Funding: NASA, NSF
Design of safety critical systems using analytical
redundancy: The objective of this research is to create
tools to manage uncertainty in the design and certification
process of safety-critical aviation systems. The research
focuses on probabilistic techniques to specify system-level
requirements and bound the performance of dynamical
components. These will reduce the design costs associated
with complex aviation systems consisting of tightly integrated
components produced by many independent engineering
organizations. This research will lead to a significant
reduction in the costs and time required for fielding new
aviation systems. This will enable, for example, the safe and
rapid implementation of next generation air traffic control
systems that have the potential of tripling airspace capacity
with no reduction in safety. The proposed methods are also
applicable to other complex systems including smart power
grids and automated highways.
Graduate Student: Raghu Venkataraman
(Publications)
Analysis of Model-based Fault Detection Algorithms:
Commercial aircraft are extremely reliable with flight control
systems certified to achieve fewer than one catastrophic
failure every billion hours. These systems achieve reliability
almost exclusively using redundant physical components.
Analytical (model-based) redundancy is an alternative approach
that has the potential to reduce system size and weight. The
objective of this research is to develop the analysis tools
required to answer the following question: Do analytically
redundant designs improve the overall system reliability and,
if so, by how much? Results thus far include tools for an
extended fault tree analysis that incorporates algorithmic
failures in addition to hardware component failures.
Moreover, theoretical bounds have been derived for the false
alarm probability of an FDI system with time-correlated
residuals.
Graduate Student: Bin Hu
(Publications)