TY - GEN
T1 - Particle filtering-based system degradation prediction applied to jet engines
AU - Wang, Peng
AU - Gao, Robert X.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:84920509946
T3 - PHM 2014 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2014
SP - 658
EP - 663
BT - PHM 2014 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2014
A2 - Bregon, Anibal
A2 - Daigle, Matthew J.
T2 - 2014 Annual Conference of the Prognostics and Health Management Society, PHM 2014
Y2 - 29 September 2014 through 2 October 2014
ER -