NDLI: RMINE: A Rough Set Based Data Mining Prototype for the Reasoning of Incomplete Data in Condition-based Fault Diagnosis

Content Provider	Springer Nature Link
Author	Li, Jing Rong Khoo, Li Pheng Tor, Shu Beng
Copyright Year	2006
Abstract	Condition-based fault diagnosis aims at identifying the root cause of a system malfunction from vast amount of condition-based monitoring information and knowledge. The needs of extracting knowledge from vast amount of information have spurred the interest in data mining, which can be categorized into two stages data preparation and knowledge extraction. It has been established that most of the current data mining approaches to fault diagnosis focus on the latter stage. In reality, condition-based monitoring data may, most of the time, contain incomplete, or missing data, which have an adverse effect on the knowledge or diagnostic rules extracted. Several approaches to deal with missing data can be found in literature. Unfortunately, all of which have serious drawbacks. In this paper, a novel approach to the treatment of incomplete data is proposed for the data preparation stage, while a rough set based approach has been developed to pre-process the data for the extraction of diagnostic rules. The two-stage data mining technique is implemented into a prototype system, RMINE, which also possesses a self-learning ability to cope with the changing condition-based data. A real industrial case study of a pump system is used to demonstrate the fault diagnosis process using RMINE. The result has shown the potential of RMINE as a general data mining prototype to condition-based fault diagnosis.
Starting Page	163
Ending Page	176
Page Count	14
File Format	PDF
ISSN	09565515
Journal	Journal of Intelligent Manufacturing
Volume Number	17
Issue Number	1
e-ISSN	15728145
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2006-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Incomplete data rough set theory data mining fault diagnosis Manufacturing, Machines, Tools Automation and Robotics Production/Logistics
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Artificial Intelligence Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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