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

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

a diagnostics tool for aero engines health monitoring using machine learning technique

Abstract In this work an integrated heath monitoring platform is proposed and developed for performance analysis and degradation diagnostics of gas turbine engines. The aim is to link engine measurable data to its health status. A numerical tool has been implemented in order to calculate engine performance in design condition and to create a database of expected vales. Then different degradation levels have been introduced in the two main components, compressor and turbine of a single spool turbojet and the diagnostics instruments have been trained to detect the component fault. In order to evaluate the performance prediction two different machine learning based techniques, namely, artificial neural network (ANN) and support vector machine (SVM) have been compared. Synthetic data generation has been carried out to show how the degradation effects can affect the engine performance. The two main degradation causes considered are the compressor fouling and turbine erosion. The machine learning techniques were applied with two aims: aero-engine performance prediction and health diagnostics. The study was carried out based on three samples flights, whose data were used for the training and testing process of the prediction and diagnostics tools. The knowledge and the continuous monitoring of the engine health status can be crucial for maintenance and fleet management operations

World’s First Aeroderivative Based LNG Liquefaction Plant – Design, Operational Experience and Debottlenecking

LectureThe Darwin LNG Facility is the world’s first liquefaction facility to utilize high efficiency aeroderivative gas turbines for its refrigeration compressors. The plant’s design, startup, successful operation for over four years, upgrade, and debottlenecking are described in this paper. The application of aeroderivative engines allows a significantly lower CO2 footprint of 20-30% compared to the use of simple cycle industrial (heavy duty) gas turbines. This paper will cover the design of all of the turbomachinery, testing of machinery, startup, operational experiences, and debottlenecking activities in which the engines were upgraded. The plant was successfully commissioned and the first LNG cargo was shipped on February 14, 2006. Debottlenecking activities were completed in 2010

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

Review of experimental research on supercritical and transcritical thermodynamic cycles designed for heat recovery application

Supercritical operation is considered a main technique to achieve higher cycle efficiency in various thermodynamic systems. The present paper is a review of experimental investigations on supercritical operation considering both heat-to-upgraded heat and heat-to-power systems. Experimental works are reported and subsequently analyzed. Main findings can be summarized as: steam Rankine cycles does not show much studies in the literature, transcritical organic Rankine cycles are intensely investigated and few plants are already online, carbon dioxide is considered as a promising fluid for closed Brayton and Rankine cycles but its unique properties call for a new thinking in designing cycle components. Transcritical heat pumps are extensively used in domestic and industrial applications, but supercritical heat pumps with a working fluid other than CO2 are scarce. To increase the adoption rate of supercritical thermodynamic systems further research is needed on the heat transfer behavior and the optimal design of compressors and expanders with special attention to the mechanical integrity

GAS TURBINE PERFORMANCE DETERIORATION AND COMPRESSOR WASHING

TutorialThe privatization of utilities, intense competition in the petrochemical and gas distribution industries, coupled with increasing fuel costs, have created a strong incentive for gas turbine operators to minimize and control performance deterioration. The most significant deterioration problem faced by gas turbine operators is compressor fouling which is the focus of this paper. The effect of compressor fouling is a drop in airflow, pressure ratio and compressor efficiency, resulting in a rematching of the gas turbine and compressor and a drop in power output and thermal efficiency. This paper provides a comprehensive practical treatment of the causes, effects and control of fouling. Gas turbine inlet filtration, fouling mechanisms and compressor washing are also covered in detail. The major emphasis will be on the causes, effects detection and control of compressor fouling. The complexities and challenges of on-line washing of large output new gas turbines will also be covered. The treatment also applies to axial air compressors used in the hydrocarbon processing industry

Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies

Maintenance is crucial for aircraft engines because of the demanding conditions to which they are exposed during operation. A proper maintenance plan is essential for ensuring safe flights and prolonging the life of the engines. It also plays a major role in managing costs for aeronautical companies. Various forms of degradation can affect different engine components. To optimize cost management, modern maintenance plans utilize diagnostic and prognostic techniques, such as Engine Health Monitoring (EHM), which assesses the health of the engine based on monitored parameters. In recent years, various EHM systems have been developed utilizing computational techniques. These algorithms are often enhanced by utilizing data reduction and noise filtering tools, which help to minimize computational time and efforts, and to improve performance by reducing noise from sensor data. This paper discusses the various mechanisms that lead to the degradation of aircraft engine components and the impact on engine performance. Additionally, it provides an overview of the most commonly used data reduction and diagnostic and prognostic techniques