Robust fault detection for commercial transport air data probes

Paul Freeman, Peter Seiler, Gary J. Balas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


Air data probes provide essential sensing capabilities to aircraft. The loss or corruption of air data measurements due to sensor faults jeopardizes an aircraft and its passengers. To address such faults, sensor hardware redundancy is typically combined with a voting system to detect and discard erroneous measurements. This approach relies on redundancy, which may lead to unacceptable increases in system weight and cost. This paper presents an alternative, model-based approach to fault detection for a non-redundant air data system. The model-based fault detection strategy uses robust linear filtering methods to reject exogenous disturbances, e.g. wind, and provide robustness to model errors. The proposed algorithm is applied to NASA's Generic Transport Model aircraft with an air data system modeled based on manufacturer data provided by Goodrich Corporation. The fault detection filters are designed using linearized models at one flight condition. The detection performance is evaluated at a particular reference flight condition using linear analysis and nonlinear simulations.

Original languageEnglish (US)
Title of host publicationProceedings of the 18th IFAC World Congress
PublisherIFAC Secretariat
Number of pages6
Edition1 PART 1
ISBN (Print)9783902661937
StatePublished - 2011

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1 PART 1
ISSN (Print)1474-6670

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 0931931 entitled CPS: Embedded Fault Detection for Low-Cost, Safety-Critical Systems. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The authors acknowledge Andrei Dorobantu for the design of the inner-loop SAS controller.


  • Air data systems
  • Fault detection
  • Robust estimation


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