxs / generative adversarial network / genetic algorithm / kurtosis / semi-supervised learning / fault diagnosis

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Su Yuanhao, Meng Liang, Xu Tongle, Kong Xiaojia, Lan Xiaosheng, Li Yunfeng. WIND TURBINE GEARBOX FAULT DIAGNOSIS METHOD FOR OPTIMIZED WGAN WITH UNBALANCED DATA SETS[J]. Acta Energiae Solaris Sinica. 2022, 43(11): 148-155 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1479

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] RAGHUWANSHI B S, SHUKLA S.Smote based class-specific extreme learning machine for imbalanced learning[J]. Knowledge-based systems, 2020, 187: 104814.
[3] XIA M,LI T,LIU L,et al.Intelligent fault diagnosis approach with unsupervised feature learning by stacked denosing autoencoder[J]. IET science, measurement and technology, 2017, 11(6) : 687-695.
[4] CABERERA D, SANCHO F, LONG J, et al.Generative adversarial networks selection approach for extremely imbalanced fault diagnosis of reciprocating machinery[J]. IEEE access, 2019, 7: 70643-70653.
[5] GOODFELLOW I, POUGET-ABADIE J, MIRZA M,et al.Generative adversarial nets[C]//Proceedings of the 27th Conference on Advances in Neural Information Processing Systems, Barcelona, Sparn, 2014: 2672-2680.
[6] ARJOVSKY M, CHINTALA S, BOTTOU L,et al.Wasserstein generative adversarial networks[C]//International Conference on Machine Learning, Sydney, Austialia, 2017: 214-223.
[7] 刘科研, 盛万兴, 马晓晨, 等. 基于多种群遗传算法的分布式光伏接入配电网规划研究[J]. 太阳能学报, 2021, 42(6): 146-155.
LIU K Y, SHENG W X, MA X C, et al.Planning research of distributed photovoltaic source access distribution network based on multi-population genetic algorithm[J]. Acta energiae solaris sinica, 2021, 42(6): 146-155.
[8] 张颖, 张超, 王天正, 等. 基于改进固有时间尺度分解和谱峭度的轴承故障诊断方法[J]. 太阳能学报, 2017, 38(3): 699-706.
ZHANG Y, ZHANG C, WANG T Z, et al.Fault diagnosis method for bearing based on improved intrinsic time-scale decom position and spectrum kurtosis[J]. Acta energiae solaris sinica, 2017, 38(3): 699-706.
[9] 吴永忠, 刘华威, 侯诗文, 等. 模拟退火遗传算法在风力提水机翼型优化设计中的研究[J]. 太阳能学报, 2021, 42(6): 385-390.
WU Y Z, LIU H W, HOU S W, et al.Reserch on simulated annealing genetic algorithm in optimization design of water-pimping wind-mill[J]. Acta energiae solaris sinica, 2021, 42(6): 385-390.
[10] 何强, 唐向红, 李传江, 等. 负载不平衡下小样本数据的轴承故障诊断[J]. 中国机械工程, 2021, 32(10): 1164-1171, 1180.
HE Q, TANG X H, LI C J, et al.Bearing fault diagnosis method based on small sample data under unbalanced loads[J]. China mechanical engineering, 2021, 32(10): 1164-1171, 1180.
[11] 胡兵兵, 唐嘉辉, 武吉梅, 等. 基于Triple GAN的滚动轴承故障诊断方法研究[J]. 噪声与振动控制, 2021, 41(4): 109-114, 220.
HU B B, TANG J H, WU J M, et al.Study on fault diagnosis method of rolling bearings based on Triple GAN[J]. Noise and vibration control, 2021, 41(4): 109-114, 220.
[12] WANG D, GUO Q, SONG Y, et al.Application of multiscale learning neural network based on CNN in bearing fault diagnosis[J]. Journal of signal processing systems, 2019, 91(10): 1205-1217.
[13] 尹召杰, 许同乐, 郑店坤. LMD支持向量机电机轴承故障诊断研究[J]. 哈尔滨理工大学学报, 2018, 23(5):35-39.
YIN Z J, XU T L, ZHENG D K.Fault diagnosis research on bearing of motor based on LMD and support vector machine[J]. Journal of Harbin University of Science and Technology, 2018, 23(5): 35-39.