Fault Detection for Process Industries via Temporal CapsNet Encoder-Assisted One-Class Classifier-Reference-Cited by-同舟云学术

Fault Detection for Process Industries via Temporal CapsNet Encoder-Assisted One-Class Classifier

Published:2023 Issue: Volume:72 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Wang Sheng¹^ORCID,Zhao Qiang²^ORCID,Han Yinghua³^ORCID,Wang Jinkuan¹^ORCID

Affiliation:

1. College of Information Science and Engineering, Northeastern University, Shenyang, China

2. School of Control Engineering and the Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Northeastern University at Qinhuangdao, Qinghuangdao, China

3. School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province of China

Research Fund from the State Key Laboratory of Rolling and Automation, Northeastern University

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10210303.pdf?arnumber=10210303

Reference46 articles.

1. Attention Recurrent Autoencoder Hybrid Model for Early Fault Diagnosis of Rotating Machinery

2. One Class Process Anomaly Detection Using Kernel Density Estimation Methods

3. MoniNet With Concurrent Analytics of Temporal and Spatial Information for Fault Detection in Industrial Processes

4. A Turboshaft Aeroengine Fault Detection Method Based on One-Class Support Vector Machine and Transfer Learning

5. Variational AutoEncoders-LSTM based fault detection of time-dependent high dimensional processes