Application of Event Based Decision Tree and Ensemble of Data Driven Methods for Maintenance Action Recommendation
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 equipment 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.
maintenance decision, Bagged trees, Decision trees, PSO-SVM, Random forests
Hsu, C.W., & Lin, C. J. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13, 415-425.
Kimotho, J., Sondermann-Woelke, 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.
Rajakarunakaran, S., Venkumar, P., Devaraj, D., & Rao, K. S. P. (2008). Artificial neural network approach for fault detection inrotary system. Applied Soft Computing, 8, 740-748.
Sutton, C. D. (2008). Classification and regression trees, bagging, and boosting. Handbook of Statistics, 24, 303-329.
Xue, F., & Yan,W. (2007). Parametric model-based anomaly detection for locomotive subsystems. In Proceedings of international joint conference on neural networks.
Xue, F., Yan, W., Roddy, N., & Varma, A. (2006). Operational data based anomaly detection for locomotive diagnostics. In Proceedings of international conference on machine learning.
Yang, B.-S., Di, X., & Han, T. (2008). Random forests classifier for machine fault diagnosis. Journal of Mechanical Science and Technology, 22, 1716-1725.
Yang, Q., Liu, C., Zhang, D., & Wu, D. (2012). A new ensemble fault diagnosis method based on k-means algorithm. International Journal of Intelligent Engineering & Systems, 5, 9-16.