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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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 24; no. 24; p. 8053
Main Authors:	Yan, Hao, Si, Xiangfeng, Liang, Jianqiang, Duan, Jian, Shi, Tielin
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01.12.2024
Subjects:	Accuracy Adaptability adaptive thresholding Artificial intelligence auto-encoder Bearings Clustering Datasets Deep learning Efficiency Embedded systems Fault diagnosis kernelized attention Machinery machinery fault detection Neural networks Systems stability unsupervised feature learning Wavelet transforms China auto-encoder kernelized attention machinery fault detection adaptive thresholding unsupervised feature learning
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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