Physics-informed machine learning for sensor fault detection with flight test data
| Autor: | de Silva, Brian M., Callaham, Jared, Jonker, Jonathan, Goebel, Nicholas, Klemisch, Jennifer, McDonald, Darren, Hicks, Nathan, Kutz, J. Nathan, Brunton, Steven L., Aravkin, Aleksandr Y. |
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| Rok vydání: | 2020 |
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| Druh dokumentu: | Working Paper |
| Popis: | We develop data-driven algorithms to fully automate sensor fault detection in systems governed by underlying physics. 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. We validate the method 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 we test fault detection for several prototypical failure modes. The combination of a learned dynamical model with the automated decision tree accurately detects sensor faults in each case. Comment: 21 pages, 10 figures, submitted to AIAA |
| Databáze: | arXiv |
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