Research Article | OPEN ACCESS
Fault Diagnosis of Machine Tool Based on Rough Set
1Xie Nan, 2Xue Wei and 3Liu Xinfang
1Sino-German College of Applied Science, Tongji University, Shanghai, 201804, China
2College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou
Zhejiang 325035, China
3Japan Condition Diagnostics Lab., Inc., Kitakyushu, Japan
Research Journal of Applied Sciences, Engineering and Technology 2013 12:2209-2212
Received: December 10, 2012 | Accepted: January 17, 2013 | Published: July 30, 2013
Abstract
Fault diagnosis of machine tool plays an important role on advanced manufacturing. The correct and rapid identification of faults depends on diverse sensing data and reasonable knowledge intensively. In this paper, a method based rough set is applied to diagnose faults of machine tool during the processing and a rapid fault diagnosis system based on rough set is also proposed. The approach correctly extracts diagnosis knowledge from the data that are obtained from both sensors and inspection devices and then generates a set of minimal diagnostic rules which could be used to quickly determine the failures of mechanical process, combined with data. Furthermore an actual instance is presented to illustrate the efficiency of the method in the end.
Keywords:
Fault diagnosis, knowledge, machine tool, rough set,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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