Unsupervised Learning for Machinery Adaptive Fault Detection Using Wide-Deep Convolutional Autoencoder with Kernelized Attention Mechanism

Applying deep learning to unsupervised bearing fault diagnosis in complex industrial environments is challenging. Traditional fault detection methods rely on labeled data, which is costly and labor-intensive to obtain. This paper proposes a novel unsupervised approach, WDCAE-LKA, combining a wide ke...

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書誌詳細

出版年:	Sensors
主要な著者:	Hao Yan, Xiangfeng Si, Jianqiang Liang, Jian Duan, Tielin Shi
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2024-12-01
主題:	unsupervised feature learning machinery fault detection auto-encoder kernelized attention adaptive thresholding
オンライン･アクセス:	https://www.mdpi.com/1424-8220/24/24/8053