Steel plate fault diagnosis based on an integration of one-against-one strategy and support vector machines
Abstract: 53
| 
PDF: 34 
##plugins.themes.academic_pro.article.main##
Author
Keywords:
one-against-one
Fault diagnosis
support vector machine
steel plates
classification accuracy
Abstract
Fault diagnosis has been a critical issue in industrial production over years. An effective fault diagnosis system enhances the quality of manufacturing and reduces the cost of product testing. This paper proposes an integration of one-against-one (OAO) strategy and support vector machines (SVM) to diagnose multiple faults of steel plates. The OAO is adopted to address multi-classification tasks in the binary SVM (i.e, OAO-SVMs). The performance of the proposed model is compared with that of optimization algorithm-based SVM. Analytical results indicate that the OAO-SVM outperforms other comparative models in fault diagnosis with an accuracy up to 86.357%. The findings of this paper, therefore, show a potential combination of an OAO strategy and an SVM in sorting common faults of steel plates in particular and industrial products in general.
References
-
[1] M. Perzyk, A. Kochanski, J. Kozlowski, A. Soroczynski, R. Biernacki, Comparison of data mining tools for significance analysis of process parameters in applications to process fault diagnosis, Information Sciences 259 (2014) 380-392.
[2] S. Jain, C. Azad, V.K. Jha, Steel faults diagnosis using predictive analysis, International Journal of Computer Engineering and Applications IV(II/III) (2013) 69-78.
[3] H. Dong, Z. Wang, H. Gao, Fault Detection for Markovian Jump Systems With Sensor Saturations and Randomly Varying Nonlinearities, IEEE Transactions on Circuits and Systems I: Regular Papers 59(10) (2012) 2354-2362.
[4] S. Yin, S.X. Ding, A. Haghani, H. Hao, P. Zhang, A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process, Journal of Process Control 22(9) (2012) 1567-1581.
[5] C.K. Lau, Y.S. Heng, M.A. Hussain, M.I. Mohamad Nor, Fault diagnosis of the polypropylene production process (UNIPOL PP) using ANFIS, ISA Transactions 49(4) (2010) 559-566.
[6] C.H. Lo, Y.K. Wong, A.B. Rad, K.M. Chow, Fusion of qualitative bond graph and genetic algorithms: A fault diagnosis application, ISA Transactions 41(4) (2002) 445-456.
[7] C.-P. Hung, M.-H. Wang, Diagnosis of incipient faults in power transformers using CMAC neural network approach, Electric Power Systems Research 71(3) (2004) 235-244.
[8] M. Fakhr, A.M. Elsayad Steel Plates Faults Diagnosis with Data Mining Models Journal of Computer Science 8(4) (2012) 506-514.
[9] V.N. Vapnik, The nature of statistical learning theory, Springer-Verlag, New York, 1995.
[10] Y. Tian, M. Fu, F. Wu, Steel plates fault diagnosis on the basis of support vector machines, Neurocomputing 151, Part 1 (2015) 296-303.
[11] S.S. Keerthi, C.-J. Lin, Asymptotic behaviors of support vector machines with Gaussian kernel,, Neural Computation 15(7) (2013) 1667–1689.
[12] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I.H. Witten, The WEKA data mining software: an update, SIGKDD Explor. Newsl. 11(1) (2009) 10-18.
[13] M. Galar, A. Fernández, E. Barrenechea, F. Herrera, DRCW-OVO: Distance-based relative competence weighting combination for One-vs-One strategy in multi-class problems, Pattern Recognition 48(1) (2015) 28-42.
[14] M. Galar, A. Fernández, E. Barrenechea, H. Bustince, F. Herrera, An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes, Pattern Recognition 44(8) (2011) 1761-1776.
[15] N. García-Pedrajas, D. Ortiz-Boyer, An empirical study of binary classifier fusion methods for multiclass classification, Information Fusion 12(2) (2011) 111-130.
Read more
##plugins.themes.academic_pro.article.sidebar##
Published
Nov 30, 2017
Download
How to Cite
Thi Phuong Trang Pham, Thi Thu Ha Truong. “Steel Plate Fault Diagnosis Based on an Integration of One-Against-One Strategy and Support Vector Machines”. The University of Danang - Journal of Science and Technology, vol. 11, no. 120.4, Nov. 2017, pp. 95-98, https://jst-ud.vn/jst-ud/article/view/3730.
