Hybrid Learning Approach to Sensor Fault Detection with Flight Test Data

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Hybrid Learning Approach to Sensor Fault Detection with Flight Test Data

de Silva, Brian M. / Callaham, Jared / Jonker, Jonathan / Goebel, Nicholas / Klemisch, Jennifer / McDonald, Darren / Hicks, Nathan / Nathan Kutz, J. / Brunton, Steven L. / Aravkin, Aleksandr Y.

Abstract

Data-driven algorithms are developed to fully automate sensor fault detection in systems governed by underlying physics, with a particular focus on the flight test setting. The proposed machine learning method uses a time series of typical behavior to approximate the evolution of measurements of interest by a linear time-invariant system. Given additional data from related sensors, a Kalman observer is used to maintain a separate real-time estimate of the measurement of interest. Sustained deviation between the measurements and the estimate is used to detect anomalous behavior. A decision tree, informed by integrating other sensor measurement values, is used to determine the amount of deviation required to identify a sensor fault. The method is validated by applying it to three test systems exhibiting various types of sensor faults: commercial flight test data, an unsteady aerodynamics model with dynamic stall, and a model for longitudinal flight dynamics forced by atmospheric turbulence. In the latter two cases, fault detection was tested for several prototypical failure modes. The combination of a learned dynamic model with the automated decision tree accurately detects sensor faults in each case.

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Dokumentinformationen

Titel :

Hybrid Learning Approach to Sensor Fault Detection with Flight Test Data

Beteiligte:

de Silva, Brian M. (Autor:in) / Callaham, Jared (Autor:in) / Jonker, Jonathan (Autor:in) / Goebel, Nicholas (Autor:in) / Klemisch, Jennifer (Autor:in) / McDonald, Darren (Autor:in) / Hicks, Nathan (Autor:in) / Nathan Kutz, J. (Autor:in) / Brunton, Steven L. (Autor:in) / Aravkin, Aleksandr Y. (Autor:in)

Erschienen in:

AIAA Journal ; 59 , 9 ; 3490-3503

Erscheinungsdatum :

2021-07-21

Format / Umfang :

14 pages

ISSN :

1533-385X

DOI :

https://doi.org/10.2514/1.J059943

Medientyp :

Aufsatz (Zeitschrift)