Reliable bearing fault diagnosis using Bayesian inference-based multi-class support vector machines-Reference-Cited by-同舟云学术

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Reliable bearing fault diagnosis using Bayesian inference-based multi-class support vector machines

Published:2017-02 Issue:2 Volume:141 Page:EL89-EL95
ISSN:0001-4966
Container-title:The Journal of the Acoustical Society of America
language:en
Short-container-title:J. Acoust. Soc. Am.

Author:

Islam M. M. Manjurul¹,Kim Jaeyoung¹,Khan Sheraz A.¹,Kim Jong-Myon¹

Affiliation:

1. School of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan, South Korea m.m.manjurul@gmail.com, kjy7079@gmail.com, sherazalik@gmail.com, jongmyon.kim@gmail.com

Publisher

Acoustical Society of America (ASA)

Subject

Acoustics and Ultrasonics,Arts and Humanities (miscellaneous)

Cited by 31 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. Fault diagnosis models based on cross machine field transfer;Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis;2024

2. Few-Shot Bearing Fault Diagnosis Based on Multi-Metric Fusion Learning;2023 China Automation Congress (CAC);2023-11-17

3. Principal component analysis approach for detecting faults in rotary machines based on vibrational and electrical fused data;Mechanical Systems and Signal Processing;2023-10

4. A novel method for bearing fault diagnosis based on BiLSTM neural networks;The International Journal of Advanced Manufacturing Technology;2023-01-09

5. Deep regularized variational autoencoder for intelligent fault diagnosis of rotor–bearing system within entire life-cycle process;Knowledge-Based Systems;2021-08

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