Knowledge-informed deep networks for robust fault diagnosis of rolling bearings-Reference-Cited by-同舟云学术

Knowledge-informed deep networks for robust fault diagnosis of rolling bearings

Published:2024-04 Issue: Volume:244 Page:109863
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Su Yunsheng,Shi Luojie,Zhou Kai,Bai Guangxing,Wang Zequn^ORCID

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference56 articles.

1. An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis;Huang;Neurocomputing,2019

2. Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network;Cheng;Knowl Based Syst,2021

3. Low-speed bearing fault diagnosis based on improved statistical filtering and convolutional neural network;Shuuji;Measur Sci Technol,2021

4. Bearing degradation assessment and remaining useful life estimation based on Kullback-Leibler divergence and Gaussian processes regression;Kumaraswamidhas;Measurement,2021

5. Hierarchical discriminating sparse coding for weak fault feature extraction of roller bearings;Jiao;Reliab Eng Syst Saf,2019

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