Model-Based Prognostics of Hybrid SystemsModel-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems, and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) prediction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.
Document ID
20160000591
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Daigle, Matthew (NASA Ames Research Center Moffett Field, CA United States)
Roychoudhury, Indranil (Stinger Ghaffarian Technologies, Inc. (SGT, Inc.) Moffett Field, CA, United States)
Bregon, Anibal (Valladolid Univ. Spain)
Date Acquired
January 11, 2016
Publication Date
October 18, 2015
Subject Category
Systems Analysis And Operations Research
Report/Patent Number
ARC-E-DAA-TN25789
Meeting Information
Meeting: Annual Conference of the Prognostics and Health Management Society 2015