The article reviews traditional and modern methods for prediction of gas turbine operating characteristics and its potential failures. Moreover, a comparison of Machine Learning based prediction models, including Artificial Neural Networks (ANN), is presented. The research focuses on High Pressure Compressor (HPC) recoup pressure level of 4th generation LM2500 gas generator
(LM2500+G4) coupled with a 2-stage High Speed Power Turbine Module. The researched parameter is adjustable and may be
used to balance net axial loads exerted on thrust bearing to ensure stable gas turbine operation, but its direct measurement is
technically difficult implicating the need to indirect measurement via set of other gas turbine sensors. Input data for the research
have been obtained from BHGE manufactured and monitored gas turbines and consists of real-time data extracted from industrial
installations. Machine learning models trained using the data show less than 1% Mean Absolute Percentage Error (MAPE) as
obtained with the use of Random Forest and Gradient Boosting Regression models. Multilayer Perceptron Artificial Neural Networks (MLP ANN) models are reviewed, and their performance checks inferior to Random Forest algorithm-based model. The
importance of hyperparameter tuning and feature engineering is discussed.
REFERENCES(38)
1.
Augustyn S. Energy Model of Change in Technical Condition of Aircraft Power Plants and Space Propulsion Systems. Aviation Advances & Maintenance 2017; 40(2), https://doi.org/10.1515/afit-2....
Badeer G. H. GE Aeroderivative Gas Turbines - Design and Operating Features. GE Power Systems GER-3695E (10/00) available online:https://www.ge.com/content/dam....
Batalha E. Aircraft Engines Maintenance Costs and Reliability. An Appraisal of the Decision Process to Remove an Engine for a Shop Visit Aiming at Minimum Maintenance Unit Cost 2012.
Carlevaro F, Cioncolini S, Sepe M, Parrella I, Escobedo E, Allegorico C, De Stefanis L, Mastroianni M. Use of operating parameters, digital replicas and models for condition monitoring and improved equipment health. ASME Turbo Expo 2018, https://doi.org/10.1115/GT2018....
Cyrus B Meher-Homji C. B, Yates D, Weyermann H. P. Aeroderivative Gas Turbine Drivers for The ConocoPhillips Optimized CascadeSM LNG Process - World's First Application and Future Potential. 15th International Conference & Exhibition on Liquefied Natural Gas 2007 available online: http://www.ivt.ntnu.no/ept/fag....
Deloux E, Castanier B, Berenguer C. Predictive maintenance policy for a gradually deteriorating system subject to stress. Reliability Engineering and System Safety 2009; 94: 418- 431, https://doi.org/10.1016/j.ress....
Demgne J, Mercier S, Lair W, Lonchampt J. Modelling and numerical assessment of a maintenance strategy with stock through Piecewise Deterministic Markov Processes and Quasi Monte Carlo methods. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2017; 231(4): 429-445, https://doi.org/10.1177/174800....
Fulara S, Chmielewski M, Gieras M. Experimental research of the small gas turbine with variable area nozzle. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 2019; 233(15): 5650-5659, https://doi.org/10.1177/095441....
Galar D, Gustafson A, Tormos B, Berges L. Maintenance Decision Making Based on Different Types of Data Fusion. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2012; 14 (2): 135-144.
Garcia Nieto P.J, Garcia-Gonzales E, Sanches Lasheras F, de Cos Juez F.J. Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliability Engineering & System Safety 2015; 138: 219-231, https://doi.org/10.1016/j.ress....
GE Power & Water Distributed Power. LM2500+ G4 BaseGas Turbine Fact Sheet available online: https://www.ge.com/content/dam... accessed on September 2018.
Heaton J. Automated Feature Engineering for Deep Neural Networks with Genetic Programming. College of Engineering and Computing Nova Southeastern University 2017.
Herbert L. Designing for Reliability, Maintainability, and Sustainability (RM&S) in Military Jet Fighter Aircraft Engines, Massachusetts Institute of Technology 2002.
Huang B, Wang Z. The Role of Data Prefiltering For Integrated Identification and Model Predictive Control. IFAC Proceedings Volumes 1999; 32(2): 6751-6756, https://doi.org/10.1016/S1474-....
Huang H.Z, Tong X, Zuo M. J. Posbist fault tree analysis of coherent systems. Reliability Engineering & System Safety 2004; 84(2): 141-148, https://doi.org/10.1016/j.ress....
Jankowski A, Kowalski M. Creating Mechanisms of Toxic Substances Emission of Combustion Engines. Journal of KONBiN 2015; 36(1):33-42, https://doi.org/10.1515/jok-20....
Kopytov E, Labendik V, Yunusov S, Tarasov A. Managing and Control of Aircraft Power Using Artificial Neural Networks. Proceeding of the 7th International Conference "Reliability and Statistics in Transportation and Communication 2007.
Kumar U. D, Crocker J, Knezevic J. Evolutionary Maintenance for Aircraft Engines. Proceedings Annual Reliability and Maintainability Symposium 1999: 62-68.
Liu D, Zhang H, Polycarpou M, Alippi C, He H. Elman-Style Process Neural Network with Application to Aircraft Engine Health Condition Monitoring. Advances in Neural Networks. Proceedings of the 8th International Symposium on Neural Networks 2011.
Lu J-M, Innal F, Wu X-Y, Liu Y, Lundteigen M. A. Two-terminal Reliability Analysis for Multi-Phase Communication Networks. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2016; 18(3): 418-427, https://doi.org/10.17531/ein.2....
Lu J-M, Lundteigen M. A, Liu Y, Wu X-Y. Flexible Truncation Method for The Reliability Assessment of Phased Mission Systems with Repairable Components. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2016; 18 (2): 229-236, https://doi.org/10.17531/ein.2....
Michelassi V, Allegorico C, Cioncolini S, Graziano A, Tognarelli L, Sepe M. Machine learning in gas turbines, from component design to asset management. ASME Journal: Global Gas Turbine News 2018; 140(09): 54-55,https://doi.org/10.1115/1.2018....
Mingazov B. G, Korobitsin N. A. Use of Probability Indices for Assessment of Gas Turbine Power Station Reliability under Commercial Operation Conditions. Russian Aeronautics 2009; 53(2): 226-229, https://doi.org/10.3103/S10687....
Pawełczyk M, Bibik P. Wykorzystanie nowoczesnych narzędzi inżynierskich w projektowaniu bezzałogowego wiropłata czterowirnikowego. Materiały IX Krajowego Forum Wiropłatowego. Instytut Lotnictwa 2013, https://doi.org/10.5604/050966....
Simon D.L. An Integrated Architecture for On-Board Aircraft Engine Performance Trend Monitoring and Gas Path Fault Diagnostics. 57th Joint Army-Navy-NASA-Air Force (JANNAF) Propulsion Meeting sponsored by the JANNAF Interagency Propulsion Committee 2010.
Tibshirani R, Friedman J, Hastie T. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software. 2010; 33(1), https://doi.org/10.18637/jss.v....
Verma M, Kumar A. A novel general approach to evaluating the reliability of gas turbine system. Engineering Applications of Artificial Intelligence 2014; 28: 13-21, https://doi.org/10.1016/j.enga....
An attempt at applying machine learning in diagnosing marine ship engine turbochargers Andrzej Adamkiewicz, Piotr Nikończuk Eksploatacja i Niezawodnosc - Maintenance and Reliability
A Novel Approach for Predicting the Height of Water-Conducting Fracture Zone under the High Overburden Caving Strength Based on Optimized Processes Tao Hu, Gongyu Hou, Su Bu, Zhen Zhu, Yan Wang, Ziyi Hu, Zixiang Li Processes
Maintenance Scope Optimization, through a Real Time Prediction of Risk of Failure Marzia Sepe, Gionata Ruggiero, Alessandro Leto, Gabriele Mordacci, Adolfo Agresta Day 4 Fri, March 25, 2022
A tool wear condition monitoring approach for end milling based on numerical simulation Qinsong Zhu, Weifang Sun, Yuqing Zhou, Chen Gao Eksploatacja i Niezawodnosc - Maintenance and Reliability
Feature selection and feature learning in machine learning applications for gas turbines: A review Jiarui Xie, Manuel Sage, Yaoyao Zhao Engineering Applications of Artificial Intelligence
Predictive modelling of turbofan engine components condition using machine and deep learning methods Michał Matuszczak, Mateusz Żbikowski, Andrzej Teodorczyk Eksploatacja i Niezawodnosc - Maintenance and Reliability
Continual Learning for anomaly detection on turbomachinery prototypes - A real application Valentina Gori, Giacomo Veneri, Valeria Ballarini 2022 IEEE Congress on Evolutionary Computation (CEC)
A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets Marzia Sepe, Antonino Graziano, Maciej Badora, Stazio Di, Luca Bellani, Michele Compare, Enrico Zio Journal of the Global Power and Propulsion Society
Predicting length of fatigue cracks by means of machine learning algorithms in the small-data regime Maciej Badora, Marzia Sepe, Marcin Bielecki, Antonino Graziano, Tomasz Szolc Eksploatacja i Niezawodnosc - Maintenance and Reliability
A sequential cross-product knowledge accumulation, extraction and transfer framework for machine learning-based production process modelling Jiarui Xie, Chonghui Zhang, Manuel Sage, Mutahar Safdar, Yaoyao Zhao International Journal of Production Research
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.