S.O.T. Ogaji

Parameter selection for diagnosing a gas-turbine's performance-deterioration

The ability to assess faults in a system, while it is operating, requires an appropriate set of measurements. Engine availability can be increased if the faults can be detected, isolated and assessed, so enabling an optimised shutdown of the plant for maintenance to ensue. Depending on the engine-power-setting parameter, the measurements required to diagnose the faults along the gas path of a gas-turbine vary. This study used a non-linear gas-path analysis (NLGPA) model to predict the required instrumentation set, which can be optimised with respect to the number and type of sensors and their locations for the considered engine-faults. A thermodynamic model of the behaviour of a 2-shaft engine is used as a case study. Redundancy in the sensor set is shown to be unnecessary.

Assessment of Future Aero-engine Designs With Intercooled and Intercooled Recuperated Cores

Reduction in CO2 emissions is strongly linked with the improvement of engine specific fuel consumption, as well as the reduction in engine nacelle drag and weight. Conventional turbofan designs, however, that reduce CO2 emissions—such as increased overall pressure ratio designs—can increase the production of NOx emissions. In the present work, funded by the European Framework 6 collaborative project NEW Aero engine Core concepts (NEWAC), an aero-engine multidisciplinary design tool, Techno-economic, Environmental, and Risk Assessment for 2020 (TERA2020), has been utilized to study the potential benefits from introducing heat-exchanged cores in future turbofan engine designs. The tool comprises of various modules covering a wide range of disciplines: engine performance, engine aerodynamic and mechanical design, aircraft design and performance, emissions prediction and environmental impact, engine and airframe noise, as well as production, maintenance and direct operating costs. Fundamental performance differences between heat-exchanged cores and a conventional core are discussed and quantified. Cycle limitations imposed by mechanical considerations, operational limitations and emissions legislation are also discussed. The research work presented in this paper concludes with a full assessment at aircraft system level that reveals the significant potential performance benefits for the intercooled and intercooled recuperated cycles. An intercooled core can be designed for a significantly higher overall pressure ratio and with reduced cooling air requirements, providing a higher thermal efficiency than could otherwise be practically achieved with a conventional core. Variable geometry can be implemented to optimize the use of the intercooler for a given flight mission. An intercooled recuperated core can provide high thermal efficiency at low overall pressure ratio values and also benefit significantly from the introduction of a variable geometry low pressure turbine. The necessity of introducing novel lean-burn combustion technology to reduce NOx emissions at cruise as well as for the landing and take-off cycle, is demonstrated for both heat-exchanged cores and conventional designs. Significant benefits in terms of NOx reduction are predicted from the introduction of a variable geometry low pressure turbine in an intercooled core with lean-burn combustion technology.

Multiple-sensor fault-diagnoses for a 2-shaft stationary gas-turbine

Sensor failures are a major cause of concern in engine-performance monitoring as they can result in false alarms and, in some cases, lead to the condemnation of a non-offending component or section of the engine. This condition has the potential to increase engine downtime and thus incur higher operational costs. The fact that more than a single sensor could be faulty simultaneously should also not be overlooked. In this paper, we present a set of neural networks, modularly designed to diagnose and quantify single and dual-sensor faults in a two-shaft stationary gas-turbine. A further outcome of the analysis is the restructuring of the faulty data to a fault-free form through the filtering out of noise and bias. This restructured data can be used to perform sensor-based calculations accurately. The engine chosen for this analysis is thermodynamically similar in performance to the Rolls Royce (RR) Avon. The data used to train the networks were derived from a non-linear aero-thermodynamic model of the engines behaviour. The results obtained show the good prospects for the use of this technique.

Novel approach for improving power-plant availability using advanced engine diagnostics

Technological advances and high cost of ownership have resulted in considerable interest in advanced maintenance techniques. Quantifying fault and consequently availability requires the use of gas-turbine and combined-cycle models able to undertake appropriate diagnostics and life-cycle costing. These are complex processes as they include the simulation of such issues as performance and assessment of degraded gas-turbines, life usage and risk analysis. This report describes how the recent developments in engine diagnostics using advanced techniques like Artificial Neural Network (ANN) and Genetic Algorithm (GA) based techniques have provided new opportunities in the field of engine-fault diagnostics. It also discusses the potential of advanced engine-diagnostics, employing such features as ANN and GA for contributing to the management of availability of industrial gas-turbines.

EVA: A Tool for Environmental Assessment of Novel Propulsion Cycles

This paper presents the development of a tool for EnVironmental Assessment (EVA) of novel propulsion cycles implementing the Technoeconomical Environmental and Risk Analysis (TERA) approach. For nearly 3 decades emissions certification and legislation has been mainly focused on the landing and take-off cycle. Exhaust emissions measurements of NOx, CO and unburned hydrocarbons are taken at Sea Level Static (SLS) conditions for 4 different power settings (idle, descent, approach and take-off) and are consecutively used for calculating the total emissions during the ICAO landing and take-off cycle. With the global warming issue becoming ever more important, stringent emissions legislation is soon to follow, focusing on all flight phases of an aircraft. Unfortunately, emissions measurements at altitude are either extremely expensive, as in the case of altitude test facility measurements, or unrealistic, as in the case of direct in flight measurements. Compensating for these difficulties, various existing methods can be used to estimate emissions at altitude from ground measurements. Such methods, however, are of limited help when it comes to assessing novel propulsion cycles or existing engine configurations with no SLS measurements available. The authors are proposing a simple and fast method for the calculation of SLS emissions, mainly implementing ICAO exhaust emissions data, corrections for combustor inlet conditions and technology factors. With the SLS emissions estimated, existing methods may be implemented to calculate emissions at altitude. The tool developed couples emissions predictions and environmental models together with engine and aircraft performance models in order to estimate the total emissions and Global Warming Potential of novel engine designs during all flight phases (i.e. the whole flight cycle). The engine performance module stands in the center of all information exchange. In this study, EVA and the described emissions prediction methodology have been used for the preliminary design analysis of three spool high bypass ratio turbofan engines. The capability of EVA to radically explore the design space available in novel engine configurations, while accounting for fuel burn and global warming potential during the whole flight cycle of an aircraft, is illustrated.

Gas Path Fault Diagnosis of a Turbofan Engine From Transient Data Using Artificial Neural Networks

Transient and steady state data may contain the same essential fault information but some faults have been shown to be more easily detectable from transient data because the transient records provide significant diagnostic content especially as the fault effects are magnified under transient. Various traditional and conventional techniques such as fault trees, fault matrixes, gas path analysis and its variants have been applied to gas path fault diagnosis of gas turbines. Recently, artificial intelligence techniques such as artificial neural networks (ANN) as well as optimization techniques such as genetic algorithm (GA) are being explored for fault diagnosis activities. In this paper, a novel approach to gas path fault diagnosis is proposed. The method involves the use of ANN with engine transient data. A set of nested neural networks designed to estimate independent parameter (efficiencies and flow capacities) changes due to faults within single or multiple components of a turbofan engine are presented. The approach involves classification and approximation type networks. Measurements from the engine are first assessed by a trained network and if a fault is diagnosed, are then classified into two groups — those originating from sensor faults and those from component faults, by another trained network. Other trained networks continue the fault isolation process and finally the magnitude of the fault(s) is quantified. A computer simulation of the process shows that results from a batched process of these networks can be obtained in less than three seconds. Four of the gas path components — intermediate pressure compressor (IPC), high pressure compressor (HPC), high pressure turbine (HPT) and low pressure turbine (LPT) — and measurements from eight sensors are considered. Sensor noise and bias are also considered in this analysis. The comparison of fault signatures from a steady state and transient process show that diagnosis with transient data can improve the accuracy of gas turbine fault diagnosis.Copyright

Engine-fault diagnostics:an optimisation procedure

A diagnostic process capable of providing an early warning of a fault in a gas turbine is of tremendous value to the user and can result in substantial financial savings. The approach in the Genetic Algorithm based technique adopted is to treat the problem of engine diagnostics as an optimisation exercise using sensor-based and mathematical behavioural model based information. The engine performance model would simulate a range of possible combinations of potential faults (i.e the effects of model-based information) and a comparison would be made with values of the actual (sensor-based) parameters obtained from an engine. The difference between the actual and simulated values of would be converted into a suitable objective-function and the aim of the optimisation technique such as the genetic algorithm would be to minimise the objective function. The technique has given promising results for simple cycle engines.

Fault Diagnosis of a Two Spool Turbo-Fan Engine Using Transient Data: A Genetic Algorithm Approach

Traditionally engine fault diagnosis has been performed at steady state conditions. There are several problems which can only be detected by transient data analysis like bearing fault, some control problems etc.. In addition, gas turbine performance deviation due to a component fault is more likely to be magnified during transients, when compared with the same parameter deviations at steady states. The specific approach used in this paper is to compare model-based information with measured data obtained from the engine during a slam acceleration. The measured transient data (from actual engine) is compared with a set of simulated data from the engine transient model, under similar operating conditions and known faults through a Cumulative Deviation. The Cumulative Deviations obtained from the comparisons are minimized for the best match using Genetic Algorithm. The Genetic Algorithm has been tailored to use real coding [1] method and to meet the requirements of the new procedure. The paper describes the application of the approach to a 2-spool turbofan engine and discusses the preliminary studies conducted.Copyright

RELIABILITY OF THE AFAM ELECTRIC POWER GENERATING STATION, NIGERIA

Todays economic climate requires that each industry aims at achieving maximum production capability, while minimizing capital investment e.g. in the maintenance function. This means finding ways to maximize equipment reliability and up-time and extend plant and equipment life through cost-effective maintenance. This paper surveys the performance of gas-turbine plants in Afam thermal-power station. The findings show that the financial impact of lost generation (through non-availability) exceeded within a few years, the initial purchase price of the power plants and associated equipment.

An Economic and Risk Analysis Model for Aircrafts and Engines

To conceive and assess engines with minimum global warming impact and lowest cost of ownership in a variety of emission legislation scenarios, emissions taxation policies, fiscal and Air Traffic Management environments, a Techno-economic and Environmental Risk Assessment model is needed. This paper presents an approach to estimate the cost of maintenance and the direct operating costs of turbofan engines of equivalent thrust rating, both for long and short range applications, as well as for typical long and short range aircraft. The economic model is composed of three modules: a lifing module, an economic module and a risk module. The lifing module estimates the life of the high pressure turbine disk and blades through the analysis of creep and fatigue over a full working cycle of the engine. The economic module uses the time between overhauls together with the cost of labour and the cost of the engine (needed to determine the cost of spare parts) to estimate the cost of maintenance of the engine. The risk module uses the Monte Carlo method with a Gaussian distribution to study the impact of the variations in some parameters on the net present cost (NPC) of operation. The accuracy of the economic model in DOC estimation is good (within about 15%) and so can be adapted for use in the cost analysis of future types of engines, such as ultra high bypass ratio turbofans, with little modifications. The equations that constitute the economic model are under a confidentiality agreement of the European project VITAL and can not be divulgated.Copyright