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Rough Sets and Rule Induction from Indiscernibility Relations Based on Possible World Semantics in Incomplete Information Systems with Continuous Domains Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Granules-Based Rough Set Theory for Circuit Breaker Fault Diagnosis

Authors : Rizk M. Rizk-Allah, Aboul Ella Hassanien

Published in: Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Publisher: Springer International Publishing

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Abstract

This chapter presents a new granules strategy integrated with rough set theory (RST) to extract diagnosis rules for redundant and inconsistent data set of high voltage circuit breaker (HVCB). In this approach, the diagnostic knowledge base is performed by the granules of indiscernible objects based on tolerance relation in which the objects are collected based on permissible scheme. This permissible scheme is decided by the opinion of the expert or the decision maker. In addition, a topological vision is introduced to induce the lower and upper approximations. Finally, the validation and effectiveness of the proposed granules strategy are investigated through a practical application of the high voltage circuit breaker fault diagnosis.

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Metadata
Title
Granules-Based Rough Set Theory for Circuit Breaker Fault Diagnosis
Authors
Rizk M. Rizk-Allah
Aboul Ella Hassanien
Copyright Year
2021
Book
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Print ISBN: 978-3-030-59337-7

Electronic ISBN: 978-3-030-59338-4

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-59338-4_5

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