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

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
Main Authors: Jun-gyo Jang, Soon-sup Lee, Se-Yun Hwang, Jae-chul Lee
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
Denoising Autoencoder
fault diagnosis
vibration signal
noise filtering
One-Class Support Vector Machine
Online Access:https://www.mdpi.com/2076-3417/15/12/6523
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