id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ajst-30235	Liu, Taohuang; Hu, Zhihua; Zhang, Yanyang; Liu, Xiang; Liu, Sunjie; Shao, Weijian	Bearing Fault Diagnosis Method Based on SE-CNN	2025	5	.pdf	application/pdf	2723	131	47	The method enhances the network's focus on key fault features by introducing the SE module to adaptively adjust the weights of each feature channel in the convolutional network, which optimizes the feature extraction ability of the model in complex vibration signals. Traditional bearing fault diagnosis methods rely on signal processing techniques and empirical feature extraction, but in practical applications, with the change of working conditions, the signal features often appear to be mixed, which brings a greater challenge to the diagnosis.	cache/ajst-30235.pdf	txt/ajst-30235.txt