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AuthorDaroogheh, N.
AuthorBaniamerian, A.
AuthorMeskin, Nader
AuthorKhorasani, K.
Available date2022-04-14T08:45:42Z
Publication Date2015
Publication Name2015 IEEE Conference on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHAf Technology and Application, PHM 2015
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ICPHM.2015.7245020
URIhttp://hdl.handle.net/10576/29798
AbstractIn 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.
SponsorQatar National Research Fund
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectAlgorithms
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
TitleA hybrid prognosis and health monitoring strategy by integrating particle filters and neural networks for gas turbine engines
TypeConference Paper
dc.accessType Abstract Only


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