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21 July 2023 Three-phase inverter fault diagnosis based on MTF-ResNet
Zewen Xie, Yucheng Chen, Wu Wang, Qunyong Han
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Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127170O (2023) https://doi.org/10.1117/12.2685381
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China
Abstract
For the problems of modeling difficulties and signal feature extraction in inverter open-circuit fault diagnosis methods, the methods based on machine learning and deep learning still have limitations in practical applications. In this paper, a three-phase inverter fault diagnosis method based on Markov variational field and residual convolutional network is proposed. The method encodes the phase voltage signal output from the inverter by MTF to generate a two-dimensional image with timing information and state migration information, avoiding the problem of signal information loss when directly converting voltage or circuit signals into one-dimensional or two-dimensional images. At the same time, the method uses ResNet to process MTF images to extract fault features of phase voltage signals and enhance the propagation of feature information so that the feature information can be fully utilized, and accurate fault classification and identification can be achieved. The experimental results show that the proposed method can effectively identify 22 open-circuit fault types of three-phase inverters with a diagnostic accuracy of 98.10%.
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Zewen Xie, Yucheng Chen, Wu Wang, and Qunyong Han "Three-phase inverter fault diagnosis based on MTF-ResNet", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127170O (21 July 2023); https://doi.org/10.1117/12.2685381
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KEYWORDS
Feature extraction
Signal to noise ratio
Circuit switching
Diagnostics
Deep learning
Education and training
Neural networks
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