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MEMS photoacoustic sensor transformer fault diagnosis method based on GAM-MTN

Peng Jiaqi, Fang Baomin, Li Shou, Luo Jin, Zhu Xiaobin, Ma Shunqing

Diagnostyka

2025

Vol. 26, No. 2

art. no. 2025201

As the service life of electrical equipment increases, it may suffer from various faults, such as overheating, partial discharge, etc., resulting in the generation of dissolved gases in the oil. MEMS photoacoustic sensors analyze acoustic signals through photoacoustic spectroscopy and signal processing technology to detect the concentration of dissolved gases in oil. Regarding the data traits of photoacoustic sensors, this document suggests a graph mutual mapping transmission network (GAM-MTN) method. First, an improved aggregation weight graph convolutional neural network is introduced, and the node aggregation weight function is designed using the Manhattan distance metric, so that the model can dynamically adjust the aggregation weight according to the similarity between nodes during the message passing aggregation process. Secondly, the graph mutual mapping transmission network is proposed to achieve uniform spread of origin field and destination field samples through sample mapping technology, which effectively improves the overall migration effect of the model. Finally, unsupervised adaptation of the classifier and domain discriminator is utilized to enhance the generalization capability of the system. Test outcomes demonstrate that the suggested GAM-MTN network can effectively improve the learning efficiency and diagnosis accuracy of transformer fault characteristics. Compared with other advanced neural network models, the recognition accuracy is as high as 96.37%.