Feature Selection for Fault Diagnosis Using Principal Component Analysis-Reference-Cited by-同舟云学术

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Feature Selection for Fault Diagnosis Using Principal Component Analysis

Published:2023-06-29 Issue: Volume: Page:
ISSN:
Container-title:2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST)
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Author:

Shashoa Nasar Aldian A.¹,Jomah Omer S.M.¹,Abusaeeda Omar²,Elmezughi Abdurrezag S.³

Affiliation:

1. School of Applied Science and Engineering,Libyan Academy for Postgraduate Studies,Tripoli,Libya

2. Tripoli University,Faculty of information technology

3. Azzaytuna University,Faculty of Engineering,Tarhuna,Libya

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10187211/10187213/10187366.pdf?arnumber=10187366

Reference10 articles.

1. Fault Detection Based on Validated Model of Data Filtering Based Recursive Least Squares Algorithm For Box-Jenkins Systems

2. Recursive Least Squares Algorithm for MISO CARAMA Systems Utilizing Data Filtering

3. Principles of Correlation Analysis;gogtay;The Journal of the Association of Physicians of India,2017

4. Filtering based least squares parameter estimation algorithms for Hammerstein nonlinear CARMA systems

5. Fault Detection and Isolation;rajamani;Fault Diagnosis and Detection InTech,2017

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 Naive Bayes Classifier for Fault Detection and Classification Using Dimension Reduction Technique;2024 5th International Conference in Electronic Engineering, Information Technology & Education (EEITE);2024-05-29

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