Failure Prognostics of a Hydraulic Pump Using Kalman Filter

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Published Sep 23, 2012
João Paulo P. Gomes Bruno P. Leão Wlamir O. L. Vianna Roberto K. H. Galvão Takashi Yoneyama

Abstract

Hydraulic systems are an important power source in modern aircraft. Most aircraft employ hydraulic power for flight control systems and landing gears actuation. Pumps are a critical component in hydraulic system and monitoring the health of such components may provide economic and operational benefits to aircraft operators. This work describes the use of Kalman Filter techniques for the estimation of remaining useful life of aircraft hydraulic pumps. An empirical model of degradation evolution is employed for this purpose. Low sampling rate measurements of the hydraulic pressure of the aircraft hydraulic systems are the only measurements employed. In order to illustrate and validate the method, two time series of actual run to failure data are analyzed. Results provide evidence that the method can be successfully employed for actual aircraft hydraulic pump failure prognosis.

How to Cite

Paulo P. Gomes, J. ., P. Leão, B., O. L. Vianna, W., K. H. Galvão, R., & Yoneyama, T. . (2012). Failure Prognostics of a Hydraulic Pump Using Kalman Filter. Annual Conference of the PHM Society, 4(1). https://doi.org/10.36001/phmconf.2012.v4i1.2085
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Keywords

Kalman Filter, failure prognostics, hydraulic pump

References
Merrit, E. H. (1976). Hydraulic Control Systems. John Wiley, New York.

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Byington, C. S.; Watson, M.; Edwards, D. and Dunkin, B. (2003). In-line health monitoring system for hydraulic pumps and motors. IEEE Aerospace Conference Proceedings, Big Sky, MO.

Bechhoefer, E.; Clark, S. and He, D. (2010). A state space model for vibration based prognostics. Proceedings of the Annual Conference of the Prognostics and Health Management Society.

Leão, B. P. (2011). Failure prognosis methods and offline performance evaluation. Ph.D. thesis. Instituto Tecnológico de Aeronáutica.

Orchard, M; Vachtsevanos, G. (2009) A particle filtering approach for online fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, no. 3-4, p. 221–246.
Section
Poster Presentations

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