Abstract
It is difficult to comprehensively extract device status information for CNNs under a single source high-frequency timing signal, and CNNs cannot effectively achieve precise identification and classification based on the importance of multichannel features. This article proposes a CNN fault diagnosis method based on multi -channel weight adaptation. This method first normalizes different data sources as input as different channels of CNN, and uses the characteristics of convolutional networks to achieve the characteristics of different data sources. Fusion and extraction. Then, the SNET module is embedded into the CNN network, adapted to the weight of each channel, and the accuracy of classification is improved. Finally, through comparative experiments, this method can further improve the accuracy of fault recognition.
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Acknowledgements
This work is financially supported by: The National Key R&D Program of China (No. 2020YFB1712600); The Fundamental Research Funds for Central University (No. 3072022QBZ0601); and The National Natural Science Foundation of China (No. 62272126).
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Xu, P., Liu, X., Lin, J., Lu, Z., Li, F., Gou, H. (2023). Multiple-Channel Weight-Based CNN Fault Diagnosis Method. In: Yu, Z., et al. Data Science. ICPCSEE 2023. Communications in Computer and Information Science, vol 1879. Springer, Singapore. https://doi.org/10.1007/978-981-99-5968-6_8
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DOI: https://doi.org/10.1007/978-981-99-5968-6_8
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