Model based approach and algorithm for fault diagnosis and prognosis of coated gas turbine blades

2010 ◽  
Author(s):  
Amar Kumar ◽  
Alka Srivastava ◽  
Nita Goel ◽  
Amiya Nayak
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Turbine Blades ◽  
Gas Turbine Blades ◽  
Model Based ◽  
Diagnosis And Prognosis ◽  
Fault Diagnosis And Prognosis

scholarly journals Model-based fault diagnosis and prognosis of dynamic systems: a review

2019 ◽  
Vol 30 ◽  
pp. 435-442 ◽  
Author(s):  
Thushara Ekanayake ◽  
Devapriya Dewasurendra ◽  
Sunil Abeyratne ◽  
Lin Ma ◽  
Prasad Yarlagadda
Keyword(s):  
Fault Diagnosis ◽  
Dynamic Systems ◽  
Model Based ◽  
Diagnosis And Prognosis ◽  
Fault Diagnosis And Prognosis

Fault Diagnosis and Prognosis for Fuel Supply System in Gas Turbine Engines

2005 ◽  
Author(s):  
Kyusung Kim ◽  
Onder Uluyol ◽  
Charles Ball
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Supply System ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Fuel Supply ◽  
Diagnosis And Prognosis ◽  
Fuzzy Clustering Method ◽  
Fuel Supply System ◽  
Fault Diagnosis And Prognosis

A fault diagnosis and prognosis method is developed for the fuel supply system in gas turbine engines. The engine startup profiles of the core speed (N2) and the exhaust gas temperature (EGT) collected with high speed sampling rate are extracted and processed into a more compact data set. The fuzzy clustering method is applied to the smaller number of parameters and the fault is detected by differentiating the clusters matching the failures. In this work, the actual flight data collected in the field is used to develop and validate the system, and the results are shown for the test on nine engines that experienced fuel supply system failure. The developed fault diagnosis system detects the failure successfully for all nine cases. For the earliest detection cases, the alarms start to trigger 26 days before the system completely fails and 7 days in advance for the last detection.

scholarly journals Long-short term memory and gas path analysis based gas turbine fault diagnosis and prognosis

2021 ◽  
Vol 13 (8) ◽  
pp. 168781402110377
Author(s):  
Hongyu Zhou ◽  
Yulong Ying ◽  
Jingchao Li ◽  
Yaofei Jin
Keyword(s):  
Fault Diagnosis ◽  
Path Analysis ◽  
Gas Turbine ◽  
Short Term Memory ◽  
Fault Prediction ◽  
Short Term ◽  
Term Memory ◽  
Diagnosis And Prognosis ◽  
Long Short Term Memory ◽  
Fault Diagnosis And Prognosis

At present, the main purpose of gas turbine fault prediction is to predict the performance decline trend of the whole system, but the quantitative and thorough performance health index (PHI) research of every major component is lacking. Regarding the issue above, a long-short term memory and gas path analysis (GPA) based gas turbine fault diagnosis and prognosis method is proposed, which realizes the coupling of fault diagnosis and prognosis process. The measurable gas path parameters (GPPs) and the health parameters (HP) of every main component of the goal engine are obtained through the adaptive modeling strategy and the gas path diagnosis method based on the thermodynamic model. The predictive model of the Long-Short Term Memory (LSTM) network combines the measurable GPPs and the diagnostic HPs to predict the HPs of each major component in the future. Simulation experiments show that the proposed method can effectively diagnose and predict detailed, quantified, and accurate PHIs of the main components. Among them, the maximum root mean square error (RMSE) of the diagnosed component HPs do not exceed 0.193%. The RMSE of the best prediction model is 0.232%, 0.029%, 0.069%, and 0.043% in the HP prediction results of each component, respectively.

Fault diagnosis and prognosis for fuel supply systems in gas turbine engines

2008 ◽  
Vol 223 (3) ◽  
pp. 757-768 ◽  
Author(s):  
K Kim
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Supply System ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Fuel Supply ◽  
Diagnosis And Prognosis ◽  
Fuzzy Clustering Method ◽  
Fuel Supply System ◽  
Fault Diagnosis And Prognosis

This paper introduces a feature-extraction method to characterize gas turbine engine dynamics. The extracted features are used to develop a fault diagnosis and prognosis method for the fuel supply system in gas turbine engines. The engine start-up profiles of the core speed (N2) and the exhaust gas temperature collected with high-speed sampling rate are obtained and processed into a more compact data set by identifying critical-to-characterization instances. The fuzzy-clustering method is applied to the smaller number of parameters, and the fault is detected by differentiating the clusters matching the failures. In this work, the actual flight data collected in the field was used to develop and validate the system, and the results are shown for the test on nine engines that experienced fuel supply system failure. The developed fault diagnosis system detected the failure successfully in all nine cases. For the earliest detection cases, the alarms start to trigger 26 days before the system completely fails and 7 days in advance for the last detection.

UDIMET L-605

Alloy Digest ◽  
2004 ◽  
Vol 53 (12) ◽  
Keyword(s):  
Corrosion Resistance ◽  
High Temperature ◽  
Physical Properties ◽  
Tensile Properties ◽  
Gas Turbine ◽  
Oxidation Resistance ◽  
Turbine Blades ◽  
Heat Treating ◽  
Gas Turbine Blades ◽  
Temperature Performance

Abstract Udimet L-605 is a high-temperature aerospace alloy with excellent strength and oxidation resistance. It is used in applications such as gas turbine blades and combustion area parts. This datasheet provides information on composition, physical properties, and tensile properties as well as creep. It also includes information on high temperature performance and corrosion resistance as well as forming, heat treating, and joining. Filing Code: CO-109. Producer or source: Special Metals Corporation.

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