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An Improved Particle Filtering-based Approach for Health Prediction and Prognosis of Nonlinear Systems


An Improved Particle Filtering-based Approach for Health Prediction and Prognosis of Nonlinear Systems

Daroogheh, Najmeh, Meskin, Nader and Khorasani, Khashayar (2018) An Improved Particle Filtering-based Approach for Health Prediction and Prognosis of Nonlinear Systems. Journal of the Franklin Institute . (In Press)

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Health monitoring of nonlinear systems is broadly concerned with the system health tracking and its prediction to future time horizons. Estimation and prediction schemes constitute as principle components of any health monitoring technique. Particle filter (PF) represents a powerful tool for performing state and parameter estimation as well as prediction of nonlinear dynamical systems. Estimation of the system parameters along with the states can yield an up-to-date and reliable model that can be used for long-term prediction problems through utilization of particle filters. This feature enables one to deal with uncertainty issues in the resulting prediction step as the time horizon is extended. Towards this end, this paper presents an improved method to achieve uncertainty management for long-term prediction of nonlinear systems by using particle filters. In our proposed approach, an observation forecasting scheme is developed to extend the system observation profiles (as time-series) to future time horizon. Particles are then propagated to future time instants according to a resampling algorithm instead of considering constant weights for the particles propagation in the prediction step. The uncertainty in the long-term prediction of the system states and parameters are managed by utilizing dynamic linear models for development of an observation forecasting scheme. This task is addressed through an outer adjustment loop for adaptively changing the sliding observation injection window based on the Mahalanobis distance criterion. Our proposed approach is then applied to predicting the health condition as well as the remaining useful life (RUL) of a gas turbine engine that is affected by degradations in the system health parameters. Extensive simulation and case studies are conducted to demonstrate and illustrate the capabilities and performance characteristics of our proposed and developed schemes.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Article
Authors:Daroogheh, Najmeh and Meskin, Nader and Khorasani, Khashayar
Journal or Publication:Journal of the Franklin Institute
Date:13 March 2018
Digital Object Identifier (DOI):10.1016/j.jfranklin.2018.02.023
Keywords:Health monitoring; Prognosis; Particle filters; State and parameter prediction; Observation forecasting; Dynamically linear models; Gas turbine engine; Fouling; Erosion
ID Code:983568
Deposited On:16 Mar 2018 18:43
Last Modified:15 Mar 2019 19:31


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