Bayes-DCGRU with bayesian optimization for rolling bearing fault diagnosis
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-021-02924-z.pdf
Reference27 articles.
1. Xie P, Lin H, Liu Y (2017) Principles and Applications of Signal Processing, vol 1. Tsinghua University Press
2. El-Thalji I, Jantunen E (2015) A summary of fault modelling and predictive health monitoring of rolling element bearings. Mech Syst Signal Process 60:252–272
3. Ding X, He Q (2017) Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis. IEEE Trans Instrum Measur PP(8):1–10
4. Fan X, Liang M, Yeap TH, Kind B (2007) A joint wavelet lifting and independent component analysis approach to fault detection of rolling element bearings. Smart Mater Struct 16(5):1973
5. Chen C, Li Z, Yang J, Liang B (2017) A cross domain feature extraction method based on transfer component analysis for rolling bearing fault diagnosis. In: 2017 29Th chinese control and decision conference (CCDC)
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