Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study
This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been...
Main Authors: | Jinlin Zhu, Muyun Jiang, Zhong Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-12-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/22/1/227 |
Similar Items
-
Fault detection and diagnosis in industrial processes with variational autoencoder: a comprehensive study
by: Zhu, Jinlin, et al.
Published: (2022) -
Conditional Variational Autoencoder for Learned Image Reconstruction
by: Chen Zhang, et al.
Published: (2021-10-01) -
Explore Protein Conformational Space With Variational Autoencoder
by: Hao Tian, et al.
Published: (2021-11-01) -
Classical Music Prediction and Composition by Means of Variational Autoencoders
by: Daniel Rivero, et al.
Published: (2020-04-01) -
Seismic labeled data expansion using variational autoencoders
by: Kunhong Li, et al.
Published: (2020-12-01)