Improved Time-Based Maintenance in Aeronautics with Regressive Support Vector Machines



Marcia Baptista Ivo P. de Medeiros Joao P. Malere Helmut Prendinger Cairo L. Nascimento Jr. Elsa Henriques

How to Cite

Baptista, M., Medeiros, I. P. de, Malere, J. P., Prendinger, H., Nascimento Jr., C. L., & Henriques, E. (2016). Improved Time-Based Maintenance in Aeronautics with Regressive Support Vector Machines. Annual Conference of the PHM Society, 8(1).
Abstract 168 | PDF Downloads 84



Data-driven modeling, Time-based Maintenance, Maintenance Data, Regression Support Machines, Technical Analysis, Outlier detection

Ahmad, R., & Kamaruddin, S. (2012). An Overview of Time-based and Condition-based Maintenance in Industrial Application. Computers & Industrial Engineering, 63(1), 135–149.
Baptista, M., P. de Medeiros, I., P. Malere, J., Prendinger, H., L. Nascimento Jr., C., & Henriques, E. (2016). A Comparison of Data-driven Techniques for Engine Bleed Valve Prognostics using Aircraft-derived Fault Messages. In Third European Conference of the Prognostics and Health Management Society. PHM Society.
Bergmeir, C., & Benítez, J. M. (2012). On the Use of Crossvalidation for Time Series Predictor Evaluation. Information Sciences, 191, 192–213.
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A Training Algorithm for Optimal Margin Classifiers. In Fifth Annual workshop on Computational Learning Theory (pp. 144–152).
Brotherton, T., Jahns, G., Jacobs, J., & Wroblewski, D. (2000). Prognosis of Faults in Gas Turbine Engines. In Aerospace Conference (Vol. 6, pp. 163–171).
Brys, G., Hubert, M., & Struyf, A. (2004). A Robustification of the Jarque–Bera Test of Normality. In COMPSTAT 2004 Symposium, Section: Robustness.
Cook, R. D., &Weisberg, S. (1982). Residuals and Influence in Regression.
de Pádua Moreira, R., & Nascimento, C. L. (2012). Prognostics of Aircraft Bleed Valves using a SVM Classification Algorithm. In Aerospace Conference (pp. 1–8).
Edwards, R. D., Magee, J., & Bassetti, W. C. (2007). Technical Analysis of Stock Trends. CRC Press.
Liu, C.-c. (1997). A Comparison between the Weibull and Lognormal Models used to Analyse Reliability Data (Unpublished doctoral dissertation). University of Nottingham.
Schwabacher, M. (2005). A Survey of Data-driven Prognostics. In AIAA Infotech@ Aerospace Conference (pp. 1–5).
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011). Remaining Useful Life Estimation – A Review on the Statistical Data Driven Approaches. European Journal of Operational Research, 213(1), 1–14.
Smola, A. J., & Sch¨olkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14(3), 199–222.
Tukey, J. W. (1977). Exploratory Data Analysis.
Wang, W. (2012). An Overview of the Recent Advances in Delay-Time-based Maintenance Modelling. Reliability Engineering & System Safety, 106, 165–178.
Widodo, A., & Yang, B.-S. (2007). Support Vector Machine in Machine Condition Monitoring and Fault Diagnosis. Mechanical Systems and Signal Processing, 21(6), 2560–2574.
Williamson, D. F., Parker, R. A., & Kendrick, J. S. (1989). The Box Plot: A Simple Visual Method to Interpret Data. Annals of Internal Medicine, 110(11), 916–921.
Xu, J., & Perron, P. (2014). Forecasting Return Volatility: Level Shifts with Varying Jump probability and Mean Reversion. International Journal of Forecasting, 30(3), 449–463.
Technical Research Papers