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

A Fatigue Life Assessment Methodology for Rolling-Element Bearing Under Irregular Loading

The paper presents a methodology for estimating the fatigue life of rolling-element bearing under irregular loading conditions. This method overcomes the limitations encountered by rolling-element bearing lifing models based on a constant bearing load assumption, when used in applications where bearing load varies over time with also changes in rotational speed. To include these irregular loading effects, a load-slice averaging methodology is applied to the loading history; in which the loading history is assumed to be composed of many thin slices of loading conditions. The operating conditions within each loading slice are averaged, and with the aid of linear damage rule and Lundberg-Palmgren load-life correlation for rolling-element bearings, each loading slice fatigue damage contribution is determined. The cumulative loading slice fatigue damage is used to estimate rolling-element bearing life. This approach can also be used as a tool for real-time life prognosis of rolling-element bearings. The method is demonstrated with simulated loading histories acting on a Cooper split cylindrical roller bearing and life prediction comparison is made between several approximate closed form bearing life expressions for different types of loading.Peer reviewedFinal Published versio

Diagnostics of gas turbine systems using gas path analysis and rotordynamic response approach

The modern gas turbine is plagued with issues centred on improving engine availability and limiting component degradation. The integrated use of different condition monitoring techniques presents a solution to addressing these challenges. This paper lays a foundation for the integration of gas path analysis and the rotordynamic response of the compressor to monitor the effect of fouling in the compressor. In investigating the resultant interaction between the aerodynamic and rotordynamic domain in a compressor caused by fouling, an approach involving the interaction of four different models is explored. The first model, a gas turbine engine performance model is used to simulate a fouled compressor and quantify the extent of performance deterioration with gas path analysis. The extent of performance deterioration from the engine performance model represented by scaling of the compressor maps becomes an input in the second model, a Moore-Greitzer compression system model, which evaluates the disturbed flow field parameters in the fouled compressor. The third model, a momentum-based aerodynamic force model, predicts the fouling induced aerodynamic force based on the disturbed flow field parameters. The aerodynamic force acting as a forcing function in the fourth model, a compressor rotordynamic model, produces the vibration response. From the investigation carried out in this work, it is observed, as the rate of fouling increases in the compressor, typified by a decrease in compressor massflow, pressure ratio and isentropic efficiency, there is a corresponding increase in the vibration amplitude at the first fundamental frequency of the compressor

Life cycle evaluation of an intercooled gas turbine plant used in conjunction with renewable energy

The life cycle estimation of power plants is important for gas turbine operators. With the introduction of wind energy into the grid, gas turbine operators now operate their plants in Load–Following modes as back-ups to the renewable energy sources which include wind, solar, etc. The motive behind this study is to look at how much life is consumed when an intercooled power plant with 100 MW power output is used in conjunction with wind energy. This operation causes fluctuations because the wind energy is unpredictable and overtime causes adverse effects on the life of the plant – The High Pressure Turbine Blades. Such fluctuations give rise to low cycle fatigue and creep failure of the blades depending on the operating regime used. A performance based model that is capable of estimating the life consumed of an intercooled power plant has been developed. The model has the capability of estimating the life consumed based on seasonal power demands and operations. An in-depth comparison was undertaken on the life consumed during the seasons of operation and arrives at the conclusion that during summer, the creep and low cycle life is consumed higher than the rest periods. A comparison was also made to determine the life consumed between Load–Following and stop/start operating scenarios. It was also observed that daily creep life consumption in summer was higher than the winter period in-spite of having lower average daily operating hours in a Start–Stop operating scenario

Examination of material variation on the life of gas turbine backing-up renewable energy sources

Gas turbine life and efficiency depend on the operating environment and material performance. Material selection is of prime importance to achieve high life and efficiency. This paper focuses on the study of the effect of material properties and variation in alloy composition ofa high-pressure turbine blade on gas turbine life when works in the flexible mode as a pick-up of renewable sources.A tool has been developed wherein different scenarios can be simulated to obtain engine life consumption factors. The engine life is examined according to the different material for different operating scenarios. It is observed that blade life is highly affected by changing material properties and moreover it is noted that the small change in the mass percentage of some constituent elements of an alloy results in a significant difference in HPT blade life

Comparison of lifing results of gas turbine operated in base load and as a back up to wind turbine

When operating the gas turbine in a flexible mode as a back up to renewable energy sources such as wind, solar, tidal and so on. A fluctuation of power produced by the GT will be apparent which in turn will cause low cycle fatigue in the high-pressure turbine blades. The drive behind this study is to estimate the life of a 100 MW GT operated in a baseload scenario and compare the lifing results with two different scenarios of operating the GT as a back up to a wind turbine operated in the UK in 2016. For the estimation of the GT lifing, some performance parameters are essential such as turbine entry temperature (TET), blade cooling temperature (Tc), and the shaft rotational speed (PCN). All these parameters are obtained from running the in-house TURBOMATCH model, which was developed in Cranfield University, under certain operating conditions (temperature and pressure). These values are used with other parameters as input to a FORTRAN code to estimate the lifing and lifing consumption of the GT. In comparison, it was found that the base load scenario has the highest value of creep while in the backup scenarios the LCF was higher due to the power fluctuation

Influence of fouling on compressor dynamics: Experimental and modelling approach

The effect of compressor fouling on the performance of a gas turbine has been the subject of several papers; however, the goal of this paper is to address a more fundamental question of the effect of fouling, which is the onset of unstable operation of the compressor. Compressor fouling experiments have been carried out on a test rig refitted with TJ100 small jet engine with centrifugal compressor. Fouling on the compressor blade was simulated with texturized paint with average roughness value of 6 microns. Compressor characteristic was measured for both the clean (baseline) and fouled compressor blades at several rotational speeds by throttling the engine with variable exhaust nozzle. A Greitzer-type compression system model has been applied based on the geometric and performance parameters of the TJ100 small jet engine test rig. Frequency of plenum pressure fluctuation, the mean disturbance flow coefficient and pressure-rise coefficient at the onset of plenum flowfield disturbance predicted by the model was compared with the measurement for both the baseline and fouled engine. Model prediction of the flowfield parameters at inception of unstable operation in the compressor showed good agreement with the experimental data. The results proved that used simple Greitzer model is suitable for prediction of the engine compressor unstable behaviour and prediction of the mild surge inception point for both the clean and the fouled compressor

Integrated gas turbine system diagnostics: components and sensor faults quantification using artificial neural networks

The role of diagnostic systems in gas turbine operations has changed over the past years from a single support troubleshooting maintenance to a more proactive integrated diagnostic system. This has become so, because detecting and fixing fault(s) on one gas turbine sub-system can trigger false fault(s) indication, on other component(s) of the gas turbine system, due to interrelationships between data obtained to monitor not only the GT single component, but also the integrated components and sensors. Hence, there is need for integration of gas turbine system diagnostics. The purpose of this paper is to present artificial neural network diagnostic system (ANNDS) as an integrated gas turbine system diagnostic tool capable of quantifying gas turbine component and sensor fault. A model based approach which consists of an engine model, and an associated parameter estimation algorithm that predicts the difference between the real engine data and the estimated output data is described in this paper. The ANNDS system was trained to detect, isolate and assess component(s) and sensor fault(s) of a single spool industrial gas turbine GT-PG9171ER. The ANN model was construed with multi-layer feed-forward back propagation network for component fault(s) and auto associative network for sensor fault(s). The diagnostic methodology adopted was a nested network structure, trained to handle specific objective function of detecting, isolating or quantifying faults. The data used for training, and testing purposes were obtained from a non-linear aero-thermodynamic model using PYTHIA; a Cranfield University in-house software. The data set analyzed in this paper represent samples of clean and faulty gas turbine components caused by fouling (0.5% - 6% degradation) and sensor fault(s) due to bias (±1% - ±7%). The results show the capability of ANN to detect, isolate (classification) and quantify multiple faults if properly trained

A combined technique of Kalman filter, artificial neural network and fuzzy logic for gas turbines and signal fault isolation

The target of this paper is the performance-based diagnostics of a gas turbine for the automated early detection of components malfunctions. The paper proposes a new combination of multiple methodologies for the performance-based diagnostics of single and multiple failures on a two-spool engine. The aim of this technique is to combine the strength of each methodology and provide a high success rate for single and multiple failures with the presence of measurement malfunctions. A combination of KF (Kalman Filter), ANN (Artificial Neural Network) and FL (Fuzzy Logic) is used in this research in order to improve the success rate, to increase the flexibility and the number of failures detected and to combine the strength of multiple methods to have a more robust solution. The Kalman filter has in his strength the measurement noise treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile and the fuzzy logic the categorization flexibility, which is used to quantify and classify the failures. In the area of GT (Gas Turbine) diagnostics, the multiple failures in combination with measurement issues and the utilization of multiple methods for a 2-spool industrial gas turbine engine has not been investigated extensively. This paper reports the key contribution of each component of the methodology and brief the results in the quantification and classification success rate. The methodology is tested for constant deterioration and increasing noise and for random deterioration. For the random deterioration and nominal noise of 0.4%, in particular, the quantification success rate is above 92.0%, while the classification success rate is above 95.1%. Moreover, the speed of the data processing (1.7 s/sample) proves the suitability of this methodology for online diagnostics