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An adversarial model for electromechanical actuator fault diagnosis under nonideal data conditions

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Abstract

Electromechanical actuators (EMAs) are safety-critical components that work under various conditions and loads. Realizing robust and precise fault diagnosis for an EMA increases the overall availability/safety of the whole system. However, the monitoring data of an EMA are collected under different working conditions and consist of numerous unlabeled samples and few unbalanced labeled samples; this severely limits the applications of intelligent data-driven diagnosis approaches. Therefore, this paper provides an extended convolutional adversarial autoencoder (ECAAE) as an adversarial model to achieve end-to-end fault diagnosis for EMAs based only on vibration signals. This approach combines the feature extraction ability of convolutional neural networks (CNNs) with the semisupervised learning and data generation capabilities of adversarial autoencoders (AAEs) by activating different submodels during different training phases and is thus able to utilize both unlabeled and unbalanced labeled samples. With the help of a hyperparameter-free signal conversion method and an imbalance-compensation loss function, the adversarial training process of the model results in a feature extractor that is robust to varying working conditions as well as a sample generator capable of generating samples belonging to a given class. Consequently, after fine-tuning on samples rebalanced by the generator, the classifier of the ECAAE is able to perform robust and precise fault diagnosis even under various working conditions, unbalanced samples and few-shot situations. The proposed method is validated under 12 multicondition data scenarios and achieves a diagnostic accuracy above 90%, even in cases worse than 5-way 5-shot scenarios, thereby revealing its superiority over 3 state-of-the-art models.

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References

  1. Qiao G, Liu G, Shi Z et al (2018) A review of electromechanical actuators for More/All Electric aircraft systems. Proc Inst Mech Eng C J Mech Eng Sci 232:4128–4151. https://doi.org/10.1177/0954406217749869

    Article  Google Scholar 

  2. Smith MJ, Byington CS, Watson MJ et al. (2009) Experimental and analytical development of health management for Electro-Mechanical Actuators. In: 2009 IEEE Aerospace conference. IEEE, pp 1–14

  3. Sriram N, Indranil R, Edward B et al. (2010) Combining model based and feature driven diagnosis approaches a case study on electromechanical Actuators. https://doi.org/10.36001/phmconf.2010.v2i1.1936

  4. Dalla Vedova MDL, Germanà A, Berri PC et al (2019) Model-based fault detection and identification for prognostics of electromechanical actuators using genetic algorithms. Aerospace 6:94. https://doi.org/10.3390/aerospace6090094

    Article  Google Scholar 

  5. Di Rito G, Luciano B, Borgarelli N et al. (2020) Health-monitoring of a jamming-tolerant electro-mechanical actuator with differential ball screws. In: 2020 IEEE 7th International Workshop on Metrology for AeroSpace (MetroAeroSpace). IEEE, pp 84–89

  6. de Martini D, Facchinetti T (2020) Fault detection of electromechanical actuators via automatic generation of a fuzzy index. IEEE/ASME Trans Mechatron 25:2197–2207. https://doi.org/10.1109/tmech.2020.3011005

    Article  Google Scholar 

  7. Wen L, Li X, Gao L et al (2018) A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans Ind Electron 65:5990–5998. https://doi.org/10.1109/TIE.2017.2774777

    Article  Google Scholar 

  8. Chirico AJ, Kolodziej JR (2014) A data driven methodology for fault detection in electromechanical actuators. J Dyn Sys, Meas, Control 10(1115/1):4026835

    Google Scholar 

  9. Liu H, Jing J, Ma J (2018) Fault diagnosis of electromechanical actuator based on VMD multifractal detrended fluctuation analysis and PNN. Complexity 2018:1–11. https://doi.org/10.1155/2018/9154682

    Article  Google Scholar 

  10. Riaz N, Shah SIA, Rehman F et al (2020) A novel 2-D current signal-based residual learning with optimized softmax to identify faults in ball screw actuators. IEEE Access 8:115299–115313. https://doi.org/10.1109/access.2020.3004489

    Article  Google Scholar 

  11. Ruiz-Carcel C, Starr A (2018) Data-based detection and diagnosis of faults in linear actuators. IEEE Trans Instrum Meas 67:2035–2047. https://doi.org/10.1109/TIM.2018.2814067

    Article  Google Scholar 

  12. Lei Y, Yang B, Jiang X et al (2020) Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Process 138:106587. https://doi.org/10.1016/j.ymssp.2019.106587

    Article  Google Scholar 

  13. Liang Y, Li B, Jiao B (2021) A deep learning method for motor fault diagnosis based on a capsule network with gate-structure dilated convolutions. Neural Comput & Applic 33:1401–1418. https://doi.org/10.1007/s00521-020-04999-0

    Article  Google Scholar 

  14. Yang J, Guo Y, Zhao W (2019) Long short-term memory neural network based fault detection and isolation for electro-mechanical actuators. Neurocomputing 360:85–96. https://doi.org/10.1016/j.neucom.2019.06.029

    Article  Google Scholar 

  15. Yang N, Shen J, Jia Y et al. (2020) Fault diagnosis of electro-mechanical actuator based on deep learning network. In: 2020 39th Chinese Control Conference (CCC). IEEE, pp 4002–4006

  16. Peng G, Zheng Y, Li J et al (2021) A single upper limb pose estimation method based on the improved stacked hourglass network. Int J Appl Math Comput Sci 31:123–133

    MATH  Google Scholar 

  17. Siahpour S, Li X, Lee J (2020) Deep learning-based cross-sensor domain adaptation for fault diagnosis of electro-mechanical actuators. Int J Dynam Control 8:1054–1062. https://doi.org/10.1007/s40435-020-00669-0

    Article  Google Scholar 

  18. Goodfellow I, Pouget-Abadie J, Mirza M et al (2020) Generative adversarial networks. Commun ACM 63:139–144. https://doi.org/10.1145/3422622

    Article  Google Scholar 

  19. Ding Y, Ma L, Ma J et al (2019) A generative adversarial network-based intelligent fault diagnosis method for rotating machinery under small sample size conditions. IEEE Access 7:149736–149749. https://doi.org/10.1109/ACCESS.2019.2947194

    Article  Google Scholar 

  20. Makhzani A, Shlens J, Jaitly N et al. (2015) Adversarial Autoencoders

  21. Wang Y, Sun G, Jin Q (2020) Imbalanced sample fault diagnosis of rotating machinery using conditional variational auto-encoder generative adversarial network. Appl Soft Comput 92:106333. https://doi.org/10.1016/j.asoc.2020.106333

    Article  Google Scholar 

  22. Pan T, Chen J, Qu C et al (2021) A method for mechanical fault recognition with unseen classes via unsupervised convolutional adversarial auto-encoder. Meas Sci Technol 32:35113. https://doi.org/10.1088/1361-6501/abb38c

    Article  Google Scholar 

  23. Balaban E, Saxena A, Narasimhan S et al (2015) Prognostic health-management system development for electromechanical actuators. J Aerosp Inform Syst 12:329–344. https://doi.org/10.2514/1.I010171

    Article  Google Scholar 

  24. Martin A, Soumith C, Léon B (2017) Wasserstein generative adversarial networks. International Conference on Machine Learning: pp. 214–223

  25. Ishaan G, Faruk A, Martin A et al. Improved Training of Wasserstein GANs

  26. Lin T-Y, Goyal P, Girshick R et al. (2017) Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE

  27. Lei Y, Jia F, Lin J et al (2016) An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans Ind Electron 63:3137–3147. https://doi.org/10.1109/TIE.2016.2519325

    Article  Google Scholar 

  28. Chawla NV, Bowyer KW, Hall LO et al (2002) SMOTE: synthetic minority over-sampling technique. Jair 1(16):321–357. https://doi.org/10.1613/jair.953

    Article  MATH  Google Scholar 

  29. Lu C, Wang Z-Y, Qin W-L et al (2017) Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Process 130:377–388. https://doi.org/10.1016/j.sigpro.2016.07.028

    Article  Google Scholar 

  30. Zhou F, Yang S, Fujita H et al (2020) Deep learning fault diagnosis method based on global optimization GAN for unbalanced data. Knowl-Based Syst 187:104837. https://doi.org/10.1016/j.knosys.2019.07.008

    Article  Google Scholar 

  31. Guo S, Yang T, Gao W et al (2018) A novel fault diagnosis method for rotating machinery based on a convolutional neural network. Sensors (Basel). https://doi.org/10.3390/s18051429

    Article  Google Scholar 

  32. Lu C, Wang Y, Ragulskis M et al (2016) Fault diagnosis for rotating machinery: a method based on image processing. PLoS ONE 11:e0164111. https://doi.org/10.1371/journal.pone.0164111

    Article  Google Scholar 

  33. Ren Z, Zhu Y, Yan K et al (2020) A novel model with the ability of few-shot learning and quick updating for intelligent fault diagnosis. Mech Syst Sig Process 138:106608. https://doi.org/10.1016/j.ymssp.2019.106608

    Article  Google Scholar 

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant Nos. 61973011 and 61803013), the Fundamental Research Funds for the Central Universities (Grant Nos. YWF-21-BJ-J-723, YWF-21-BJ-J-517 and ZG140S1993), as well as the Capital Science & Technology Leading Talent Program (Grant No. Z191100006119029). The authors also would like to extend their gratitude to NASA Ames Research Center for its open access to the FLEA data.

Funding

This study is supported by the National Natural Science Foundation of China (Grant Nos. 61973011 and 61803013), the Fundamental Research Funds for the Central Universities (Grant Nos. YWF-21-BJ-J-723, YWF-21-BJ-J-517 and ZG140S1993), as well as the Capital Science & Technology Leading Talent Program (Grant No. Z191100006119029).

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Authors and Affiliations

  1. Institute of Reliability Engineering, Beihang University, Beijing, 100191, China

    Chao Wang, Laifa Tao, Chen Lu & Jian Ma

  2. Science & Technology On Reliability and Environmental Engineering Laboratory, Beijing, 100191, China

    Chao Wang, Laifa Tao, Chen Lu & Jian Ma

  3. School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China

    Chao Wang, Laifa Tao, Chen Lu & Jian Ma

  4. School of Aeronautic Science and Engineering, Beihang University, Beijing, 100191, China

    Yu Ding

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  1. Chao Wang
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  2. Laifa Tao
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Correspondence to Yu Ding.

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Wang, C., Tao, L., Ding, Y. et al. An adversarial model for electromechanical actuator fault diagnosis under nonideal data conditions. Neural Comput & Applic 34, 5883–5904 (2022). https://doi.org/10.1007/s00521-021-06732-x

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  • DOI: https://doi.org/10.1007/s00521-021-06732-x

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