  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.
