Optimal Parameter Tuning for Multiclass Support Vector Machines in Machinery Health State Estimation

J.K. Kimotho, W. Sextro, PAMM 14 (2014) 815–816.

Download
No fulltext has been uploaded.
Journal Article | English
Author
;
Abstract
The increasing demand for high reliability, safety and availability of technical systems calls for innovative maintenance strategies. The use of prognostic health management (PHM) approach where maintenance action is taken based on current and future health state of a component or system is rapidly gaining popularity in the maintenance industry. Multiclass support vector machines (MC-SVM) has been identified as a promising algorithm in PHM applications due to its high classification accuracy. However, it requires parameter tuning for each application, with the objective of minimizing the classification error. This is a single objective optimization problem which requires the use of optimization algorithms that are capable of exhaustively searching for the global optimum parameters. This work proposes the use of hybrid differential evolution (DE) and particle swarm optimization (PSO) in optimally tuning the MC-SVM parameters. DE identifies the search limit of the parameters while PSO finds the global optimum within the search limit. The feasibility of the approach is verified using bearing run-to-failure data and the results show that the proposed method significantly increases health state classification accuracy.
Publishing Year
Journal Title
PAMM
Volume
14
Issue
1
Page
815-816
ISSN
LibreCat-ID

Cite this

Kimotho JK, Sextro W. Optimal Parameter Tuning for Multiclass Support Vector Machines in Machinery Health State Estimation. PAMM. 2014;14(1):815-816. doi:10.1002/pamm.201410388
Kimotho, J. K., & Sextro, W. (2014). Optimal Parameter Tuning for Multiclass Support Vector Machines in Machinery Health State Estimation. PAMM, 14(1), 815–816. https://doi.org/10.1002/pamm.201410388
@article{Kimotho_Sextro_2014, title={Optimal Parameter Tuning for Multiclass Support Vector Machines in Machinery Health State Estimation}, volume={14}, DOI={10.1002/pamm.201410388}, number={1}, journal={PAMM}, publisher={WILEY-VCH Verlag}, author={Kimotho, James Kuria and Sextro, Walter}, year={2014}, pages={815–816} }
Kimotho, James Kuria, and Walter Sextro. “Optimal Parameter Tuning for Multiclass Support Vector Machines in Machinery Health State Estimation.” PAMM 14, no. 1 (2014): 815–16. https://doi.org/10.1002/pamm.201410388.
J. K. Kimotho and W. Sextro, “Optimal Parameter Tuning for Multiclass Support Vector Machines in Machinery Health State Estimation,” PAMM, vol. 14, no. 1, pp. 815–816, 2014.
Kimotho, James Kuria, and Walter Sextro. “Optimal Parameter Tuning for Multiclass Support Vector Machines in Machinery Health State Estimation.” PAMM, vol. 14, no. 1, WILEY-VCH Verlag, 2014, pp. 815–16, doi:10.1002/pamm.201410388.

Export

Marked Publications

Open Data LibreCat

Search this title in

Google Scholar