Towards Performance Prognostics of a Launch Valve



Glenn Shevach Mark Blair James Hing Larry Venetsky Everard Martin John Wheelock


Due to its criticality in aircraft carrier steam catapult operations, the performance of the Launch Valve is monitored using timer components to determine the elapsed time for the valve to achieve a set opening distance. Significant degradation in performance can lead to loss in end speed of the catapult and result in loss of aircraft/lives. This paper presents a method of using existing timing data for anomaly detection and predicting when maintenance is required (MIR) for a Launch Valve. Features such as mean and standard deviation of timing values are extracted from clock time data to detect anomalies. Neyman-Pearson Criterion and Sequential Probability Ratio Testing are used to formulate a decision on the degraded state. Once an anomaly is detected, an observation window of the previous N filtered samples are used in a risk sensitive particle filter framework. The resulting distribution is used in the prediction of shots until MIR. Performance degradation is extracted from training data and modeled as a third order polynomial. The algorithm was tested on two test sets and validated by Subject Matter Experts (SMEs) supplying the data. An Alpha-Lambda performance metric shows the time predictions until MIR fall inside an acceptable performance cone of 20% error.

How to Cite

Shevach, G., Blair , M. ., Hing , J. ., Venetsky, L. ., Martin, E., & Wheelock, J. . (2014). Towards Performance Prognostics of a Launch Valve. Annual Conference of the PHM Society, 6(1).
Abstract 17 | PDF Downloads 29



Launch Valve, prognostics, anomaly detection, health monitoring

An, D., Choi, J., Kim, N.H., (2012), “A Tutorial for Model- based Prognostics Algorithms based on Matlab Code,” Annual Conference of Prognostics and Health Management Society, vol. 3, no. 122, September 2012.

Cheng, S. (2008), Autonomous Prognostic Monitoring Device. Proceeding for 62nd Meeting of the Society for Machinery Failure Prevention Technology (MFPT), Virginia Beach, VA, May 2008.

Daigle, M., Goebel, K. (2011), “A model-based prognostics approach applied to pneumatic valves,” International Journal of the PHM Society, vol. 2, no. 2, pp. 1-16.

Gomes, J., Ferreira, B., Cabral, D., Glavao, R., Yoneyama, T. (2010), “Health Monitoring of a Pneumatic Valve Using a PIT Based Technique,” Annual Conference of Prognostics and Health Management Society E. Lehmann (1986), Testing Statistical Hypotheses, New York: Wiley, 1986.

Orchard, M., Tang, L., Saha, B., Goebel, K., Vachtsevanos, G. (2010), “Risk-Sensitive Particle-Filtering-based Prognosis Framework for Estimation of Remaining Useful Life in Energy Storage Devices,” Studies in Informatics and Control, vol. 19, no. 3, September 2010.

Saxena, A; Celaya, J.; Saha, B.; Saha, S.; Goebel, K. (2009), “Evaluating Algorithm Performance Metrics Tailored for Prognostics,” IEEE Aerospace Conference, vol., no., pp.1,13, 7-14 March 2009.

Wald, A. (1947), “Sequential Analysis”, John Wiley & Sons, New York, NY.
Technical Papers