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Fault diagnosis of aeroengine fan based on generative adversarial network and acoustic features

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Abstract

Aeroengine fan is an important component of aeroengine, and its reliability is very important for aircraft. Therefore, the fault diagnosis and fault analysis of aeroengine fan are of great significance to aircraft safety. This paper proposes a fault diagnosis method of aeroengine fan based on generative adversarial network and acoustic features. First, referring to Mel frequency cepstral coefficients, the features of the collected original signal are extracted, and the first-order and second-order difference parameters to form a three-dimensional feature vector are also extracted. Then, the neural network model is used to build generator and discriminator, and the training is carried out through a generative adversarial network model. Finally, the public rotating machinery data set is used to construct training set and test set to verify the recognition effect of the model. Compared with the recognition results of the model using the same neural network architecture and the same data set, it is verified that the model has different degrees of improvement in terms of training efficiency, robustness and accuracy. Using data sequences at different speeds, it is verified that the model is stable in various states of aeroengine.

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References

  1. Wang B, Zhong WB (2019) Technology research on aviation fault diagnosis and health management. Modern Navig 10(06):454–457

    Google Scholar 

  2. Chen G (2014) Structural design and analysis of aeroengine. Beihang University Press, China

    Google Scholar 

  3. Zhou J, Liu JH, Yang T et al (2015) Development of aero-engine online vibration monitoring system. Comput Measur Control 23(11):3599–3602

    Google Scholar 

  4. Geng QS, Wang FH, Jin X et al (2020) Sound diagnosis of mechanical fault of dry-type transformer based on Gammatone filter cepstral coefficient and whale algorithm optimization random forest. Elect Power Automat Equipm 40(08):191–196

    Google Scholar 

  5. Li B, Chow MY, Tipsuwan Y et al (2000) Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans Ind Electron 47(5):1060–1069

    Article  Google Scholar 

  6. Yin Z, Hou J (2016) Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes. Neurocomputing 174:643–650

    Article  Google Scholar 

  7. Goodfellow I, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial nets (Advances in neural information processing systems). Red Hook, NY Curran 27:2672–2680

    Google Scholar 

  8. Fu Q, Wang H, Zhao J, et al (2019) A maintenance-prediction method for aircraft engines using generative adversarial networks. In: IEEE 5th International Conference on Computer and Communications (ICCC). IEEE, 2019: 225–229

  9. Wu C, Zeng Z (2021) A fault diagnosis method based on auxiliary classifier generative adversarial network for rolling bearing. PLoS One 16(3):e0246905

    Article  Google Scholar 

  10. Wang C, Sun H, Zhao R et al (2020) Research on bearing fault diagnosis method based on an adaptive anti-noise network under long time series. Sensors 20(24):7031

    Article  Google Scholar 

  11. Muda L, Begam M, Elamvazuthi I (2010) Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques. arXiv preprint arXiv:1003.4083

  12. Xu MY, Li J, Sun HW et al (2020) Mechanical fault diagnosis method of GIS based on improved MFCC. High Volt Appar 56(9):7

    Google Scholar 

  13. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press

  14. Smith WA, Randall RB (2015) Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mech Syst Signal Process 64:100–131

    Article  Google Scholar 

  15. Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425

    Article  Google Scholar 

  16. Pestana-Viana D, Zambrano-López R, de Lima AA et al (2016) The influence of feature vector on the classification of mechanical faults using neural networks. IEEE 7th Latin American Symposium on Circuits and Systems (LASCAS). IEEE 2016:115–118

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grand No. 51506121).

Funding

The funding has been received from National Science and Technology Major Project of China with Grant no. 2017-II-003-0015.

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, People’s Republic of China

    Haoyuan Dong & Wei Ma

  2. Shanghai KeyGo Technology Company Limited, Shanghai, People’s Republic of China

    Liu Xun

Authors
  1. Haoyuan Dong
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  2. Liu Xun
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  3. Wei Ma
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Corresponding author

Correspondence to Wei Ma.

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The authors have not disclosed any competing interests.

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Dong, H., Xun, L. & Ma, W. Fault diagnosis of aeroengine fan based on generative adversarial network and acoustic features. AS 5, 567–575 (2022). https://doi.org/10.1007/s42401-022-00151-z

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  • DOI: https://doi.org/10.1007/s42401-022-00151-z

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