无源数据约束下多源域自适应的风电齿轮箱故障诊断方法

吴宣勇, 黄忠全, 李琪康, 汤宝平

太阳能学报 ›› 2024, Vol. 45 ›› Issue (4) : 238-246.

PDF(3547 KB)
欢迎访问《太阳能学报》官方网站,今天是
PDF(3547 KB)
太阳能学报 ›› 2024, Vol. 45 ›› Issue (4) : 238-246. DOI: 10.19912/j.0254-0096.tynxb.2022-1953

无源数据约束下多源域自适应的风电齿轮箱故障诊断方法

  • 吴宣勇, 黄忠全, 李琪康, 汤宝平
作者信息 +

MULTI-SOURCE DOMAIN ADAPTIVE FAULT DIAGNOSIS METHOD OF WIND TURBINE GEARBOX UNDER NO-ACCESSING SOURCE DATA CONSTRAINTS

  • Wu Xuanyong, Huang Zhongquan, Li Qikang, Tang Baoping
Author information +
文章历史 +

摘要

针对在数据隐私和安全性的背景下,无法接触源域数据导致领域自适应方法不可用的问题,提出一种无源数据约束下多源域自适应的故障诊断方法。首先,通过信息最大化损失促使源域与目标域数据在特征空间进行对齐;然后利用自监督伪标签策略挖掘目标域数据的特征表征信息,并采用熵筛选策略抑制噪声伪标签的影响;最后通过自适应加权有效利用多个源域的知识并抑制负迁移影响,实现无源数据约束下的风电齿轮箱的故障诊断。通过动力传动综合实验台数据和某风场风电机组CMS数据对所提方法进行验证与应用。结果表明:所提方法仅利用预训练的源域模型和目标域无标签数据即可有效实现目标域风电齿轮箱故障诊断。

Abstract

In the context of data privacy and security, domain adaptive methods is unavailable due to inaccessibility of source domain data. A multi-source domain adaptive fault diagnosis method under no-accessing source data constraints is proposed. Firstly, the source and target domain data are aligned in the feature space by information maximization loss. Then the feature representation information of the target domain data is further mined using the self-supervised pseudo-label strategy, and the influence of noise pseudo-labels is suppressed using the entropy filtering strategy. Finally, the knowledge of multiple source domains is effectively utilized and the influence of negative transfer is suppressed to realize the fault diagnosis of wind turbine gearbox under no-accessing source data constraints through adaptive weighting. This method is applied and verified using the drivetrain dynamic simulator test bench data and the wind turbine CMS data of a wind farm. The results show that the proposed method can effectively realize the fault diagnosis of wind turbine gearbox in the target domain using only the pre-trained source domain model and the unlabeled data of the target domain.

关键词

风电机组 / 数据隐私 / 自适应算法 / 无源数据约束 / 齿轮箱 / 故障诊断

Key words

wind turbines / data privacy / adaptive algorithms / no-accessing source data constrains / gearbox / fault diagnosis

引用本文

导出引用
吴宣勇, 黄忠全, 李琪康, 汤宝平. 无源数据约束下多源域自适应的风电齿轮箱故障诊断方法[J]. 太阳能学报. 2024, 45(4): 238-246 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1953
Wu Xuanyong, Huang Zhongquan, Li Qikang, Tang Baoping. MULTI-SOURCE DOMAIN ADAPTIVE FAULT DIAGNOSIS METHOD OF WIND TURBINE GEARBOX UNDER NO-ACCESSING SOURCE DATA CONSTRAINTS[J]. Acta Energiae Solaris Sinica. 2024, 45(4): 238-246 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1953
中图分类号: TH17   

参考文献

[1] 曾军, 陈艳峰, 杨苹, 等. 大型风力发电机组故障诊断综述[J]. 电网技术, 2018, 42(3): 849-860.
ZENG J, CHEN Y F, YANG P, et al.Review of fault diagnosis methods of large-scale wind turbines[J]. Power system technology, 2018, 42(3): 849-860.
[2] 陈雪峰, 郭艳婕, 许才彬, 等. 风电装备故障诊断与健康监测研究综述[J]. 中国机械工程, 2020, 31(2): 175-189.
CHEN X F, GUO Y J, XU C B, et al.Review of fault diagnosis and health monitoring for wind power equipment[J]. China mechanical engineering, 2020, 31(2): 175-189.
[3] JIANG G Q, HE H B, YAN J, et al.Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox[J]. IEEE transactions on industrial electronics, 2019, 66(4): 3196-3207.
[4] LEI J H, LIU C, JIANG D X.Fault diagnosis of wind turbine based on long short-term memory networks[J]. Renewable energy, 2019, 133: 422-432.
[5] 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.
[6] LI Q K, TANG B P, DENG L, et al.Deep balanced domain adaptation neural networks for fault diagnosis of planetary gearboxes with limited labeled data[J]. Measurement, 2020, 156: 107570.
[7] WEI D D, HAN T, CHU F L, et al.Weighted domain adaptation networks for machinery fault diagnosis[J]. Mechanical systems and signal processing, 2021, 158: 107744.
[8] GUO J W, WU J P, ZHANG S H, et al.Generative transfer learning for intelligent fault diagnosis of the wind turbine gearbox[J]. Sensors, 2020, 20(5): 1361.
[9] LECUN Y, BOTTOU L, BENGIO Y, et al.Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[10] LU N N, XIAO H H, SUN Y J, et al.A new method for intelligent fault diagnosis of machines based on unsupervised domain adaptation[J]. Neurocomputing, 2021, 427: 96-109.
[11] 郭亮, 董勋, 高宏力, 等. 无标签数据下基于特征知识迁移的机械设备智能故障诊断[J]. 仪器仪表学报, 2019, 40(8): 58-64.
GUO L, DONG X, GAO H L, et al.Feature knowledge transfer based intelligent fault diagnosis method of machines with unlabeled data[J]. Chinese journal of scientific instrument, 2019, 40(8): 58-64.
[12] LIANG J, HU D P, FENG J S. Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation[EB/OL].2020: arXiv: 2002.08546. http://arxiv.org/abs/2002.08546.pdf
[13] GRANDVALET Y, BENGIO Y.Semi-supervised learning by entropy minimization[C]//Advances in Neural Information Processing Systems 17. Vancouver, British Columbia, Canada, 2005, 529-536.
[14] BRIDLE J, HEADING A J R, MACKAY D. Unsupervised classifiers, mutual information and ‘phantom targets'[C]//Advances in Neural Information Processing Systems 4.Denver, Colorado, USA, 1991.
[15] CARON M, BOJANOWSKI P, JOULIN A, et al.Deep clustering for unsupervised learning of visual features[C]//Computer Vision-ECCV 2018: 15th European Conference. Munich, Germany, 2018, Proceedings, Part XIV. 2018: 139-156.
[16] AHMED S M, RAYCHAUDHURI D S, PAUL S, et al.Unsupervised multi-source domain adaptation without access to source data[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA, 2021: 10098-10107.
[17] SAPORTA A, VU T H, CORD M, et al. ESL: entropy-guided self-supervised learning for domain adaptation in semantic segmentation[EB/OL].2020: arXiv: 2006.08658. http://arxiv.org/abs/2006.08658.pdf
[18] VAN DER MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of machine learning research, 2008, 9: 2579-2605.

基金

国家自然科学基金(52275087); 国家重点研发计划(2020YFB1709800)

PDF(3547 KB)

Accesses

Citation

Detail

段落导航
相关文章

/