Derivative-driven window-based regression method for gas turbine performance prognostics

Autor: Elias Tsoutsanis, Nader Meskin
Rok vydání: 2017
Předmět:
Engineering
Second order derivatives
02 engineering and technology
regression analysis
Industrial and Manufacturing Engineering
Automotive engineering
0202 electrical engineering
electronic engineering
information engineering
MATLAB
computer.programming_language
Condition-based maintenance
turbine
methodology
Data handling
Renewable energy resources
Pollution
General Energy
Metric (mathematics)
Nonlinear diagnostics
Prognostics
Gases
Gas turbines
020209 energy
Renewable energy source
maintenance
Acceleration
020401 chemical engineering
Gas turbine performance
0204 chemical engineering
Electrical and Electronic Engineering
Simulation
accuracy assessment
Civil and Structural Engineering
Second derivative
real time
engine
software
business.industry
Mechanical Engineering
Building and Construction
Remaining useful lives
Nonlinear system
Condition based maintenance
Window-based
Diagnostics and prognostics
energy resource
Environmental regulations
performance assessment
business
computer
Energy (signal processing)
Zdroj: Energy. 128:302-311
ISSN: 0360-5442
1873-6785
DOI: 10.1016/j.energy.2017.04.006
Popis: The domination of gas turbines in the energy arena is facing many challenges from environmental regulations and the plethora of renewable energy sources. The gas turbine has to operate under demand-driven modes and its components consume their useful life faster than the engines of the base-load operation era. As a result the diagnostics and prognostics tools should be further developed to cope with the above operation modes and improve the condition based maintenance (CBM). In this study, we present a derivative-driven diagnostic pattern analysis method for estimating the performance of gas turbines under dynamic conditions. A real time model-based tuner is implemented through a dynamic engine model built in Matlab/Simulink for diagnostics. The nonlinear diagnostic pattern is then partitioned into data-windows. These are the outcome of a data analysis based on the second order derivative which corresponds to the acceleration of degradation. Linear regression is implemented to locally fit the detected deviations and predict the engine behavior. The accuracy of the proposed method is assessed through comparison between the predicted and actual degradation by the remaining useful life (RUL) metric. The results demonstrate and illustrate an improved accuracy of our proposed methodology for prognostics of gas turbines under dynamic modes. 2017 Elsevier Ltd Qatar Foundation; Qatar National Research Fund Scopus
Databáze: OpenAIRE
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