TY - JOUR
T1 - Estimation and monitoring of unmeasured gas turbine variables
AU - Miró-Zárate, Luis Angel
AU - Loboda, Igor
AU - Pérez-Ruiz, Juan Luis
AU - Toledo-Velázquez, Miguel
N1 - Publisher Copyright:
© 2019 NRCC. All rights reserved.
PY - 2019
Y1 - 2019
N2 - This work proposes a universal data-driven approach to compute and monitor gas turbine unmeasured variables. To this end, a large amount of unmeasured and measured data is first computed at steady state for both baseline and faulty engine conditions using a nonlinear thermodynamic model. On the data generated, polynomial models that relate the unmeasured quantities with the measured variables are then determined. These data-driven models allow the computation of unmeasured variables and their deviations. Accuracy analysis is conducted separately for baseline and current estimates of unmeasured variables and for deviation estimates. All the results prove that the estimates are exact enough. Thus it is possible to obtain a universal fast and accurate method for computing important unmeasured gas turbine quantities that is suitable for practical applications. The method promises a drastic increase in the diagnostic capabilities of online monitoring systems.
AB - This work proposes a universal data-driven approach to compute and monitor gas turbine unmeasured variables. To this end, a large amount of unmeasured and measured data is first computed at steady state for both baseline and faulty engine conditions using a nonlinear thermodynamic model. On the data generated, polynomial models that relate the unmeasured quantities with the measured variables are then determined. These data-driven models allow the computation of unmeasured variables and their deviations. Accuracy analysis is conducted separately for baseline and current estimates of unmeasured variables and for deviation estimates. All the results prove that the estimates are exact enough. Thus it is possible to obtain a universal fast and accurate method for computing important unmeasured gas turbine quantities that is suitable for practical applications. The method promises a drastic increase in the diagnostic capabilities of online monitoring systems.
KW - Data-driven approach
KW - Diagnostics
KW - Gas turbine
KW - Monitoring system
KW - Unmeasured variables
UR - http://www.scopus.com/inward/record.url?scp=85062334440&partnerID=8YFLogxK
U2 - 10.1139/tcsme-2017-0009
DO - 10.1139/tcsme-2017-0009
M3 - Artículo
SN - 0315-8977
VL - 43
SP - 26
EP - 37
JO - Transactions of the Canadian Society for Mechanical Engineering
JF - Transactions of the Canadian Society for Mechanical Engineering
IS - 1
ER -