ODE - Augmented Training Improves Anomaly Detection in Sensor Data from
  Machines
  Machines of all kinds from vehicles to industrial equipment are increasingly
instrumented with hundreds of sensors. Using such data to detect anomalous
behaviour is critical for safety and efficient maintenance. However, anomalies
occur rarely and with great variety in such systems, so there is often
insufficient anomalous data to build reliable detectors. A standard approach to
mitigate this problem is to use one class methods relying only on data from
normal behaviour. Unfortunately, even these approaches are more likely to fail
in the scenario of a dynamical system with manual control input(s). Normal
behaviour in response to novel control input(s) might look very different to
the learned detector which may be incorrectly detected as anomalous. In this
paper, we address this issue by modelling time-series via Ordinary Differential
Equations (ODE) and utilising such an ODE model to simulate the behaviour of
dynamical systems under varying control inputs. The available data is then
augmented with data generated from the ODE, and the anomaly detector is
retrained on this augmented dataset. Experiments demonstrate that ODE-augmented
training data allows better coverage of possible control input(s) and results
in learning more accurate distinctions between normal and anomalous behaviour
in time-series.
