Wise-local response convolutional neural network based on Naïve Bayes theorem for rotating machinery fault classification-Reference-Cited by-同舟云学术

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Wise-local response convolutional neural network based on Naïve Bayes theorem for rotating machinery fault classification

Published:2021-02-18 Issue:10 Volume:51 Page:6932-6950
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Aljemely Anas H.,Xuan Jianping,Xu Long,Jawad Farqad K. J.,Al-Azzawi Osama

Funder

the National Key R&D Program of China

the National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-021-02252-2.pdf

Reference46 articles.

1. Jiang H, Li C, Li H (2013) An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis. Mech Syst Signal Process 36(2):225–239. https://doi.org/10.1016/j.ymssp.2012.12.010

2. Li Y, Liang X, Zuo MJ (2017) Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis. Mech Syst Signal Process 85:146–161. https://doi.org/10.1016/j.ymssp.2016.08.019

3. Jia F, Lei Y, Lin J, Zhou X, Lu N (2016) Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Process 72–73:303–315. https://doi.org/10.1016/j.ymssp.2015.10.025

4. Wang H, Chen P (2011) Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network. Comput Ind Eng 60(4):511–518. https://doi.org/10.1016/j.cie.2010.12.004

5. Zimroz R, Bartelmus W, Barszcz T, Urbanek J (2014) Diagnostics of bearings in presence of strong operating conditions non-stationarity—a procedure of load-dependent features processing with application to wind turbine bearings. Mech Syst Signal Process 46(1):16–27. https://doi.org/10.1016/j.ymssp.2013.09.010

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