Robust sensor estimation using temporal information

We propose a dynamic Bayesian framework for sensor estimation, a critical step of many machine condition monitoring systems. The temporal behavior of normal sensor data is described by a stationary switching autoregressive (SSAR) model that possesses two advantages over traditional switching autoregressive (SAR) models. First, the SSAR model removes time dependency of signals during mode switching and fits sensor data better. Secondly, the SSAR model is stationary in that at each time, sensor data have the same distribution which represents the normal operating range of a system; this ensures that estimates are accurate and are not distracted by deviations. During monitoring the deviation covariance is estimated adaptively, which effectively handles variable levels of deviations. Tests on gas turbine data are presented.