Adaptive gas path diagnostics using strong tracking filter

Abstract

Kalman filters are very popular in gas path diagnostics. This algorithm estimates the engine state variables to assess engine health conditions and is accurate in tracking gradual deterioration. However, the performance of the Kalman filter deteriorates when an abrupt fault occurs. There could be a long delay with the Kalman filter in diagnosing the abrupt fault. In addition, the Kalman filter may transfer the abrupt fault on to other components. In this article, an adaptive gas path diagnostic method using strong tracking filter is described that can track gradual deterioration and abrupt fault accurately. The strong tracking filter is an adaptive extended Kalman filter, which introduces suboptimal fading factors into the prediction error covariance of the extended Kalman filter algorithm. The suboptimal fading factors automatically increase when an abrupt fault occurs, therefore, more importance is given to the new measurement in state estimation which allows the filter to quickly track abrupt faults. All of the suboptimal fading factors become one when gradual deterioration occurs, and in this situation, the strong tracking filter becomes the common extended Kalman filter to filter the measurement noise. Therefore, the strong tracking filter can track abrupt faults quickly and accurately, filter measurement noise, and obtain noise-free parameter estimation for gradual deterioration. The strong tracking filter is applied to heavy-duty gas turbine gas path diagnostics for a variety of simulated fault cases to demonstrate the capability of the strong tracking filter in accurately tracking gradual deterioration and abrupt fault.