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Bearing fault diagnosis in high noise environment using multi-scale processing, channel-attention and feature-enhanced convolutional neural network model

This paper presents a model using deep learning techniques which includes Multi-scale processing, Channel attention, Feature enhancement, and anomaly Classification layers, referred to as MCFCNN, for bearing fault diagnosis in noisy industrial environments. The MCFCNN network combines multi-channel...

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書誌詳細
主要な著者:	Xiuyu, Li, Shirley Johnathan, Tanjong
フォーマット:	論文
言語:	English
出版事項:	Learning Gate 2025
主題:	T Technology (General) TJ Mechanical engineering and machinery
オンライン･アクセス:	http://ir.unimas.my/id/eprint/47773/1/3601-EAST20259%282%292132-2146.pdf http://ir.unimas.my/id/eprint/47773/ https://learning-gate.com/index.php/2576-8484/article/view/5050 https://doi.org/10.55214/25768484.v9i2.5050
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