---
_id: '9808'
abstract:
- lang: eng
  text: 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.
author:
- first_name: James Kuria
  full_name: Kimotho, James Kuria
  last_name: Kimotho
- first_name: Chritoph
  full_name: Sondermann-Wölke, Chritoph
  last_name: Sondermann-Wölke
- first_name: Tobias
  full_name: Meyer, Tobias
  last_name: Meyer
- first_name: Walter
  full_name: Sextro, Walter
  id: '21220'
  last_name: Sextro
citation:
  ama: Kimotho JK, Sondermann-Wölke C, Meyer T, Sextro W. Application of Event Based
    Decision Tree and Ensemble of Data Driven Methods for Maintenance Action Recommendation.
    <i>International Journal of Prognostics and Health Management</i>. 2013;4(2).
  apa: Kimotho, J. K., Sondermann-Wölke, C., Meyer, T., &#38; Sextro, W. (2013). Application
    of Event Based Decision Tree and Ensemble of Data Driven Methods for Maintenance
    Action Recommendation. <i>International Journal of Prognostics and Health Management</i>,
    <i>4</i>(2).
  bibtex: '@article{Kimotho_Sondermann-Wölke_Meyer_Sextro_2013, title={Application
    of Event Based Decision Tree and Ensemble of Data Driven Methods for Maintenance
    Action Recommendation}, volume={4}, number={2}, journal={International Journal
    of Prognostics and Health Management}, author={Kimotho, James Kuria and Sondermann-Wölke,
    Chritoph and Meyer, Tobias and Sextro, Walter}, year={2013} }'
  chicago: Kimotho, James Kuria, Chritoph Sondermann-Wölke, Tobias Meyer, and Walter
    Sextro. “Application of Event Based Decision Tree and Ensemble of Data Driven
    Methods for Maintenance Action Recommendation.” <i>International Journal of Prognostics
    and Health Management</i> 4, no. 2 (2013).
  ieee: J. K. Kimotho, C. Sondermann-Wölke, T. Meyer, and W. Sextro, “Application
    of Event Based Decision Tree and Ensemble of Data Driven Methods for Maintenance
    Action Recommendation,” <i>International Journal of Prognostics and Health Management</i>,
    vol. 4, no. 2, 2013.
  mla: Kimotho, James Kuria, et al. “Application of Event Based Decision Tree and
    Ensemble of Data Driven Methods for Maintenance Action Recommendation.” <i>International
    Journal of Prognostics and Health Management</i>, vol. 4, no. 2, 2013.
  short: J.K. Kimotho, C. Sondermann-Wölke, T. Meyer, W. Sextro, International Journal
    of Prognostics and Health Management 4 (2013).
date_created: 2019-05-13T14:15:36Z
date_updated: 2022-01-06T07:04:21Z
department:
- _id: '151'
intvolume: '         4'
issue: '2'
keyword:
- maintenance decision
- Bagged trees
- Decision trees
- PSO-SVM
- Random forests
language:
- iso: eng
publication: International Journal of Prognostics and Health Management
quality_controlled: '1'
status: public
title: Application of Event Based Decision Tree and Ensemble of Data Driven Methods
  for Maintenance Action Recommendation
type: journal_article
user_id: '55222'
volume: 4
year: '2013'
...