Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model
Acoustic emission (AE) technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/7356189 |
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| author | Changming Liu Di Zhou Zhigang Wang Dan Yang Gangbing Song |
| author_facet | Changming Liu Di Zhou Zhigang Wang Dan Yang Gangbing Song |
| author_sort | Changming Liu |
| collection | DOAJ |
| description | Acoustic emission (AE) technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In this paper, experiments involving three-point bending tests of refractories were conducted and AE signals were collected. A new data processing method of merging the similar parameters in the description of the damage and reducing the dimension was developed. By means of the principle component analysis (PCA) for dimension reduction, the fifteen related parameters can be reduced to two parameters. The parameters were the linear combinations of the fifteen original parameters and taken as the indexes for damage classification. Based on the proposed approach, the Gaussian mixture model was integrated with the Bayesian information criterion to group the AE signals into two damage categories, which accounted for 99% of all damage. Electronic microscope scanning of the refractories verified the two types of damage. |
| format | Article |
| id | doaj-art-0468ebf1025149a98ccc4abc21bd680f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0468ebf1025149a98ccc4abc21bd680f2025-02-03T01:28:40ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/73561897356189Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture ModelChangming Liu0Di Zhou1Zhigang Wang2Dan Yang3Gangbing Song4The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology, Wuhan 430081, ChinaKey Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, ChinaMinistry of Education, Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaNational Demonstration Center for Experimental Machinery Education, Wuhan University of Science and Technology, Wuhan 430081, ChinaSmart Materials and Structures Laboratory, Department of Mechanical Engineering, University of Houston, Houston, TX 77204, USAAcoustic emission (AE) technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In this paper, experiments involving three-point bending tests of refractories were conducted and AE signals were collected. A new data processing method of merging the similar parameters in the description of the damage and reducing the dimension was developed. By means of the principle component analysis (PCA) for dimension reduction, the fifteen related parameters can be reduced to two parameters. The parameters were the linear combinations of the fifteen original parameters and taken as the indexes for damage classification. Based on the proposed approach, the Gaussian mixture model was integrated with the Bayesian information criterion to group the AE signals into two damage categories, which accounted for 99% of all damage. Electronic microscope scanning of the refractories verified the two types of damage.http://dx.doi.org/10.1155/2018/7356189 |
| spellingShingle | Changming Liu Di Zhou Zhigang Wang Dan Yang Gangbing Song Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model Complexity |
| title | Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model |
| title_full | Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model |
| title_fullStr | Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model |
| title_full_unstemmed | Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model |
| title_short | Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model |
| title_sort | damage detection of refractory based on principle component analysis and gaussian mixture model |
| url | http://dx.doi.org/10.1155/2018/7356189 |
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