Particle filtering-based system degradation prediction applied to jet engines

This paper investigates a real-time fault detection and degradation prediction scheme for dynamical systems such as jet engines, based on Regularized Particle Filtering (RPF). Particle Filtering is a prognosis method for the prediction of state degradation and remaining useful life (RUL) due to its demonstrated performance in handling non-linear and non-Gaussian situations. RPF overcomes the problem of sample impoverishment among particles over the resampling process. Based on measured data from hybrid sensing and nonlinear models, which link system parameters and degradation state to the measurement, RPF has been applied to establishing a framework for both state and parameter estimation, to achieve prognosis at the component level. In addition, a modified system evolution model is proposed to track both exponential and transient types of system performance degradation. The developed method is evaluated using simulated data created with CMAPSS, which contains measured parameters associated with engine degradation under nominal and varied fault types (fan, compressor and turbine) during a series of flights. The developed system-parameter estimation method is found effective in state estimation and degradation prediction in jet engines.