A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder

In modern industrial systems, establishing the optimal maintenance policy for rotating machinery is essential to improve productivity and prevent catastrophic accidents. To this end, many machinery engineers have been interested in condition-based maintenance strategies, which execute the maintenanc...

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Bibliographic Details

Published in:	Applied Sciences
Main Authors:	Hoyeon Lee, Jaehong Yu
Format:	Article
Language:	English
Published:	MDPI AG 2025-07-01
Subjects:	fault detection one-class classification rotating machinery spectrogram two-dimensional convolutional autoencoder vibration signal
Online Access:	https://www.mdpi.com/2076-3417/15/14/7698