Locality‐preserving data modelling and its application in fault classification-Reference-Cited by-同舟云学术

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Locality‐preserving data modelling and its application in fault classification

Published:2021-05-18 Issue: Volume: Page:
ISSN:0008-4034
Container-title:The Canadian Journal of Chemical Engineering
language:en
Short-container-title:Can J Chem Eng

Author:

Miao Aimin¹,Cheng Zhishang¹,Li Peng²,Cui Huawei³,Liu Sitong⁴,Wu Hong⁵

Affiliation:

1. College of Automation Zhongkai University of Agriculture and Engineering Guangzhou China

2. Department of Electronic Engineering, School of Information Yunnan University Kunming China

3. College of Agriculture and Biology Zhongkai University of Agriculture and Engineering Guangzhou China

4. Department of Mathematical and Computational Sciences University of Toronto Toronto Ontario Canada

5. School of Information Engineering Qujing Normal University Qujing China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Chemical Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjce.24149

Reference53 articles.

1. Online diagnostics of time-varying nonlinear chemical processes using moving window kernel principal component analysis and Fisher discriminant analysis

2. Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning

3. A multi-layered view of chemical and biochemical engineering

4. New frontiers, challenges, and opportunities in integration of design and control for enterprise-wide sustainability

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A multi‐fault diagnosis method based on improved SMOTE for class‐imbalanced data;The Canadian Journal of Chemical Engineering;2022-10-05

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