Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model
Joint Authors
Wang, Zhigang
Liu, Changming
Zhou, Di
Yang, Dan
Song, Gang-Bing
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-31
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liu, Changming& Zhou, Di& Wang, Zhigang& Yang, Dan& Song, Gang-Bing. 2018. Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model. Complexity،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1135756
Modern Language Association (MLA)
Liu, Changming…[et al.]. Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model. Complexity No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1135756
American Medical Association (AMA)
Liu, Changming& Zhou, Di& Wang, Zhigang& Yang, Dan& Song, Gang-Bing. Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model. Complexity. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1135756
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1135756