A novel deep output kernel learning method for bearing fault structural diagnosis-Reference-Cited by-同舟云学术

A novel deep output kernel learning method for bearing fault structural diagnosis

Published:2019-02 Issue: Volume:117 Page:293-318
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Mao Wentao,Feng Wushi,Liang Xihui^ORCID

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

University Science & Technology Innovation in Henan Province

Henan Normal University for Excellent Young Teachers

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference40 articles.

1. Fault level diagnosis for planetary gearboxes using hybrid kernel feature selection and kernel Fisher discriminant analysis;Liu;Int. J. Adv. Manuf.Tech.,2013

2. A review on empirical mode decomposition in fault diagnosis of rotating machinery;Lei;Mech. Syst. Signal Process.,2013

3. Bearing fault diagnosis based on statistical locally linear embedding;Wang;Sensors,2015

4. A hybrid approach to fault diagnosis of roller bearings under variable speed conditions;Wang;Meas. Sci. Technol.,2017

5. New clustering algorithm-based fault diagnosis using compensation distance evaluation technique;Lei;Mech. Syst. Signal Process.,2008

Cited by 107 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Transferring damage detection knowledge across rotating machines and framed structures: Harnessing domain adaptation and contrastive learning;Mechanical Systems and Signal Processing;2024-12

2. Extreme pressure coefficients: modelling a hydraulic jump using deep-learning based methods;Sādhanā;2024-04-25

3. Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder;Applied Sciences;2024-02-07

4. Dynamic identification of coupler yaw angle of heavy haul locomotive: An optimal multi-task ELM-based method;Mechanical Systems and Signal Processing;2024-02

5. Transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment;Measurement Science and Technology;2024-01-25