Machinery Prognostic Method Based on Multi-Class Support Vector Machines and Hybrid Differential Evolution -- Particle Swarm Optimization

J.K. Kimotho, C. Sondermann-Wölke, T. Meyer, W. Sextro, Chemical Engineering Transactions 33 (2013) 619–624.

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Abstract
Recently, focus on maintenance strategies has been shifted towards prognostic health management (PHM) and a number of state of the art algorithms based on data-driven prognostics have been developed to predict the health states of degrading components based on sensory data. Amongst these algorithms, Multiclass Support Vector Machines (MC-SVM) has gained popularity due to its relatively high classification accuracy, ability to classify multiple patterns and capability to handle noisy /incomplete data. However, its application is limited by the difficulty in determining the required kernel function and penalty parameters. To address this problem, this paper proposes a hybrid differential evolution -- particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters. The differential algorithm (DE) obtains the search limit for the SVM parameters, while the particle swarm optimization algorithm (PSO) determines the global optimum parameters for a given training data set. Since degrading machinery components display several degradation stages in their lifetime, the MC-SVM trained with optimum parameters are used to estimate the health states of a degrading machinery component, from which the remaining useful life (RUL) is predicted. This method improves the classification accuracy of MC-SVM in predicting the health states of a machinery component and consequently increases the accuracy of RUL predictions. The feasibility of the method is validated using bearing prognostic run-to-failure data obtained from NASA public data repository. A comparative study between MC-SVM with parameters obtained using simple grid search with n-fold cross validation and MCSVM with DE-PSO based on prognostic performance metrics reveals that the proposed method has better performance, with all the cases considered falling within a 10 \% error margin. The method also outperforms other soft computing methods proposed in literature.
Publishing Year
Journal Title
Chemical Engineering Transactions
Volume
33
Page
619-624
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Kimotho JK, Sondermann-Wölke C, Meyer T, Sextro W. Machinery Prognostic Method Based on Multi-Class Support Vector Machines and Hybrid Differential Evolution -- Particle Swarm Optimization. Chemical Engineering Transactions. 2013;33:619-624. doi:10.3303/CET1333104
Kimotho, J. K., Sondermann-Wölke, C., Meyer, T., & Sextro, W. (2013). Machinery Prognostic Method Based on Multi-Class Support Vector Machines and Hybrid Differential Evolution -- Particle Swarm Optimization. Chemical Engineering Transactions, 33, 619–624. https://doi.org/10.3303/CET1333104
@article{Kimotho_Sondermann-Wölke_Meyer_Sextro_2013, title={Machinery Prognostic Method Based on Multi-Class Support Vector Machines and Hybrid Differential Evolution -- Particle Swarm Optimization}, volume={33}, DOI={10.3303/CET1333104}, journal={Chemical Engineering Transactions}, author={Kimotho, James Kuria and Sondermann-Wölke, Christopher and Meyer, Tobias and Sextro, Walter}, year={2013}, pages={619–624} }
Kimotho, James Kuria, Christopher Sondermann-Wölke, Tobias Meyer, and Walter Sextro. “Machinery Prognostic Method Based on Multi-Class Support Vector Machines and Hybrid Differential Evolution -- Particle Swarm Optimization.” Chemical Engineering Transactions 33 (2013): 619–24. https://doi.org/10.3303/CET1333104.
J. K. Kimotho, C. Sondermann-Wölke, T. Meyer, and W. Sextro, “Machinery Prognostic Method Based on Multi-Class Support Vector Machines and Hybrid Differential Evolution -- Particle Swarm Optimization,” Chemical Engineering Transactions, vol. 33, pp. 619–624, 2013.
Kimotho, James Kuria, et al. “Machinery Prognostic Method Based on Multi-Class Support Vector Machines and Hybrid Differential Evolution -- Particle Swarm Optimization.” Chemical Engineering Transactions, vol. 33, 2013, pp. 619–24, doi:10.3303/CET1333104.

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