NDLI: Fault diagnosis of rotating machinery based on SVD, FCM and RST

Content Provider	Springer Nature Link
Author	Li, Ru qiang Chen, Jin Wu, Xing Alugongo, Alfayo A.
Copyright Year	2005
Abstract	Because there is no criterion available, discretization of numerous data about objects of information systems may be the biggest obstacle to performing inductive learning from instances. Based on a Singular value decomposition of matrix and Fuzzy C-means clustering, a discrete approach of continuous attribute values in a decision table, with continuous condition attribute values and discrete decision attribute values, has been proposed. Rough sets theory has been employed for diagnostic rule acquisition of rotating machinery with consideration of conflicting objects of decision table. A weak matching mode of objects with diagnostic rules has been proposed, with diagnostic conclusion and its belief degree obtained by comparing new objects with the standard diagnostic rules. An example at the end of this paper shows that the acquired rules have good merits of the ability of generalization and extension, and to some extent, improves classification level.
Starting Page	128
Ending Page	135
Page Count	8
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	27
Issue Number	1-2
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-01-19
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Decision table Fault diagnosis Fuzzy C-means clustering Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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