1. Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion58, 82–115 (2020)

   Article  Google Scholar 

2. Bechhoefer, E.: A quick introduction to bearing envelope analysis. Green Power Monit. Syst. (2016)

   Google Scholar 

3. Brito, L.C., Susto, G.A., Brito, J.N., Duarte, M.A.: An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery. Mech. Syst. Signal Process.163, 108105 (2022)

   Article  Google Scholar 

4. Chen, H.Y., Lee, C.H.: Vibration signals analysis by explainable artificial intelligence (XAI) approach: application on bearing faults diagnosis. IEEE Access8, 134246–134256 (2020)

   Article  Google Scholar 

5. Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: a survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215. IEEE (2018)

   Google Scholar 

6. Fan, Y., Hamid, S., Nowaczyk, S.: Incorporating physics-based models into data-driven approaches for air leak detection in city buses. In: ECML PKDD 2022 Workshops (2022)

   Google Scholar 

7. Feldman, M.: Hilbert transforms. In: Braun, S. (ed.) Encyclopedia of Vibration, pp. 642–648. Elsevier, Oxford (2001)

   Chapter  Google Scholar 

8. Feldman, M.: Hilbert transform in vibration analysis. Mech. Syst. Signal Process.25(3), 735–802 (2011)

   Article  Google Scholar 

9. Han, D., Liang, K., Shi, P.: Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection. J. Low Freq. Noise Vib. Active Control39(4), 939–953 (2020)

   Article  Google Scholar 

10. Holzinger, A., Saranti, A., Molnar, C., Biecek, P., Samek, W.: Explainable AI methods - a brief overview. In: Holzinger, A., Goebel, R., Fong, R., Moon, T., Müller, K.R., Samek, W. (eds.) xxAI 2020. LNCS, pp. 13–38. Springer, Cham (2022).https://doi.org/10.1007/978-3-031-04083-2_2

    Chapter  Google Scholar 

11. Lee, D.H., Hong, C., Jeong, W.B., Ahn, S.: Time-frequency envelope analysis for fault detection of rotating machinery signals with impulsive noise. Appl. Sci.11(12), 5373 (2021)

    Article  Google Scholar 

12. Lee, J.S., Yoon, T.M., Lee, K.B.: Bearing fault detection of IPMSMs using zoom FFT. J. Electr. Eng. Technol.11(5), 1235–1241 (2016)

    Article  Google Scholar 

13. Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., Nandi, A.K.: Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech. Syst. Signal Process.138, 106587 (2020)

    Article  Google Scholar 

14. Lessmeier, C., Kimotho, J.K., Zimmer, D., Sextro, W.: Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: a benchmark data set for data-driven classification. In: PHM Society European Conference, vol. 3 (2016)

    Google Scholar 

15. Li, C., Zhang, W., Peng, G., Liu, S.: Bearing fault diagnosis using fully-connected winner-take-all autoencoder. IEEE Access6, 6103–6115 (2017)

    Article  Google Scholar 

16. Liu, Y.: Fault diagnosis based on SWPT and Hilbert transform. Procedia Eng.15, 3881–3885 (2011)

    Article  Google Scholar 

17. Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30, pp. 4765–4774. Curran Associates, Inc. (2017)

    Google Scholar 

18. Meng, Z., Zhan, X., Li, J., Pan, Z.: An enhancement denoising autoencoder for rolling bearing fault diagnosis. Measurement130, 448–454 (2018)

    Article  Google Scholar 

19. Mey, O., Neufeld, D.: Explainable AI algorithms for vibration data-based fault detection: use case-adapted methods and critical evaluation. arXiv preprintarXiv:2207.10732 (2022)

20. Rajabi, S., Azari, M.S., Santini, S., Flammini, F.: Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier. Expert Syst. Appl.206 (2022)

    Google Scholar 

21. Rudin, C., Radin, J.: Why are we using black box models in AI when we don’t need to? A lesson from an explainable AI competition. Harvard Data Sci. Rev.1(2) (2019).https://hdsr.mitpress.mit.edu/pub/f9kuryi8

22. Wang, N., Liu, X.: Bearing fault diagnosis method based on Hilbert envelope demodulation analysis. In: IOP Conference Series: Materials Science and Engineering, vol. 436, p. 012009. IOP Publishing (2018)

    Google Scholar 

23. Xia, M., Li, T., Liu, L., Xu, L., de Silva, C.W.: Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder. IET Sci. Measur. Technol.11(6), 687–695 (2017)

    Article  Google Scholar 

Download references