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Dynamic data-driven learning for self-healing avionics

  • Shigeru Imai
  • Sida Chen
  • Wennan Zhu
  • Carlos A. Varela
Article
  • 195 Downloads

Abstract

In sensor-based systems, spatio-temporal data streams are often related in non-trivial ways. For example in avionics, while the airspeed that an aircraft attains in cruise phase depends on the weight it carries, it also depends on many other factors such as engine inputs, angle of attack, and air density. It is therefore a challenge to develop failure models that can help recognize errors in the data, such as an incorrect fuel quantity or an incorrect airspeed. In this paper, we present a highly-declarative programming framework that facilitates the development of self-healing avionics applications, which can detect and recover from data errors. Our programming framework enables specifying expert-created failure models using error signatures, as well as learning failure models from data. To account for unanticipated failure modes, we propose a new dynamic Bayes classifier, that detects outliers and upgrades them to new modes when statistically significant. We evaluate error signatures and our dynamic Bayes classifier for accuracy, response time, and adaptability of error detection. While error signatures can be more accurate and responsive than dynamic Bayesian learning, the latter method adapts better due to its data-driven nature.

Keywords

Data streaming Spatio-temporal data Declarative programming Linear regression Bayesian statistics 

Notes

Acknowledgements

This research is partially supported by the DDDAS program of the Air Force Office of Scientific Research, Grant No. FA9550-15-1-0214, NSF Grant No. 1462342, and a Yamada Corporation Fellowship. We would like to thank anonymous reviewers for their valuable feedback. We would also like to acknowledge co-authors of previous PILOTS papers: Erik Blasch, Richard Klockowski, Alessandro Galli, Frederick Lee, and Colin Rice.

Supplementary material

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Shigeru Imai
    • 1
  • Sida Chen
    • 1
  • Wennan Zhu
    • 1
  • Carlos A. Varela
    • 1
  1. 1.Department of Computer ScienceRensselaer Polytechnic InstituteTroyUSA

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