Risk Prediction of Engineering Assets: An Ensemble of Part Lifespan Calculation and Usage Classification Methods
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
For the 2014 Prognostics and Health Management (PHM) Data Challenge Competition, the PHM Society proposed a problem surrounding risk prediction of engineering assets. We worked to address this problem by statistically analyzing the maintenance records, extracting key data features, and proposing an ensemble method for accurate prediction of imminent failure of assets. The data analysis of maintenance records provided two key pieces of information: 1) parts and part replacement reasons were able to be classified into corrective and scheduled maintenance actions, and 2) a linear relation was found between failure frequency and usage time. Based on this information, we proposed two risk-prediction methods, namely, a method based on part lifespan calculation and a method based on usage classification. Further work showed that the ensemble approach, which combined these two methods with a risk assignment formulation, provided more accurate risk prediction. The score predicted by the ensemble approach ranked in the second place in the 2014 PHM Data Challenge Competition.
##plugins.themes.bootstrap3.article.details##
risk assessment, Reliability Centred Maintenance, Fleet-Wide Prognostic Health Management, Big Data
Braglia, M., Carmignani, G., Frosolini, M., & Zammori, F. (2012). Data classification and MTBF prediction with a multivariate analysis approach. Reliability Engineering & System Safety, 97(1), 27-35. doi:10.1016/j.ress.2011.09.010
Crow, L. H. (1990). Evaluating the reliability of repairable systems. IEEE Annual Reliability and Maintainability Symposium (275–279), January 23-25, Los Angeles, CA. doi:10.1109/ARMS.1990.67969
Gao, J., Fan, W., & Han, J. (2010). On the power of ensemble: Supervised and unsupervised methods reconciled, Tutorial on SIAMD at a Mining Conference, Columbus, OH, 2010
Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739. doi:10.1016/j.ymssp.2008.06.009
Hjartarson, T., & Otal, S. (2006). Predicting future asset condition based on current health index and maintenance level. IEEE 11th Conference on Transmission & Distribution Construction, Operation and Live-Line Maintenance, October 15-19, Albuquerque, NM. doi: 10.1109/TDCLL M.2006.340747
Hu, C., Youn, B. D., & Wang, P. F. (2012). Ensemble of datadriven prognostic algorithms for robust prediction of remaining useful life, Reliability Engineering & System Safety, 103, 120–135. doi:10.1016/j.ress.2012.03.008
Jahromi, A., Piercy, R., Cress, S., Service, J., & Fan, W. (2009). An approach to power transformer asset management using health index. IEEE Electrical Insulation Magazine, 25(2), 20–34. doi:10.1109/MEI.2009.4802595
Lawless, J., Hu, J., & Cao, J. (1995). Methods for the estimation of failure distributions and rates from automobile warranty data. Lifetime Data Analysis, 1(3), 227-240. doi:10.1007/BF00985758
Louit, D. M., Pascual, R., & Jardine, A. K. S. (2009). A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data. Reliability Engineering & System Safety, 94(10), 1618-1628. doi:10.1016/j.ress.2009.04.001
Taghipour, S., Banjevic, D., & Jardine, A. K. S. (2011). Reliability analysis of maintenance data for complex medical devices. Quality and Reliability Engineering International, 27(1), 71-84. doi:10.1002/qre.1084
Zhou, Y. (2011). The auto regression model of bus fleet failure number. International Journal of Reliability and Applications, 12(2), 95-102