A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature

Performance degradation forecast technology for quantitatively assessing degradation states of aeroengine using exhaust gas temperature is an important technology in the aeroengine health management. In this paper, a GM (1, 1) Markov chain-based approach is introduced to forecast exhaust gas temperature by taking the advantages of GM (1, 1) model in time series and the advantages of Markov chain model in dealing with highly nonlinear and stochastic data caused by uncertain factors. In this approach, firstly, the GM (1, 1) model is used to forecast the trend by using limited data samples. Then, Markov chain model is integrated into GM (1, 1) model in order to enhance the forecast performance, which can solve the influence of random fluctuation data on forecasting accuracy and achieving an accurate estimate of the nonlinear forecast. As an example, the historical monitoring data of exhaust gas temperature from CFM56 aeroengine of China Southern is used to verify the forecast performance of the GM (1, 1) Markov chain model. The results show that the GM (1, 1) Markov chain model is able to forecast exhaust gas temperature accurately, which can effectively reflect the random fluctuation characteristics of exhaust gas temperature changes over time

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Micro turbojets are used for propelling radio-controlled aircraft, aerial targets, and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional artificial neural network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and exhaust gas temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions

REAL TIME PROGNOSTIC STRATEGIES APPLICATION TO GAS TURBINES

Gas turbines are increasingly deployed throughout the world to provide electrical and mechanical power in consumer and industrial sectors. The efficiency of these complex multi-domain systems is dependant on the turbine\u27s design, established operating envelope, environmental conditions, and maintenance schedule. A real-time health management strategy can enhance overall plant reliability through the continual monitoring of transient and steady-state system operations. The availability of sensory information for control system needs often allow diagnostic/prognostic algorithms to be executed in a parallel fashion which warn of impending system degradations. Specifically, prognostic strategies estimate the future plant behavior which leads to minimized maintenance costs through timely repairs, and hence, improved reliability. A health management system can incorporate prognostic algorithms to effectively interpret and determine the healthy working span of a gas turbine. The research project\u27s objective is to develop real-time monitoring and prediction algorithms for simple cycle natural gas turbines to forecast short and long term system behavior. Two real-time statistical and wavelet prognostic methods have been investigated to predict system operation. For the statistical approach, a multi-dimensional empirical description reveals dominant data trends and estimates future behavior. The wavelet approach uses second and fourth-order Daubechies wavelet coefficients to generate signal approximations that forecast future plant operation. To complement the empirical models, a real-time analytical, lumped parameter mathematical model has been developed that describes normal transient and steady-state gas turbine system operation. The model serves as the basis to understand a simple cycle gas turbine\u27s operation, and may be utilized in model-based diagnostic algorithms. To validate the model and the prognostic strategies, extensive data has been gathered for a 4.5 MW Solar Mercury 50 and a 85 MW General Electric 7EA simple cycle gas turbine. For the dynamic gas turbine model, the comparison between the field data and simulation results for five Mercury 50 gas turbine signals (e.g., shaft speed, power, fuel flow, turbine rotor inlet temperature, and compressor delivery pressure) demonstrate a high degree of correspondence. Although there are some deviations between the analytical and experimental results during the transient phase, the estimated steady state results are within 2.0% of the actual data. The direct comparison of the two forecasting methods revealed that the wavelet method is superior since the forecasting error is 2.4% versus 4.0% for the statistical method on the Mercury 50 simple cycle gas turbine steady-state signals (e.g., compressor delivery pressure and turbine rotor inlet temperature). Similarly, the General Electric 7EA steady-state signal (e.g., turbine inlet temperature) offered a forecasting error of 9.23% for the wavelet and 11.47% for the statistical methods, respectively. The developed approaches successfully estimate and predict the system operation and may be used with a diagnostic algorithm to monitor gas turbine system health. An excellent opportunity exists to apply the algorithms to gas turbines for improved operation and reliability

LNG TURBOMACHINERY

TutorialThe International Liquefied Natural Gas (LNG) trade is expanding rapidly. Projects are being proposed worldwide to meet the industry forecasted growth rate of 12% by the end of the decade. LNG train designs in the coming years appear to fall within three classes, having nominal capacities of approximately 3.5, 5.0 and 8.0 MTPA (Million Tons Per Annum). These designs may co-exist in the coming years, as individual projects choose designs, which closely match their gas supplies, sales, and other logistical and economic constraints. The most critical components of a LNG liquefaction facility are the refrigeration compressors and their drivers which represent a significant expense and strongly influence overall plant performance and production efficiency. The refrigeration compressors themselves are challenging to design due to high Mach numbers, large volume flows, low inlet temperatures and complex sidestream flows. Drivers for these plants include gas turbines that range in size from 30 MW units to large Frame 9E gas turbines. Aeroderivative engines have also been recently introduced. This paper covers the design, application and implementation considerations pertaining to LNG plant drivers and compressors. The paper does not focus on any particular LNG process but addresses turbomachinery design and application aspects that are common to all processes. Topics cover key technical design issues and complexities involved in the turbomachinery selection, aeromechanical design, testing and implementation. The paper attempts to highlight the practical design compromises that have to be made to obtain a robust solution from a mechanical and aerodynamic standpoint

Aeronautical engineering: A continuing bibliography with indexes (supplement 234)

This bibliography lists 539 reports, articles, and other documents introduced into the NASA scientific and technical information system in December, 1988. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Aeronautical engineering: A continuing bibliography with indexes (supplement 271)

This bibliography lists 666 reports, articles, and other documents introduced into the NASA scientific and technical information system in October, 1991. Subject coverage includes design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction

Aiming at the nonlinearity, chaos, and small-sample of aero engine performance parameters data, a new ensemble model, named the least squares support vector machine (LSSVM) ensemble model with phase space reconstruction (PSR) and particle swarm optimization (PSO), is presented. First, to guarantee the diversity of individual members, different single kernel LSSVMs are selected as base predictors, and they also output the primary prediction results independently. Then, all the primary prediction results are integrated to produce the most appropriate prediction results by another particular LSSVM—a multiple kernel LSSVM, which reduces the dependence of modeling accuracy on kernel function and parameters. Phase space reconstruction theory is applied to extract the chaotic characteristic of input data source and reconstruct the data sample, and particle swarm optimization algorithm is used to obtain the best LSSVM individual members. A case study is employed to verify the effectiveness of presented model with real operation data of aero engine. The results show that prediction accuracy of the proposed model improves obviously compared with other three models

Aeronautical engineering: A continuing bibliography with indexes (supplement 276)

This bibliography lists 705 reports, articles, and other documents introduced into the NASA scientific and technical information system in Feb. 1992. Subject coverage includes: design, construction, and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Machine-learning-based condition assessment of gas turbine: a review

Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machinelearning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.This research was funded by Siemens Energy.Peer ReviewedPostprint (published version