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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Vydané v:	Applied sciences Ročník 15; číslo 12; s. 6523
Hlavní autori:	Jang, Jun-gyo, Lee, Soon-sup, Hwang, Se-Yun, Lee, Jae-chul
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.06.2025
Predmet:	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
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