Early fault diagnosis based on reinforcement learning optimized-SVM model with vibration-monitored signals-Reference-Cited by-同舟云学术

Early fault diagnosis based on reinforcement learning optimized-SVM model with vibration-monitored signals

Published:2023-04-03 Issue:4 Volume:35 Page:696-711
ISSN:0898-2112
Container-title:Quality Engineering
language:en
Short-container-title:Quality Engineering

Author:

Zhao Wenqin¹^ORCID,Lv Yaqiong¹^ORCID,Liu Jialun²^ORCID,Lee Carman K. M.³^ORCID,Tu Lei⁴

Affiliation:

1. School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zuhai, China

2. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, China

3. Department of Industrial and System Engineering, Hong Kong Polytechnic University, Hong Kong, China

4. Hangzhou Ren-ai Hearing Rehabilitation Research Centre, Hangzhou, China

Publisher

Informa UK Limited

Subject

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

Link

https://www.tandfonline.com/doi/pdf/10.1080/08982112.2023.2193255

Reference36 articles.

1. Sensor fault detection based on principal component analysis for interval-valued data

2. Noisy parallel hybrid model of NBGRU and NCNN architectures for remaining useful life estimation

3. Data-driven fault diagnosis based on coal-fired power plant operating data

4. A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm

5. GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction

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