Ruchun Zhao - IEEE Xplore Author Profile
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Ruchun Zhao

Publications
2
Citations
7
Publications by Year
20232024
Co-Authors:
Image of author Ruchun Zhao

Ruchun Zhao

Affiliation

School of Electrical Engineering
Yanshan University
Qinhuangdao, China

Publication Topics

Biography

Ruchun Zhao received the B.S. degree in measuring and control technology and instrumentation from Yanshan University, Qinhuangdao, China, in 2020, where he is currently pursuing the M.S. degree in instrumentation and meter engineering with the School of Electrical Engineering.,He is currently an Associate Professor with the School of Electrical Engineering, Yanshan University. His research interests include multimodal fusion and its application to machine fault diagnosis.(Based on document published on 8 February 2024).
Publications
2
Citations
7
Publications by Year
20232024
Co-Authors:
Author's Published Works

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Current-Aided Vibration Fusion Network for Fault Diagnosis in Electromechanical Drive System

Ruchun Zhao;Guoqian Jiang;Qun He;Xiaohang Jin;Ping Xie

IEEE Transactions on Instrumentation and Measurement
Year: 2024 | Volume: 73 | Journal Article |
Cited by: Papers (6)
Traditional fault diagnosis methods mainly rely on a single sensor signal, such as vibration or generator current signals, thus it often leads to limited diagnosis accuracy, primarily when multiple faults exist at the same time. Considering the electromechanical coupling characteristics of the electromechanical drive system, different sensors usually contain correlated and complementary informatio...Show More

Current-Aided Vibration Fusion Network for Fault Diagnosis in Electromechanical Drive System

Ruchun Zhao;Guoqian Jiang;Qun He;Xiaohang Jin;Ping Xie

Year: 2024 | Volume: 73 | Journal Article |

Differential-Augmented Current Feature Learning Network With Multi-Information Interaction for Fault Diagnosis in Electromechanical Drive System

Qun He;Ruchun Zhao;Guoqian Jiang;Ping Xie

IEEE Sensors Journal
Year: 2023 | Volume: 23, Issue: 14 | Journal Article |
Cited by: Papers (1)
Current-based fault diagnosis has become a promising solution for electromechanical systems due to the low cost and easy access. However, most of the existing studies require additional signal processing or diagnostic expertise to preprocess the signals due to the effects of the fundamental component and electrical noise. It is challenging to extract effective fault-related features from the raw c...Show More

Differential-Augmented Current Feature Learning Network With Multi-Information Interaction for Fault Diagnosis in Electromechanical Drive System

Qun He;Ruchun Zhao;Guoqian Jiang;Ping Xie

Year: 2023 | Volume: 23, Issue: 14 | Journal Article |

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