@ARTICLE{Kimotho2013c,
  howpublished = {Journal},
  author = {Kimotho, James Kuria AND Sondermann-Woelke, Christoph AND Meyer,
	Tobias AND Sextro, Walter},
  title = {Application of Event Based Decision Tree and Ensemble of Data Driven
	Methods for Maintenance Action Recommendation},
  journal = {International Journal of Prognostics and Health Management},
  year = {2013},
  volume = {4},
  number = {2},
  abstract = {This study presents the methods employed by a team from the department
	of Mechatronics and Dynamics at the University of Paderborn, Germany
	for the 2013 PHM data challenge. The focus of the challenge was on
	maintenance action recommendation for an industrial machinery based
	on remote monitoring and diagnosis. Since an ensemble of data driven
	methods has been considered as the state of the art approach in diagnosis
	and prognosis, the first approach was to evaluate the performance
	of an ensemble of data driven methods using the parametric data as
	input and problems (recommended maintenance action) as the output.
	Due to close correlation of parametric data of different problems,
	this approach produced high misclassification rate. Event-based decision
	trees were then constructed to identify problems associated with
	particular events. To distinguish between problems associated with
	events that appeared in multiple problems, support vector machine
	(SVM) with parameters optimally tuned using particle swarm optimization
	(PSO) was employed. Parametric data was used as the input to the
	SVM algorithm and majority voting was employed to determine the final
	decision for cases with multiple events. A total of 165 SVM models
	were constructed. This approach improved the overall score from 21
	to 48. The method was further enhanced by employing an ensemble of
	three data driven methods, that is, SVM, random forests (RF) and
	bagged trees (BT), to build the event based models. With this approach,
	a score of 51 was obtained . The results demonstrate that the proposed
	event based method can be effective in maintenance action recommendation
	based on events codes and parametric data acquired remotely from
	an industrial equipment.},
  keywords = {maintenance decision, Bagged trees, Decision trees, PSO-SVM, Random
	forests},
  doi={10.36001/ijphm.2013.v4i2.2125}
}