An improved ensemble fusion autoencoder model for fault diagnosis from imbalanced and incomplete data-Reference-Cited by-同舟云学术

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An improved ensemble fusion autoencoder model for fault diagnosis from imbalanced and incomplete data

Published:2020-05 Issue: Volume:98 Page:104358
ISSN:0967-0661
Container-title:Control Engineering Practice
language:en
Short-container-title:Control Engineering Practice

Author:

Yang Jing,Xie Guo,Yang Yanxi

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Key research and development plan of Shaanxi Province

Natural Science Foundation of Shaanxi Provincial Department of Education

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Reference37 articles.

1. Data-driven fault detection and diagnosis for HVAC water chillers;Beghi;Control Engineering Practice,2016

2. Parallel processing algorithm for railway signal fault diagnosis data based on cloud computing;Cao;Future Generation Computer Systems,2018

3. Enhanced random forest with concurrent analysis of static and dynamic nodes for industrial fault classification;Chai;IEEE Transactions on Industrial Informatics,2020

4. A fine-grained adversarial network method for cross-domain industrial fault diagnosis;Chai;IEEE Transactions on Automatic Sciences and Engineering,2020

5. K-means Bayes algorithm for imbalanced fault classification and big data application;Chen;Journal of Process Control,2019

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