A multi-level adaptation scheme for hierarchical bearing fault diagnosis under variable working conditions-Reference-Cited by-同舟云学术

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A multi-level adaptation scheme for hierarchical bearing fault diagnosis under variable working conditions

Published:2022-07 Issue: Volume:64 Page:251-260
ISSN:0278-6125
Container-title:Journal of Manufacturing Systems
language:en
Short-container-title:Journal of Manufacturing Systems

Author:

Su Kaige,Liu Jianhua,Xiong Hui

Funder

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Hardware and Architecture,Software,Control and Systems Engineering

Reference37 articles.

1. Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines;Zhang;Mech Syst Signal Pr,2013

2. A systematic review of machine learning algorithms for prognostics and health management of rolling element bearings: fundamentals, concepts and applications;Singh;Meas Sci Technol,2020

3. Health monitoring and fault diagnosis in induction motor—a review;Siddiqui;Int J Adv Res Elect Electron Instrum Eng,2014

4. Advances in applications of hybrid intelligent fault diagnosis and prognosis technique;Lei;J Vib Shock,2011

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

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1. Bi-level binary coded fully connected classifier based on residual network 50 with bottom and deep level features for bearing fault diagnosis;Engineering Applications of Artificial Intelligence;2024-07

2. A novel incremental method for bearing fault diagnosis that continuously incorporates unknown fault types;Mechanical Systems and Signal Processing;2024-07

3. Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance;Expert Systems with Applications;2024-06

4. Rolling bearing fault diagnosis based on the fusion of sparse filtering and discriminative domain adaptation method under multi-channel data-driven;Measurement Science and Technology;2024-03-13

5. Stacked maximum independence autoencoders: A domain generalization approach for fault diagnosis under various working conditions;Mechanical Systems and Signal Processing;2024-02

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