A hybrid prognosis and health monitoring strategy by integrating particle filters and neural networks for gas turbine engines
Author | Daroogheh, N. |
Author | Baniamerian, A. |
Author | Meskin, Nader |
Author | Khorasani, K. |
Available date | 2022-04-14T08:45:42Z |
Publication Date | 2015 |
Publication Name | 2015 IEEE Conference on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHAf Technology and Application, PHM 2015 |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/ICPHM.2015.7245020 |
Abstract | In this paper, a novel hybrid structure is proposed for the development of health monitoring techniques of nonlinear systems by integration of model-based and computationally intelligent and data-driven techniques. In our proposed health monitoring framework, the well-known particle filtering method is utilized to estimate the states as well as the health parameters of the system. Simultaneously, the system observations are predicted through an observation forecasting scheme which is developed based on artificial neural networks to construct observation profiles for future time horizons. As a case study, the proposed approach is applied to predict the health condition of a gas turbine engine when it is affected by degradation damage. 2015 IEEE. |
Sponsor | Qatar National Research Fund |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Algorithms Bandpass filters Degradation Engines Forecasting Gas turbines Health Mathematical models Monte Carlo methods Neural networks Signal filtering and prediction Systems engineering Turbines Data driven technique Health condition Health monitoring Health monitoring technique Health parameters Particle filtering methods Prediction algorithms Prognostics and health managements Structural health monitoring |
Type | Conference Paper |
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Electrical Engineering [2616 items ]