Login | Register

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

Title:

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)

[img]
Text (application/pdf)
An Improved Particle Filtering-based Approach for Health Prediction and Prognosis of Nonlinear Systems.pdf - Accepted Version
Restricted to Repository staff only until 12 March 2020.
Available under License Spectrum Terms of Access.
3MB

Official URL: https://www.sciencedirect.com/science/article/pii/...

Abstract

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
Refereed:Yes
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 By: MICHAEL BIRON
Deposited On:16 Mar 2018 18:43
Last Modified:15 Mar 2019 19:31

References:

R. Sekhon, H. Bassily, J. Wagner A comparison of two trending strategies for gas turbine performance prediction Journal of Engineering for Gas Turbines and Power, 130 (4) (2008), p. 041601

A. Grall, L. Dieulle, C. Berenguer, M. Roussignol Continuous-time predictive-maintenance scheduling for a deteriorating system IEEE Transactions on Reliability,, 51 (2) (2002), pp. 141–150

H.-Q. Gu, S. Zhang, L. Ma Process analysis for performance evaluation of prognostics methods orienting to engineering application International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (2012)

P. Shetty, D. Mylaraswamy, T. Ekambaram A hybrid prognostic model formulation and health estimation of auxiliary power unitsJournal of Engineering for Gas Turbines and Power, 130 (2) (2008)

M.E. Orchard, G.J. Vachtsevanos A particle-filtering approach for on-line fault diagnosis and failure prognosis Transactions of the Institute of Measurement and Control (2009)

J. Son, Q. Zhou, S. Zhou, X. Mao, M. Salman Evaluation and comparison of mixed effects model based prognosis for hard failure IEEE Transactions on Reliability, 62 (2) (2013), pp. 379–394

X.-S. Si, W. Wang, C.-H. Hu, D.-H. Zhou, M.G. Pecht Remaining useful life estimation based on a nonlinear diffusion degradation process IEEE Transactions on Reliability, 61 (1) (2012), pp. 50–67

D. Simon, D.L. Simon Aircraft turbofan engine health estimation using constrained Kalman filtering Journal of engineering for Gas Turbines and Power, 127 (2) (2005), pp. 323–328

P. Baraldi, F. Mangili, E. Zio A Kalman filter-based ensemble approach with application to turbine creep prognostics IEEE Transactions on Reliability, 61 (4) (2012), pp. 966–977

B. Pattipati, C. Sankavaram, K. Pattipati, Y. Zhang, M. Howell, M. Salman Multiple model moving horizon estimation approach to prognostics in coupled systems AUTOTESTCON (2011)

J. Sun, H. Zuo, M. Pecht Advances in sequential Monte Carlo methods for joint state and parameter estimation applied to prognostics Prognostics and System Health Management Conference (PHM-Shenzhen) (2011)

B.E. Olivares, C. Muñoz, M.E. Orchard, J.F. Silva Particle-filtering-based prognosis framework for energy storage devices with a statistical characterization of state-of-health regeneration phenomena IEEE Transactions on Instrumentation and Measurement, 62 (2) (2013), pp. 364–376

N. Daroogheh, N. Meskin, K. Khorasani A novel particle filter parameter prediction scheme for failure prognosis Proceedings of the American Control Conference (2014), pp. 1735–1742

N. Daroogheh, N. Meskin, K. Khorasani Particle filtering for state and parameter estimation in gas turbine engine fault diagnostics Proceedings of the American Control Conference (2013), pp. 4343–4349

N. Daroogheh, N. Meskin, K. Khorasani A dual particle filter-based fault diagnosis scheme for nonlinear systems IEEE Transactions on Control Systems Technology (2017) 10.1109/TCST.2017.2705056

N. Daroogheh, N. Meskin, K. Khorasani, Particle filters for dual state and parameter estimation of nonlinear systems with application to fault diagnosis of gas turbine engines, https://archive.org/details/DualParticleFilter, 2014.

M. West Mixture models, Monte Carlo, Bayesian updating, and dynamic models Computing Science and Statistics (1993) 325–325

J.D. Hamilton Time series analysis, 2Princeton university press Princeton (1994)

D.A. Pola, H.F. Navarrete, M.E. Orchard, R.S. Rabie, M.A. Cerda, B.E. Olivares, J.F. Silva, P.A. Espinoza, A. Perez Particle-filtering-based discharge time prognosis for lithium-ion batteries with a statistical characterization of use profiles IEEE Transactions on Reliability, 64 (2) (2015), pp. 710–720

M. West, J. Harrison Bayesian forecasting and dynamic models, 18 (1997)

A. Soylemezoglu, S. Jagannathan, C. Saygin Mahalanobis-taguchi system as a multi-sensor based decision making prognostics tool for centrifugal pump failures IEEE Transactions on Reliability, 60 (4) (2011), pp. 864–878

M. Orchard, G. Kacprzynski, K. Goebel, B. Saha, G. Vachtsevanos Advances in uncertainty representation and management for particle filtering applied to prognostics International Conference on Prognostics and Health Management (2008)

M. Orchard, F. Tobar, G. Vachtsevanos Outer feedback correction loops in particle filtering-based prognostic algorithms: Statistical performance comparison Studies in Informatics and Control, 18 (4) (2009), pp. 295–304

M. Orchard, L. Tang, B. Saha, K. Goebel, G. Vachtsevanos Risk-sensitive particle-filtering-based prognosis framework for estimation of remaining useful life in energy storage devices Studies in Informatics and Control, 19 (3) (2010), pp. 209–218

A. Saxena, J. Celaya, B. Saha, S. Saha, K. Goebel Metrics for offline evaluation of prognostic performance International Journal of Prognostics and Health Management, 1 (2010), p. 4

A. Saxena, J. Celaya, E. Balaban, K. Goebel, B. Saha, S. Saha, M. Schwabacher Metrics for evaluating performance of prognostic techniques International Conference on Prognostics and Health Management (2008)

S. Uckun, K. Goebel, P. Lucas Standardizing research methods for prognostics International Conference on Prognostics and Health Management (2008)

X. Guan, Y. Liu, R. Jha, A. Saxena, J. Celaya, K. Geobel Comparison of two probabilistic fatigue damage assessment approaches using prognostic performance metrics International Journal of the PHM Society, 1 (005) (2011)

M. Daigle, K. Goebel Multiple damage progression paths in model-based prognostics Aerospace Conference, IEEE (2011)

R. Karlsson, T. Schön, F. Gustafsson Complexity analysis of the marginalized particle filter IEEE Transactions on Signal Processing, 53 (11) (2005), pp. 4408–4411

E. Naderi, N. Meskin, K. Khorasani Nonlinear fault diagnosis of jet engines by using a multiple model-based approach Journal of Engineering for Gas Turbines and Power, 134 (1) (2012), p. 011602

N. Meskin, E. Naderi, K. Khorasani A multiple model-based approach for fault diagnosis of jet engines IEEE Transactions on Control Systems Technology, 21 (1) (2013)

R. Mohammadi, E. Naderi, K. Khorasani, S. Hashtrudi-Zad Fault diagnosis of gas turbine engines by using dynamic neural networks ASME Conference Proceedings, 2010 (43987) (2010), pp. 365–376

W.P.J. Visser, O. Kogenhop, M. Oostveen A generic approach for gas turbine adaptive modeling Journal of Engineering for Gas Turbines and Power, 128 (1) (2006), pp. 13–19

M. Naeem Impacts of low-pressure (lp) compressors fouling of a turbofan upon operational- effectiveness of a military aircraft Applied Energy, 85 (4) (2008), pp. 243–270

N. Daroogheh, A. Vatani, M. Gholamhossein, K. Khorasani Engine life evaluation based on a probabilistic approach ASME 2012 International Mechanical Engineering Congress and Exposition (2012), pp. 347–358

A.C. Harvey Forecasting, Structural Time Series Models and the Kalman Filter Cambridge University Press (1990)

G. Verdier, A. Ferreira Adaptive mahalanobis distance and k -nearest neighbor rule for fault detection in semiconductor manufacturing IEEE Transactions on Semiconductor Manufacturing, 24 (1) (2011), pp. 59–68

L. Ljung System Identification Wiley Online Library (1999)

S. Jaggia Forecasting with ARMA models Case Studies In Business, Industry And Government Statistics, 4 (1) (2014), pp. 59–65

L. Ljung System Identification: Theory for the User Prentice Hall Information and System Sciences Series, New Jersey, 7632 (1987)

E. Ahmed, A. Clark, G. Mohay A novel sliding window based change detection algorithm for asymmetric traffic International Conference on Network and Parallel Computing,, IEEE (2008), pp. 168–175

J.D. Hamilton Time Series Analysis, 2Princeton university press Princeton (1994)

R. Smith Matrix equation xa+bx=c SIAM Journal on Applied Mathematics, 16 (1) (1968), pp. 198–201

N. Oudjane, C. Musso Progressive correction for regularized particle filters Proceedings of the Third International Conference on Information Fusion, 2, IEEE (2000), pp. THB2 10

N. Daroogheh, A. Baniamerian, N. Meskin, K. Khorasani Prognosis and health monitoring of nonlinear systems using a hybrid scheme through integration of pfs and neural networks IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47 (8) (2017), pp. 1990–2004

M. Daigle, I. Roychoudhury, S. Narasimhan, S. Saha, B. Saha, K. Goebel Investigating the effect of damage progression model choice on prognostics performance Proceedings of the Annual Conference of the Prognostics and Health Management Society (2011)

A. Doucet, S. Godsill, C. Andrieu On sequential Monte Carlo sampling methods for Bayesian filtering Statistics and computing, 10 (3) (2000), pp. 197–208

M. Naeem, R. Singh, D. Probert Implications of engine’s deterioration upon an aero-engine hp turbine blade’s thermal fatigue life International journal of fatigue, 22 (2000)

Y. Li, P. Nilkitsaranont Gas turbine performance prognostic for condition-based maintenance Applied Energy, 86 (2009), pp. 2152–2161

G. Hardy, J. Littlewood, G. Polya, Inequalities. reprint of the 1952 edition. cambridge mathematical library, 1988.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top