Among systems that provide sensor data of their performance, one approach to prognostic estimation is forecasting, i.e. prediction of measurable parameters and comparison of predicted values against established operational limits. Forecasting can be attempted statistically, or can be based on rigorous physical simulation. However, combining these approaches is difficult where system mode behavior or timing of system activities is uncertain, limiting the accuracy or applicability of a forecast.
In this paper we describe a method to modify simulation outputs to better match current telemetry. We begin with a familiar autoregressive approach to model residuals between predicted spacecraft performance, provided by physics-based modeling tools, and up-to-date spacecraft telemetry. This result is then improved by transforming the simulation result to better fit recent data, and the transformation is applied to generate more accurate future predictions. The method is suitable for real-time signal prediction. We will motivate this approach and characterize its performance using example telemetry from the NASA Mars Exploration Rover spacecraft.
How to Cite
forecasting, prognostics, sensor fusion, physical modeling
P J. Brockwell, R. A. Davis (1996). Introduction to Time Series and Forecasting, Springer.
R. O. Duda and P. E. Hart (1973). Pattern Classification and Scene Analysis, John Willey & Sons.
J. D. Hamilton (1994). Time Series Analysis, Princeton University Press.
M. Kordon and E. G. Wood (2002). Multi-mission Space Vehicle Subsystem Analysis Tools, in Proceedings of the IEEE Aerospace Conference,Big Sky, MO.
R. Mackey, M. James, and I. Kulikov (2009).Prognostic Reasoning with Missing and Uncertain Data for Spacecraft Systems, in Proceedings of the 2008 PHM Conference, Denver, CO.
R. Mackey and I. Kulikov (2009). Template Matching Approach to Signal Prediction, NASA Tech Briefs, in press.
Barbara Zitova and Jan Flusser (2003). Image Registration Methods: A Survey, Image and Vision Computing, Elsevier, vol. 21, no. 11, pp. 977-1000.
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