A <scp>multi‐fault</scp> diagnosis method based on improved <scp>SMOTE</scp> for <scp>class‐imbalanced</scp> data-Reference-Cited by-同舟云学术

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A multi‐fault diagnosis method based on improved SMOTE for class‐imbalanced data

Published:2022-10-05 Issue:4 Volume:101 Page:1986-2001
ISSN:0008-4034
Container-title:The Canadian Journal of Chemical Engineering
language:en
Short-container-title:Can J Chem Eng

Author:

Xu Yuan¹²,Zhao Yang¹²,Ke Wei³,He Yan‐Lin¹²^ORCID,Zhu Qun‐Xiong¹²,Zhang Yang¹²,Cheng Xiaoqian¹²

Affiliation:

1. College of Information Science & Technology Beijing University of Chemical Technology Beijing China

2. Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing China

3. School of Applied Sciences Macao Polytechnic Institute Macao SAR China

Funder

China Scholarship Council

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Wiley

Subject

General Chemical Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjce.24610

Reference35 articles.

1. An optimized long short-term memory network based fault diagnosis model for chemical processes

2. Fault Diagnosis in Industrial Processes by Maximizing Pairwise Kullback–Leibler Divergence

3. Novel Pattern Recognition Using Bootstrap-Based Discriminant Locality-Preserving Projection and Its Application to Fault Diagnosis

4. Deep Focus Parallel Convolutional Neural Network for Imbalanced Classification of Machinery Fault Diagnostics

5. An Imbalance Modified Deep Neural Network With Dynamical Incremental Learning for Chemical Fault Diagnosis

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