Dirichlet probability navigated fault detection via key-group memory auto-encoder under non-stationary working conditions-Reference-Cited by-同舟云学术

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Dirichlet probability navigated fault detection via key-group memory auto-encoder under non-stationary working conditions

Published:2024-06 Issue: Volume:670 Page:120635
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Weng De-Yu,Zhu Jun-Wei^ORCID,Xuan Qi

Publisher

Elsevier BV

Reference50 articles.

1. Applications of machine learning to machine fault diagnosis: a review and roadmap;Lei;Mech. Syst. Signal Process.,2020

2. Deep-learning-based Earth fault detection using continuous wavelet transform and convolutional neural network in resonant grounding distribution systems;Guo;IEEE Sens. J.,2018

3. Warped variational mode decomposition with application to vibration signals of varying-speed rotating machineries;Chen;IEEE Trans. Instrum. Meas.,2019

4. Application of a coarse-to-fine minimum entropy deconvolution method for rotating machines fault detection;Miao;Mech. Syst. Signal Process.,2023

5. Autocorrelated envelopes for early fault detection of rolling bearings;Xu;Mech. Syst. Signal Process.,2021

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