MEMS photoacoustic sensor transformer fault diagnosis method based on GAM-MTN
1
Haidong Power Supply Company, State Grid Qinghai Provincial Electric Power Company, Qinghai 810699, China
Submission date: 2024-12-17
Final revision date: 2025-02-28
Acceptance date: 2025-03-11
Online publication date: 2025-03-12
Publication date: 2025-03-12
Corresponding author
Jiaqi Peng
Haidong Power Supply Company, State Grid Qinghai Provincial Electric Power Company, Qinghai 810699, China
Haidong Power Supply Company, State Grid Qinghai Provincial Electric Power Company, Qinghai 810699, China
KEYWORDS
defect detectionMEMS photoacoustic sensordissolved gas in oilgraph convolutional neural networkchart reciprocal mapping transfer web
TOPICS
Diagnostic systemsTechnical diagnosticsDiagnostics of electronic systemsDiagnostics of electrical and energy machines
ABSTRACT
MEMS photoacoustic sensors analyze acoustic signals through photoacoustic spectroscopy and signal pro-cessing 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%.
FUNDING
This research was funded by the Science and Technology Project of State Grid Qinghai Electric Power Company (522802240006).
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| eISSN: | 2449-5220 |
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