Health Monitoring of a Pneumatic Valve Using a PIT Based Technique



Published Oct 10, 2010
João P. P. Gomes Bruno C. Ferreira Dennis Cabral Roberto K. H. Glavão Takashi Yoneyama


This paper is concerned with the development of a health monitoring system for a pneumatic valve employed in pressure regulation systems. The proposed method is based on the statistical analysis of deviations of the controlled pressure signal from a baseline behavior. For this purpose, the Probability Integral Transform is employed to calculate an index of dissimilarity between the distributions of monitored and baseline data. The proposed method was applied to field records of 15 units, which were monitored during eight months. In the case of failed units, the degradation index showed an increasing trend prior to the failure occurrence. It is worth noting that the failure level was similar in all cases, which is an important characteristic for the future development of prognostic solutions. In addition, no false alarms were observed for the healthy units. The results found in the case study are realistic and fit within practical requirements to support maintenance decision

How to Cite

P. P. Gomes, J., C. Ferreira, B., Cabral, D., K. H. Glavão, R., & Yoneyama, T. (2010). Health Monitoring of a Pneumatic Valve Using a PIT Based Technique. Annual Conference of the PHM Society, 2(1).
Abstract 367 | PDF Downloads 124



health monitoring, Pneumatic Valves, Probability Integral Transform

(Byington et al, 2003) C. S Byington,., M. Watson., D. Edwards., and B. Dunkin. 2003. In-Line Health Monitoring System for Hydraulic Pumps and Motors. In Proceedings of the IEEE Aerospace Conference. Big Sky, 2003.
(Chen et al, 2007) L. Chen., C. Lee , R. K. Mehra How to Tell a Bad Filter Through Monte Carlo Simulations. IEEE Transactions on Automatic Control, 52, 1302-1307.
(Demetgul et al, 2009) M. Demetgul, I. N. Tansel, S. Taskin. Fault diagnosis of pneumatic systems with artificial neural network algorithms. Expert systems with Applications, 36, 10512-10519.
(Ishida, 2005) I. Ishida. Scanning Multivariate Conditional Densities with Probability Integral Transforms. CIRJE Discussion Paper CIRJE-F-369, Faculty of Economics, University of Tokyo.
(Kalgren et al, 2007) P. W. Kalgren, M. Baybutt, A. Ginart, C. Minnella, M. J. Roemer and T. Dabney. Application of Prognostic Health Management in Digital Electronic Systems. In Proceedings of the IEEE Aerospace Conference. Big Sky, 2007
(Leão et al, 2010) B. P. Leão , J. P. P. Gomes, R. K. H. Galvão, T. Yoneyama. How to Tell the Good from the Bad in Failure Prognostics Methods. in Proceedings of IEEE Aerospace Conference, Big Sky, 2010
(Wald, A. 1945) A. Wald. Sequential Tests of Statistical Hypotheses The Annals of Mathematical Statistics, Vol16, No 2, pp 117-186
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