Yuejin Huang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yuejin Huang has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Huaqiang Jin
College of Education, Zhejiang University of Technology, Hangzhou, China
1 shared publication - Jiangping Gu
School of Education, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou 310023, China
1 shared publication - Kang Li
School of Mechanical Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou 310023, China
1 shared publication - Qinjian Zhang
Joint Laboratory of Refrigeration Compressor Reliability Evaluation, Zhejiang University of Technology, Ltd., 288 Liuhe Road, Hangzhou 310023, China
1 shared publication - Xi Shen
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
1 shared publication - Yingjie Xu
School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
1 shared publication
Latest Publications
- Open Access
- 16 March 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (6) , 3033
Proposal and Experimental Study on a Diagnosis Method for Hermetic Refrigeration Compressor Using Dual Time-Frequency Image Fusion
The hermetic refrigeration compressor is the core component of the refrigeration system, failure of which will cause energy waste and reduce service life. Fault diagnosis based on vibration signal is a research hotspot. However, it is challenging to extract features of nonlinear and nonstationary vibration signals, which severely restricts the development of this method. This paper proposes a dual time-frequency images fusion method to obtain more effective features for diagnosing compressor manufacturing defects. Firstly, two time-frequency images are obtained by implementing continuous wavelet transform and Hilbert-Huang transform of the same vibration signal sample. Then, a convolutional neural network is used for image feature extraction and fusion, where the features extracted from two time-frequency images have complementarity. A data set containing six categories of typical manufacturing defects is used to verify the proposed method. The results show that the average diagnostic accuracy of the proposed method reaches 95.9%, and the proposed method has a better performance than other methods.
- 16 March 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (6) , 3033
Open AccessProposal and Experimental Study on a Diagnosis Method for Hermetic Refrigeration Compressor Using Dual Time-Frequency Image Fusion
The hermetic refrigeration compressor is the core component of the refrigeration system, failure of which will cause energy waste and reduce service life. Fault diagnosis based on vibration signal is a research hotspot. However, it is challenging to extract features of nonlinear and nonstationary vibration signals, which severely restricts the development of this method. This paper proposes a dual time-frequency images fusion method to obtain more effective features for diagnosing compressor manufacturing defects. Firstly, two time-frequency images are obtained by implementing continuous wavelet transform and Hilbert-Huang transform of the same vibration signal sample. Then, a convolutional neural network is used for image feature extraction and fusion, where the features extracted from two time-frequency images have complementarity. A data set containing six categories of typical manufacturing defects is used to verify the proposed method. The results show that the average diagnostic accuracy of the proposed method reaches 95.9%, and the proposed method has a better performance than other methods.