Research on Vibration Data-Driven Fault Diagnosis for Iron Core Looseness of Saturable Reactor in UHVDC Thyristor Valve Based on CVAE-GAN and Multimodal Feature Integrated CNN

• Published in: Energies (Basel) Vol. 15; no. 24; p. 9508
• Main Authors: Zhang, Xiaolong, Wei, Xiaoguang, Zheng, Lin, Wang, Chenghao, Wang, Huafeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2022
• Subjects: Analysis; Artificial intelligence; data imbalance; Datasets; Deep learning; Design; Engineering; Fault diagnosis; Fourier transforms; Frequency spectrum; Image classification; Iron; iron core looseness; Liquors; Machine learning; Neural networks; Reactors; saturable reactor; Saturable reactors; Thyristors; UHVDC thyristor valve; Valves; variable operating conditions; Venus; Vibration; Wavelet transforms; Working conditions
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15249508

The imbalance of data samples and fluctuating operating conditions are the two main challenges faced by vibration data-driven fault diagnosis for the iron core looseness of saturable reactors in UHVDC thyristor valves. This paper proposes a vibration data-driven saturable reactor iron core looseness fault diagnosis strategy named CVG-MFICNN based on CVAE-GAN and MFICNN to overcome the two challenges. This strategy uses a novel 1-D CVAE-GAN model to produce generated samples and expand the training set based on imbalanced training samples. An MFICNN model structure is designed to allow the simultaneous processing of multimodal features such as the SST time-frequency spectrum, time-domain vibration sequence, frequency-domain power spectrum sequence, and time-domain statistics. Using these multimodal features and the MFICNN model, the hidden fault information in vibration data can be effectively mined. An experiment is conducted to collect vibration data of saturable reactors with different faults. Models based on the proposed strategy and other methods are trained and tested using the collected data. The comparison results show that the performance of the proposed CVG-MFICNN approach is significantly superior to that of single-feature CNNs, traditional machine learning methods, and classical image classification CNNs in the application of UHVDC thyristor valve saturable reactor iron core looseness fault diagnosis.