Noise and Performances Analysis of Commerical Aircrafts using Artificial Neural Networks

Commerical aircrafts are very important part for airway travelling. In spite of high technology on aircrafts, there is still fatality accidents in the world. Because of this reason, it is very important criteria to analyse noises of main elements of the air-craft systems. In tis study, an aircraft’s main disturbances are analysed with proposed neural networks. Firstly, the noises of the jet, turbine and fan were measured from the aircraft. Secondly, the measured parameter values were predicted the proposed neural networks. The results of the proposed neuarl approaches were shown that this type of neural predictors will be employed to predict aircrafts unpredicted disturbances in real time applications

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

Neural nonlinear autoregressive model with exogenous input (Narx) for turboshaft aeroengine fuel control unit model†

none3noOne of the most important parts of a turboshaft engine, which has a direct impact on the performance of the engine and, as a result, on the performance of the propulsion system, is the engine fuel control system. The traditional engine control system is a sensor-based control method, which uses measurable parameters to control engine performance. In this context, engine component degradation leads to a change in the relationship between the measurable parameters and the engine performance parameters, and thus an increase of control errors. In this work, a nonlinear model predictive control method for turboshaft direct fuel control is implemented to improve engine response ability also in presence of degraded conditions. The control objective of the proposed model is the prediction of the specific fuel consumption directly instead of the measurable parameters. In this way is possible decentralize controller functions and realize an intelligent engine with the development of a distributed control system. Artificial Neural Networks (ANN) are widely used as data-driven models for modelling of complex systems such as aeroengine performance. In this paper, two Nonlinear Autoregressive Neural Networks have been trained to predict the specific fuel consumption for several transient flight maneuvers. The data used for the ANN predictions have been estimated through the Gas Turbine Simulation Program. In particular the first ANN predicts the state variables based on flight conditions and the second one predicts the performance parameter based on the previous predicted variables. The results show a good approximation of the studied variables also in degraded conditions.openDe Giorgi M.G.; Strafella L.; Ficarella A.De Giorgi, M. G.; Strafella, L.; Ficarella, A

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

A non-linear weighted least squares gas turbine diagnostic approach and multi-fuel performance simulation

The gas turbine which has found numerous applications in Air, Land and Sea applications, as a propulsion system, electricity generator and prime mover, is subject to deterioration of its individual components. In the past, various methodologies have been developed to quantify this deterioration with varying degrees of success. No single method addresses all issues pertaining to gas turbine diagnostics and thus, room for improvement exists. The first part of this research investigates the feasibility of non-linear W eighted Least Squares as a gas turbine component deterioration quantification tool. Two new weighting schemes have been developed to address measurement noise. Four cases have been run to demonstrate the non-linear weighted least squares method, in conjunction with the new weighting schemes. Results demonstrate that the non-linear weighted least squares method effectively addresses measurement noise and quantifies gas path component faults with improved accuracy over its linear counterpart and over methods that do not address measurement noise. Since Gas turbine diagnostics is based on analysis of engine performance at given ambient and power setting conditions; accurate and reliable engine performance modelling and simulation models are essential for meaningful gas turbine diagnostics. The second part of this research therefore sought to develop a multi-fuel and multi-caloric simulation method with the view of improving simulation accuracy. The method developed is based on non-linear interpolation of fuel tables. Fuel tables for Jet-A, UK Natural gas, Kerosene and Diesel were produced. Six case studies were carried out and the results demonstrate that the method has significantly improved accuracy over linear interpolation based methods and methods that assume thermal perfection.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Diagnostic Methods for an Aircraft Engine Performance

The main gas path components, namely compressor and turbine, are inherently reliable but the operation of the aero engines under hostile environments, results into engine breakdowns and performance deterioration. Performance deterioration increases the operating cost, due to the reduction in thrust output and higher fuel consumption, and also increases the engine maintenance cost. In times when economic considerations dominate airline operators’ strategies, carrying out unnecessary rectification, can be very costly and time consuming. In an attempt to minimize such unexpected circumstances, having detailed knowledge prior to any inspection will allow the gas turbine user to take some of the maintenance action when it is necessary. Advanced engine-fault diagnostics tools offer the possibility of identifying degradation at the module level, determining the trends of these degradations during the usage of the engine, and planning the maintenance action ahead

Multi-Objective Optimisation of A Centrifugal Compressor for a Micro Gas Turbine Operated by Concentrated Solar Power

Solar powered micro-gas turbines (MGTs) are required to work over a wide range of operating conditions due to the fluctuations in the solar insulation. This means that the compressor has to perform efficiently over a wider range than in conventional MGTs. To be able to extend the efficient operating range of a compressor at the design stage, both impeller blades and diffuser passage need to be optimised. Vaneless diffusers could offer more flexibility to extend the operating range than typical diffuser vanes. This paper presents a methodology for the design and optimisation of a centrifugal compressor for a 6 kW micro-gas turbine intended for operation using a Concentrated Solar Power (CSP) system using a parabolic dish concentrator. Preliminary design parameters were obtained from the overall system specifications and detailed cycle analysis combined with practical constraints. The compressor’s geometry optimisation has been performed using a fast and computationally efficient method, which involves the Latin hypercube Design of Experiment (DoE) technique coupled with the response surface method (RSM) in order to build a regression model through CFD simulations. Three different RSM techniques were compared with the aim to choose the most suitable technique for this specific application and then a genetic algorithm was applied. The CFD analysis for the optimised compressor showed that the high efficiency operating range has increased compared to the baseline design. Cycle analysis for the plant has been performed in order to evaluate the effect of the new compressor design on the system performance. The simulations demonstrated that the operating range of the plant was increased by over 30%

Aeronautical engineering: A continuing bibliography with indexes (supplement 272)

This bibliography lists 719 reports, articles, and other documents introduced into the NASA scientific and technical information system in November, 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

Fault diagnostics for advanced cycle marine gas turbine using genetic algorithm

The major challenges faced by the gas turbine industry, for both the users and the manufacturers, is the reduction in life cycle costs , as well as the safe and efficient running of gas turbines. In view of the above, it would be advantageous to have a diagnostics system capable of reliably detecting component faults (even though limited to gas path components) in a quantitative marmer. V This thesis presents the development an integrated fault diagnostics model for identifying shifts in component performance and sensor faults using advanced concepts in genetic algorithm. The diagnostics model operates in three distinct stages. The rst stage uses response surfaces for computing objective functions to increase the exploration potential of the search space while easing the computational burden. The second stage uses the heuristics modification of genetics algorithm parameters through a master-slave type configuration. The third stage uses the elitist model concept in genetic algorithm to preserve the accuracy of the solution in the face of randomness. The above fault diagnostics model has been integrated with a nested neural network to form a hybrid diagnostics model. The nested neural network is employed as a pre- processor or lter to reduce the number of fault classes to be explored by the genetic algorithm based diagnostics model. The hybrid model improves the accuracy, reliability and consistency of the results obtained. In addition signicant improvements in the total run time have also been observed. The advanced cycle Intercooled Recuperated WR2l engine has been used as the test engine for implementing the diagnostics model.SOE Prize winne