CROSS-WORKING CONDITIONS FAULT DIAGNOSIS OF ROTATING MACHINERY BASED ON PARTIAL DOMAIN ADAPTATION

Ma Tianting, Sun Lianghai, Han Bing, Shi Yaowei, Deng Aidong

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (6) : 479-486.

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (6) : 479-486. DOI: 10.19912/j.0254-0096.tynxb.2023-0275

CROSS-WORKING CONDITIONS FAULT DIAGNOSIS OF ROTATING MACHINERY BASED ON PARTIAL DOMAIN ADAPTATION

  • Ma Tianting1,2, Sun Lianghai2, Han Bing2, Shi Yaowei1, Deng Aidong1
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Abstract

To address the diagnosis problem in the scenario of varying data distribution and inconsistent label space due to the change of wind turbine working conditions, a partial domain adaptation method (FWDAN) based on fusion weights domain adversarial is proposed for cross-working condition fault diagnosis of rotating machinery. The core idea of FWDAN is to apply the training weights at both the sample and category to weaken the role of outlier category samples in the adversarial training process and enhance the learning of shared category samples, thus facilitating the transfer of shared diagnostic knowledge between domains and improving the diagnostic performance. For sample-level weight generation, the label information is coupled into the sample data to fully explore the feature representation. Further, different statistical methods are applied to generate weights for assisting model training according to the differences between source and target domain data to achieve the purpose of promoting positive model transfer and reducing the risk of negative transfer. The experimental results of two diagnostic cases built on rolling bearing and gearbox datasets show that the proposed method has higher diagnostic accuracy and stronger generalization ability than other methods.

Key words

wind turbines / fault diagnosis / transfer learning / domain adaptation / adversarial training

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Ma Tianting, Sun Lianghai, Han Bing, Shi Yaowei, Deng Aidong. CROSS-WORKING CONDITIONS FAULT DIAGNOSIS OF ROTATING MACHINERY BASED ON PARTIAL DOMAIN ADAPTATION[J]. Acta Energiae Solaris Sinica. 2024, 45(6): 479-486 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0275

References

[1] 陈雪峰, 郭艳婕. 风电装备振动监测与诊断[M]. 北京: 科学出版社, 2016: 4-5.
CHEN X F, GUO Y J.Vibration monitoring and diagnosis of wind power equipment[M]. Beijing: Science Press, 2016: 4-5.
[2] 齐咏生, 单成成, 高胜利, 等. 基于AEWT-KELM的风电机组轴承故障诊断策略[J]. 太阳能学报, 2022, 43(8): 281-291.
QI Y S, SHAN C C, GAO S L, et al.Fault diagnosis strategy of wind turbines bearing based on AEWT-KELM[J]. Acta energiae solaris sinica, 2022, 43(8): 281-291.
[3] LI X, ZHANG W, DING Q, et al.Multi-Layer domain adaptation method for rolling bearing fault diagnosis[J]. Signal processing, 2019, 157(C): 180-197.
[4] 安文杰, 陈长征, 田淼, 等. 基于迁移学习的风电机组轴承故障诊断研究[J]. 太阳能学报, 2023, 44(6): 367-373.
AN W J, CHEN C Z, TIAN M, et al.Research on bearing fault diagnosis of wind turbines based on transfer learning[J]. Acta energiae solaris sinica, 2023, 44(6): 367-373.
[5] 康守强, 胡明武, 王玉静, 等. 基于特征迁移学习的变工况下滚动轴承故障诊断方法[J]. 中国电机工程学报, 2019, 39(3): 764-772, 955.
KANG S Q, HU M W, WANG Y J, et al.Fault diagnosis method of a rolling bearing under variable working conditions based on feature transfer learning[J]. Proceedings of the CSEE, 2019, 39(3): 764-772, 955.
[6] 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.
[7] DENG M Q, DENG A D, ZHU J, et al.Intelligent fault diagnosis of rotating components in the absence of fault data: a transfer-based approach[J]. Measurement, 2021, 173: 108601.
[8] ZHANG Y C, JI J C, REN Z H, et al.Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing[J]. Reliability engineering & system safety, 2023, 234: 109186.
[9] DENG Y F, HUANG D L, DU S C, et al.A double-layer attention based adversarial network for partial transfer learning in machinery fault diagnosis[J]. Computers in industry, 2021, 127: 103399.
[10] LI X, ZHANG W.Deep learning-based partial domain adaptation method on intelligent machinery fault diagnostics[J]. IEEE transactions on industrial electronics, 2021, 68(5): 4351-4361.
[11] LI W H, CHEN Z Y, HE G L.A novel weighted adversarial transfer network for partial domain fault diagnosis of machinery[J]. IEEE transactions on industrial informatics, 2021, 17(3): 1753-1762.
[12] GANIN Y, USTINOVA E, AJAKAN H, et al. Domain-adversarial training of neural networks[EB/OL].2015: arXiv: 1505.07818. http://arxiv.org/abs/1505.07818.pdf.
[13] WANG Y, SUN X J, LI J, et al.Intelligent fault diagnosis with deep adversarial domain adaptation[J]. IEEE transactions on instrumentation and measurement, 2020, 70: 2503509.
[14] GAO D W, ZHU Y S, YAN K, et al.Joint learning system based on semi-pseudo-label reliability assessment for weak-fault diagnosis with few labels[J]. Mechanical systems and signal processing, 2023, 189: 110089.
[15] LI X Q, JIANG H K, LIU S W, et al.A unified framework incorporating predictive generative denoising autoencoder and deep Coral network for rolling bearing fault diagnosis with unbalanced data[J]. Measurement, 2021, 178: 109345.
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