Machinery Prognostic Method Based on Multi-Class Support Vector Machines and Hybrid Differential Evolution – Particle Swarm Optimization (bibtex)
by , , ,
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.
Reference:
Kimotho, J. K.; Sondermann-Woelke, 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, volume 33, 2013.
Bibtex Entry:
@ARTICLE{Kimotho2013a,
  howpublished = {Journal},
  author = {James Kuria Kimotho AND Christoph Sondermann-Woelke AND Tobias Meyer
	AND Walter Sextro},
  title = {Machinery Prognostic Method Based on Multi-Class Support Vector Machines
	and Hybrid Differential Evolution -- Particle Swarm Optimization},
  journal = {Chemical Engineering Transactions},
  year = {2013},
  volume = {33},
  pages = {619-624},
  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.},
  bdsk-url-1 = {http://www.aidic.it/cet/13/33/104.pdf},
  bdsk-url-2 = {http://dx.doi.org/10.3303/CET1333104},
  doi = {10.3303/CET1333104},
  url = {http://www.aidic.it/cet/13/33/104.pdf}
}
Powered by bibtexbrowser