Interactive Multiple-Model Application for Hydraulic Servovalve Health Monitoring

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Wlamir Olivares Loesch Vianna Takashi Yoneyama

Abstract

Hydraulic systems are widely used as power source for several different applications. Servovalves are critical components and often subjected to failures. Estimating degradations from these components requires dynamic analysis of their behavior and consequently advanced monitoring techniques. This article proposes an online monitoring method to estimate a degradation parameter of the servovalve using an interactive multiple-model technique considering a bank of Extended Kalman Filters that models not only the valve itself but also the degradation trend. A single failure mode was considered related to the nozzle line clogging. The degradation estimates and the likelihood of the correctness of each model were analyzed in order to evaluate the proposed method.

How to Cite

Olivares Loesch Vianna, W., & Yoneyama, T. (2015). Interactive Multiple-Model Application for Hydraulic Servovalve Health Monitoring. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2598
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Keywords

PHM

References
Samadani, M., Kwuimy, C. A. K. & Nataraj, C. (2014) Fault Detection and Severity Analysis of Servo Valves Using Recurrence Quantification Analysis. Proceedings of the Annual Conference of the Prognostics and Health Management Society, September 29 – October 02, Fort Worth.

Borello, L., Vedova, M. D., Jacazio, G. & Sorli, M. (2009) A Prognostic Model for Electrohydraulic Servovalves. Proceedings of the Annual Conference of the Prognostics and Health Management Society, September 27 – October 1, San Diego.

Mussi, E. T., Góes, L. C. S. (2009) The Study of an Electrohydraulic Servovalve for Fault Detection and Isolation Purposes. Proceedings of COBEM 20th International Congress of Mechanical Engineering November 15-20, 2009, Gramado, RS, Brazil.

Sepasi, M. (2005) Fault Monitoring in Hydraulic Systems using Unscented Kalman Filter Doctoral dissertation. Sharif University of Technology, Iran.

Hajiyev, C & Caliskan, F. (2003) Fault Diagnosis and Reconfiguration in Flight Control Systems. Boston: Kluwer Academic plubishers.

Keong, C., Lim, R. & Mbab D. (2014). Switching Kalman filter for failure prognostic, Mechanical Systems and Signal Processing.

Farmer, M. E., Hsu, R. L. & Jain, A. K. (2002) Interacting Multiple Model (IMM) Kalman Filters for Robust High Speed Human Motion Tracking. Proceedings of the th
IEEE 16 International Conference on Pattern Recognition, (20-23), August 11-15.

Pitre, R. (2004) A Comparison of Multiple-Model Target Tracking Algorithms Doctoral dissertation. University of New Orleans, New Orleans.

Chze, E. S. & Inseok, H. (2008) Performance Analysis of Kalman Filter Based Hybrid Estimation Algorithms Proceedings of the 17th World Congress The International Federation of Automatic Control, July 6- 11, Seoul, Korea.

Merrit, E. H. (1976). Hydraulic Control Systems. New York: John Wiley & Sons.

Blom, H. A. P. & Bar-Shalom, Y.(1988). The interacting multiple model algorithm for systems with markovian switching coefficients. IEEE Trans. on Automatic Control, 33(8): 780–783.

Farmer, M. E., Hsu, R.-L. & Jain, A. K. (2002). Interacting Multiple Model (IMM) Kalman Filters for Robust High Speed Human Motion Tracking. Proceedings of the 16th International Conference on Pattern Recognition, August 11-15, Quebec, Canada.
Section
Technical Papers

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