Failure prognostics can provide benefits in operation and maintenance of equipments by predicting when the component is going to fail and consequently acting at the most appropriate time. In several situations degradation estimations are sparse or missing estimations are present at collected data. Considering these situations, a failure prognostics method was proposed considering the usage of the extended version of the Kalman filter. This method was analyzed with hydraulic system reservoir levels indication collected from four different aircrafts. In this study a prognostic model was estimated by the filter and then future values of hydraulic level as well as the remaining useful life interval were obtained considering a set of Monte Carlo simulations and a failure probability distribution approximation. Results evidenced the benefit of this method to properly prognose the system.
How to Cite
extended Kalman filter, failure prognostics, Missing Data
Blischke, W. R.,&Murthy, D. N. P. (2000). Reliability: Modeling, prediction, and optimization. New York: John Willey & Sons.
Daigle, M. J., & Goebel, K. (2013). Model-based prognostics with concurrent damage progression processes. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43, 535–546.
Gomes, J. P. P., Leao, B. P., Vianna, W. O. L., Galv˜ao, R. K. H., & Yoneyama, T. (2012). Failure prognostics of a hydraulic pump using kalman filter. In Proceedings... Minneapolis: PHMSociety.
Jazwinski, A. H. (1970). Stochastic processes and filtering theory. New York: Elsevier.
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME – Journal of Basic Engineering, 82, 35–45.
Leao, B. P. (2011). Failure prognosis methods and offline performance evaluation. Doctor in science, Instituto Tecnol´ogico de Aeron´autica, S˜ao Jos´e dos Campos.
Lim, C. K. R., & Mba, D. (2015). Switching kalman filter for failure prognostic. Mechanical Systems and Signal Processing, 52–53, 426–435.
Orchard, M., & Vachtsevanos, G. (2009). A particle filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, 31, 221-246.
Vianna, W. O. L., & Malere, J. P. P. (2014). Aircraft hydraulic system leakage detection and servicing recommendations method. In Proceedings... Fort Worth: PHMSociety.
Vianna, W. O. L., Souza Ribeiro, L. G. de, & Yoneyama, T. (2015). Electro hydraulic servovalve health monitoring using fading extended kalman filter. In Proceedings... Houston: IEEE.
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