ConvLSTM and Self-Attention Aided Canonical Correlation Analysis for Multioutput Soft Sensor Modeling-Reference-Cited by-同舟云学术

ConvLSTM and Self-Attention Aided Canonical Correlation Analysis for Multioutput Soft Sensor Modeling

Published:2023 Issue: Volume:72 Page:1-10
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhu Xiuli¹^ORCID,Damarla Seshu Kumar²^ORCID,Hao Kuangrong³^ORCID,Huang Biao²^ORCID,Chen Hongtian²^ORCID,Hua Yicun³

Affiliation:

1. College of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China

2. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada

3. College of Information Sciences and Technology, and the Engineering Research Center of Digitized Textile and Apparel Technology, Donghua University, Shanghai, China

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

CSC Scholarship for the visit to the University of Alberta

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10021983.pdf?arnumber=10021983

Reference80 articles.

1. Supervised Diagnosis of Quality and Process Faults with Canonical Correlation Analysis

2. Comparison of Soft-Sensor Design Methods for Industrial Plants Using Small Data Sets

3. A Single-Side Neural Network-Aided Canonical Correlation Analysis With Applications to Fault Diagnosis

4. Dynamic Soft Sensor Development Based on Convolutional Neural Networks

5. Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives

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