A Study on Denoising Autoencoder Noise Selection for Improving the Fault Diagnosis Rate of Vibration Time Series Data

This study analyzes the impact of different types of random noise applied in Denoising Autoencoder (DAE) training on fault diagnosis performance, with the aim of improving noise removal for vibration time series data. While conventional studies typically train DAEs using Gaussian random noise, such...

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Bibliographic Details

Published in:	Applied sciences Vol. 15; no. 12; p. 6523
Main Authors:	Jang, Jun-gyo, Lee, Soon-sup, Hwang, Se-Yun, Lee, Jae-chul
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.06.2025
Subjects:	Accuracy Deep learning Denoising Autoencoder Fault diagnosis Gravitational waves Methods Microscopy Noise control noise filtering One-Class Support Vector Machine Signal processing Time series vibration signal Wavelet transforms
ISSN:	2076-3417, 2076-3417
Online Access:	Get full text
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