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: | Zhu, Jinlin, Jiang, Muyun, Liu, Zhong |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/161306 |
Similar Items
-
Deep heterogeneous autoencoders for Collaborative Filtering
by: Li, Tianyu, et al.
Published: (2020) -
Effect of nanostructures orientation on electroosmotic flow in a microfluidic channel
by: Lim, An Eng, et al.
Published: (2017) -
A stacked autoencoder neural network based automated feature extraction method for anomaly detection in on-line condition monitoring
by: Roy, Mohendra, et al.
Published: (2019) -
Semi-Amortized Variational Autoencoders
by: Kim, Yoon, et al.
Published: (2022) -
Semi-Amortized Variational Autoencoders
by: Kim, Yoon, et al.
Published: (2021)