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Performance Evaluation of the Signal Processing Based Transfer Learning Algorithm for the Fault Classification at Different Datasets

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Abstract

The detection of rolling bearing faults of rotating machines is very important in reducing production losses, financial losses, and accidents in the manufacturing industry. Therefore, various methods have been developed so far for the detection of rolling bearing faults. Recently, transfer learning-based methods are getting popularity in the area of artificial intelligence for this purpose. In this study, the performance of a transfer learning algorithm for fault classification using scalogram images has been studied at different load conditions of the collected dataset, size of the dataset, and training–testing ratio of the dataset for the benefit of future researchers.

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References

  1. M. Hakim, A.A.B. Omran, A.N. Ahmed, M. Al-Waily, A. Abdellatif, 2022. A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: taxonomy, overview, application, open challenges, weaknesses and recommendations. Ain Shams Eng. J., p.101945

  2. M.S. Rathore, S.P. Harsha, Roller bearing failure analysis using gaussian mixture models and convolutional neural networks. J Fail. Anal. Preven. 22, 1853–1871 (2022). https://doi.org/10.1007/s11668-022-01469-8

    Article  Google Scholar 

  3. G. Ciaburro, Machine fault detection methods based on machine learning algorithms: a review. Math. Biosci. Eng. 19(11), 11453–11490 (2022)

    Article  Google Scholar 

  4. H. Wang, J. Chen, G. Dong, Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform. Mech. Syst. Signal Process. 48(1–2), 103–119 (2014)

    Article  CAS  Google Scholar 

  5. F. Jiang, Z. Zhu, W. Li, G. Zhou, G. Chen, Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis. J. Sound Vib. 333(14), 3321–3331 (2014)

    Article  Google Scholar 

  6. Y. Yang, Z. Peng, W. Zhang, G. Meng, Parameterised time-frequency analysis methods and their engineering applications: a review of recent advances. Mech. Syst. Signal Process. 119, 182–221 (2019)

    Article  Google Scholar 

  7. I.H. Sarker, Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci. 2, 420 (2021). https://doi.org/10.1007/s42979-021-00815-1

    Article  Google Scholar 

  8. Y. Xie, T. Zhang, Feature extraction based on DWT and CNN for rotating machinery fault diagnosis. In 2017 29th Chinese Control and Decision Conference (CCDC), pp. 3861–3866. IEEE, 2017

  9. X. Zhang, Y. Liang, J. Zhou, A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement. 69, 164–179 (2015)

    Article  Google Scholar 

  10. T.H. Koornwinder, The continuous wavelet transform, in Wavelets: An Elementary Treatment of Theory and Applications. ed. by T.H. Koornwinder (University of Amsterdam, Amsterdam, 1993)

    Chapter  Google Scholar 

  11. K.M. Hosny, M.A. Kassem, M.M. Foaud, Classification of skin lesions using transfer learning and augmentation with Alex-net. PLoS ONE. 14(5), e0217293 (2019)

    Article  CAS  Google Scholar 

  12. A. Almisreb Ali, N. Jamil, N. Md Din. Utilizing AlexNet deep transfer learning for ear recognition. In 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP), pp. 1–5. IEEE, 2018.

  13. Case Western Reserve University Bearing Data Center. Accessed: June. 20, 2021. [Online]. Available:http://csegroups.case.edu/bearingdatacenter/pages/download-data-file, http://csegroups.case.edu/bearingdatacenter/pages/download-data-file

  14. B. Adel, A. Moussaoui, A. Dahane, I. Atoui, A comparative study of various methods of bearing faults diagnosis using the case Western Reserve University data. J. Fail. Anal. Prev. 16(2), 271–284 (2016)

    Article  Google Scholar 

  15. D. Djamel, R. Laidi, Y. Djenouri, I. Balasingham, Machine learning for smart building applications: review and taxonomy. ACM Comput. Surv. 52(2), 1–36 (2019)

    Google Scholar 

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  1. Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Sunil Datt Sharma

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  1. Sunil Datt Sharma
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Correspondence to Sunil Datt Sharma.

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Sharma, S.D. Performance Evaluation of the Signal Processing Based Transfer Learning Algorithm for the Fault Classification at Different Datasets. J Fail. Anal. and Preven. 23, 1081–1091 (2023). https://doi.org/10.1007/s11668-023-01648-1

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  • DOI: https://doi.org/10.1007/s11668-023-01648-1

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