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The gas turbine which has found numerous applications in Air, Land and Seaapplications, as a propulsion system, electricity generator and prime mover, issubject to deterioration of its individual components. In the past, variousmethodologies have been developed to quantify this deterioration with varyingdegrees of success. No single method addresses all issues pertaining to gasturbine diagnostics and thus, room for improvement exists. The first part of thisresearch investigates the feasibility of non-linear W eighted Least Squares as agas turbine component deterioration quantification tool. Two new weightingschemes have been developed to address measurement noise. Four caseshave been run to demonstrate the non-linear weighted least squares method, inconjunction with the new weighting schemes. Results demonstrate that thenon-linear weighted least squares method effectively addresses measurementnoise and quantifies gas path component faults with improved accuracy over itslinear counterpart and over methods that do not address measurement noise. Since Gas turbine diagnostics is based on analysis of engine performance atgiven ambient and power setting conditions; accurate and reliable engineperformance modelling and simulation models are essential for meaningful gasturbine diagnostics. The second part of this research therefore sought todevelop a multi-fuel and multi-caloric simulation method with the view ofimproving simulation accuracy. The method developed is based on non-linearinterpolation of fuel tables. Fuel tables for Jet-A, UK Natural gas, Kerosene andDiesel were produced. Six case studies were carried out and the resultsdemonstrate that the method has significantly improved accuracy over linearinterpolation based methods and methods that assume thermal perfection.
Widely used for power generation, gas turbine engines are susceptible to faults due to the harsh working environment. Most engine problems are preceded by a sharp change in measurement deviations compared to a baseline engine, but the trend data of these deviations over time are contaminated with noise and non-Gaussian outliers. Gas Turbine Diagnostics: Signal Processing and Fault Isolation presents signal processing algorithms to improve fault diagnosis in gas turbine engines, particularly jet engines. The algorithms focus on removing noise and outliers while keeping the key signal features that may indicate a fault. The book brings together recent methods in data filtering, trend shift detection, and fault isolation, including several novel approaches proposed by the author. Each method is demonstrated through numerical simulations that can be easily performed by the reader. Coverage includes: Filters for gas turbines with slow data availability Hybrid filters for engines equipped with faster data monitoring systems Nonlinear myriad filters for cases where monitoring of transient data can lead to better fault detection Innovative nonlinear filters for data cleaning developed using optimization methods An edge detector based on gradient and Laplacian calculations A process of automating fault isolation using a bank of Kalman filters, fuzzy logic systems, neural networks, and genetic fuzzy systems when an engine model is available An example of vibration-based diagnostics for turbine blades to complement the performance-based methods Using simple examples, the book describes new research tools to more effectively isolate faults in gas turbine engines. These algorithms may also be useful for condition and health monitoring in other systems where sharp changes in measurement data indicate the onset of a fault.
This thesis contributes and provides solutions to the problem of fault diagnosis and estimation from three different perspectives which are i) fault diagnosis of nonlinear systems using nonlinear multiple model approach, ii) inversion-based fault estimation in linear systems, and iii) data-driven fault diagnosis and estimation in linear systems. The above contributions have been demonstrated to the gas turbines as one of the most important engineering systems in the power and aerospace industries. The proposed multiple model approach is essentially a hierarchy of nonlinear Kalman filters utilized as detection filters. A nonlinear mathematical model for a gas turbines is developed and verified. The fault vector is defined using the Gas Path Analysis approach. The nonlinear Kalman filters that correspond to the defined single or concurrent fault modes provide the conditional probabilities associated with each fault mode using the Bayes' law. The current fault mode is then determined based on the maximum probability criteria. The performance of both Extended Kalman Filters (EKF) and Unscented Kalman Filters (UKF) are investigated and compared which demonstrates that the UKF outperforms the EKF for this particular application.The problem of fault estimation is increasingly receiving more attention due to its practical importance. Fault estimation is closely related to the problem of linear systems inversion. This thesis includes two contributions for the stable inversion of non-minimum phase systems. First, a novel methodology is proposed for direct estimation of unknown inputs by using only measurements of either minimum or non-minimum phase systems as well as systems with transmission zeros on the unit circle. A dynamic filter is then identified whose poles coincide with the transmission zeros of the system. A feedback is then introduced to stabilize the above filter dynamics as well as provide an unbiased estimation of the unknown input. The methodology is then applied to the problem of fault estimation and has been shown that the proposed inversion filter is unbiased for certain categories of faults. Second, a solution for unbiased reconstruction of general inputs is proposed. It is based on designing an unknown input observer (UIO) that provides unbiased estimation of the minimum phase states of the system. The reconstructed minimum phase states serve then as inputs for reconstruction of the non-minimum phase states. The reconstruction error for non-minimum phase states exponentially decrease as the estimation delay is increased. Therefore, an almost perfect reconstruction can be achieved by selecting the delay to be sufficiently large. The proposed inversion scheme is then applied to the output-tracking control problem. An important practical challenge is the fact that engineers rarely have a detailed and accurate mathematical model of complex engineering systems such as gas turbines. Consequently, one can find a trend towards data-driven approaches in many disciplines, including fault diagnosis. In this thesis, explicit state-space based fault detection, isolation and estimation filters are proposed that are directly identified from only the system input-output (I/O) measurements and through the system Markov parameters. The proposed procedures do not involve a reduction step and do not require identification of the system extended observability matrix or its left null space. Therefore, the performance of the proposed filters is directly connected to and linearly dependent on the errors in the Markov parameters estimation process. The estimation error dynamics is then derived in terms of the Markov parameters identification errors and directly synthesized from the healthy system I/O data. Consequently, the estimation errors have been effectively compensated for. The proposed data-driven scheme requires the persistently exciting condition for healthy input data which is not practical for certain real life applications and in particular to gas turbine engines. To address this issue, a robust methodology for Markov parameters estimation using frequency response data is developed. Finally, the performance of the proposed data-driven approach is comprehensively evaluated for the fault diagnosis and estimation problems in the gas turbine engines.
The performance diagnostics of any engine model is accomplished by estimating a set of internal engine health parameters from available sensor measurements. These sensors which comprises of a variety of gas path measurements e.g. pressures, temperatures, fuel flow and spool speeds provide information regarding the health of the engine. No physical measurement, however, elaborate or precise, or how often repeated, can ever completely eliminate the universal presence of measurement uncertainties. Instrument measurements are often distorted by noise and bias, thereby masking the true condition of the engine leads to incorrect estimation results. Measurement uncertainties encourage the inaccurate fault diagnosis, and in order to improve the reliability of diagnostic results, it is important to statistically analyse the data scattering caused by sensor noise. Leakage analysis is a key factor in determining energy losses from a gas turbine. Once the components assembly fails, air leakage through the opening increases resulting in a performance loss. Therefore, the performance efficiency of the engine cannot be reliably determined, without good estimates and analysis of leakage faults. Specifically, for energy calculations it is the air flow leaking around the components at operating conditions that is required. Consequently the implementation of a leakage fault within a gas turbine engine model is necessary for any diagnostic technique that can expand its diagnostics capabilities for more accurate predictions. The simulating methods should either, precisely measure the size of leaks or measure the air flow along gas path with sufficient accuracy. In this research, the diagnostic tool that used to deals with the statistical analysis of measurement noise and leakage fault diagnostics is a model-based method utilizing non-linear GPA. For the purpose of diagnostic, the simulation code used in this study is TURBOMATCH and the engine model Trent 500. TURBOMATCH is the name of a.
Annotation This is Volume 1 of five volumes that comprise the proceedings of the June 2002 conference, sponsored by the International Gas Turbine Institute (IGTI), a technical institute of the American Society of Mechanical Engineers. The purpose of the conference was to facilitate international exchange and development of educational and technical information related to the design, application, manufacture, operation, maintenance, and environmental impact of all types of gas engines. With an emphasis upon the need for more efficient, cleaner, and more reliable gas turbines, the approximately 130 articles cover various technical aspects of aircraft engines; coal, biomass, and alternative fuels; combustion and fuels; education; electric power; and vehicular and small turbomachines. There is no subject index. Annotation c. Book News, Inc., Portland, OR (booknews.com).