A Rough Set-Based Effective State Identification Method of Multisensor Tool Condition Monitoring System
Multisensor improves the accuracy of machine tool condition monitoring system, which provides the critical feedback information to the manufacture process controller. Multisensor monitoring system needs to collect abundant data to employ attribute extraction, election, reduction, and classification...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2014-06-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1155/2014/634107 |
| _version_ | 1818997934101889024 |
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| author | Nan Xie Lin Chen Beirong Zheng Xinfang Liu |
| author_facet | Nan Xie Lin Chen Beirong Zheng Xinfang Liu |
| author_sort | Nan Xie |
| collection | DOAJ |
| description | Multisensor improves the accuracy of machine tool condition monitoring system, which provides the critical feedback information to the manufacture process controller. Multisensor monitoring system needs to collect abundant data to employ attribute extraction, election, reduction, and classification to form the decision knowledge. A machine tool condition monitoring system has been built and the method of tool condition decision knowledge discovery is also presented. Multiple sensors include vibration, force, acoustic emission, and main spindle current. The novel approach engages rough theory as a knowledge extraction tool to work on the data that are obtained from both multisensor and machining parameters and then extracts a set of minimal state identification rules encoding the preference pattern of decision making by domain experts. By means of the knowledge acquired, the tool conditions are identified. A case study is presented to illustrate that the approach produces effective and minimal rules and provides satisfactory accuracy. |
| first_indexed | 2024-12-20T21:53:31Z |
| format | Article |
| id | doaj.art-b9b4fd571e0e4d9f82d44636c65837dd |
| institution | Directory Open Access Journal |
| issn | 1687-8132 |
| language | English |
| last_indexed | 2024-12-20T21:53:31Z |
| publishDate | 2014-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-b9b4fd571e0e4d9f82d44636c65837dd2022-12-21T19:25:29ZengSAGE PublishingAdvances in Mechanical Engineering1687-81322014-06-01610.1155/2014/63410710.1155_2014/634107A Rough Set-Based Effective State Identification Method of Multisensor Tool Condition Monitoring SystemNan Xie0Lin Chen1Beirong Zheng2Xinfang Liu3 Industrial and Systems Engineering Department, Wayne State University, Detroit, MI 48202, USA College of Computer and Information, Shanghai Second Polytechnic University, Shanghai 201209, China College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China Japan Condition Diagnostics Lab., Inc., Kitakyushu 806-0049, JapanMultisensor improves the accuracy of machine tool condition monitoring system, which provides the critical feedback information to the manufacture process controller. Multisensor monitoring system needs to collect abundant data to employ attribute extraction, election, reduction, and classification to form the decision knowledge. A machine tool condition monitoring system has been built and the method of tool condition decision knowledge discovery is also presented. Multiple sensors include vibration, force, acoustic emission, and main spindle current. The novel approach engages rough theory as a knowledge extraction tool to work on the data that are obtained from both multisensor and machining parameters and then extracts a set of minimal state identification rules encoding the preference pattern of decision making by domain experts. By means of the knowledge acquired, the tool conditions are identified. A case study is presented to illustrate that the approach produces effective and minimal rules and provides satisfactory accuracy.https://doi.org/10.1155/2014/634107 |
| spellingShingle | Nan Xie Lin Chen Beirong Zheng Xinfang Liu A Rough Set-Based Effective State Identification Method of Multisensor Tool Condition Monitoring System Advances in Mechanical Engineering |
| title | A Rough Set-Based Effective State Identification Method of Multisensor Tool Condition Monitoring System |
| title_full | A Rough Set-Based Effective State Identification Method of Multisensor Tool Condition Monitoring System |
| title_fullStr | A Rough Set-Based Effective State Identification Method of Multisensor Tool Condition Monitoring System |
| title_full_unstemmed | A Rough Set-Based Effective State Identification Method of Multisensor Tool Condition Monitoring System |
| title_short | A Rough Set-Based Effective State Identification Method of Multisensor Tool Condition Monitoring System |
| title_sort | rough set based effective state identification method of multisensor tool condition monitoring system |
| url | https://doi.org/10.1155/2014/634107 |
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