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.

Advanced optimization of gas turbine aero-engine transient performance using linkage-learning genetic algorithm:PartⅡ,optimization in flight mission and controller gains correlation development

PartⅠhas illustrated the procedures to apply the Linkage Learning Genetic Algorithm(LLGA)in Gas Turbine Engine(GTE)controller gains tuning and generated the optimization results for runway conditions from idle to takeoff.However,the total pressure and temperature of the engine inlet vary as the changing of altitude and Mach number,which would lead to the variation in fuel flow supply regulation.As a result,the optimized gains in runway might not be suitable for other flight conditions.In order to maintain the optimal control performance,the GTE controller gains should be adjusted according to the flight conditions.This paper extends the application of the LLGA method to other flight conditions and then simulates a complete flight mission with different gains and weather condition configurations.For this purpose,the control parameters in the Simulink model of the GTE controller are first corrected by the weather condition in altitude.Then,a typical flight mission is defined and divided into different flight segments based on the altitude and Mach number configuration.One representative point is selected from each segment as the datum point for optimization process.After this step,the LLGA method is used to find the best gains combinations for different flight conditions and the differences in optimization effects for different flight conditions are analyzed subsequently.The simulation results show that the optimization effect of the control performance of each flight condition is dependent on the value of(θδ)~(1/2)and the optimal K_(pla)in some flight conditions is approximately equal to p hd times of the Kplavalue in sea level standard condition.Finally,the complete flight mission is simulated with different gains and weather condition configurations.The simulation results show that the engine performance has been greatly improved after optimization by LLGA in the transient state and the high altitude conditions.In other steady states,the optimization effect is not very obvious.

Advanced optimization of gas turbine aero-engine transient performance using linkage-learning genetic algorithm:PartⅠ,building blocks detection and optimization in runway

This paper proposes a Linkage Learning Genetic Algorithm(LLGA)based on the messy Genetic Algorithm(mGA)to optimize the Min-Max fuel controller performance in Gas Turbine Engine(GTE).For this purpose,a GTE fuel controller Simulink model based on the Min-Max selection strategy is firstly built.Then,the objective function that considers both performance indices(response time and fuel consumption)and penalty items(fluctuation,tracking error,overspeed and acceleration/deceleration)is established to quantify the controller performance.Next,the task to optimize the fuel controller is converted to find the optimization gains combination that could minimize the objective function while satisfying constraints and limitations.In order to reduce the optimization time and to avoid trapping in the local optimums,two kinds of building block detection methods including lower fitness value method and bigger fitness value change method are proposed to determine the most important bits which have more contribution on fitness value of the chromosomes.Then the procedures to apply LLGA in controller gains tuning are specified stepwise and the optimization results in runway condition are depicted subsequently.Finally,the comparison is made between the LLGA and the simple GA in GTE controller optimization to confirm the effectiveness of the proposed approach.The results show that the LLGA method can get better solution than simple GA within the same iterations or optimization time.The extension applications of the LLGA method in other flight conditions and the complete flight mission simulation will be carried out in partⅡ.

Utilisation of turboelectric distribution propulsion in commercial aviation:A review on NASA's TeDP concept

Emissions produced by the aviation industry are currently a severe environmental threat;therefore,aviation agencies and governments have set emission targets and formulated plans to restrict emissions within the next decade.Hybrid aircraft technology is being considered to meet these targets.The importance of these technologies lies in their advancements in terms of aircraft life cycles and environmental benignity.Owing to these advancements,hybrid electric systems with more than one power source have become promising for the aviation industry,considering that the growth of air traffic is projected to double in the next decade.Hybrid technologies have given future hybrid fans and motor-fan engines potential as alternative power generators.Herein,Turboelectric Distributed Propulsion(TeDP)is discussed in terms of power distribution and power sources.The fundamentals of turbofan and turboshaft engines are presented along with their electricitygeneration mechanism.TeDP is discussed from a design viewpoint,with a detailed discussion of different types of hybrid electric and turboelectric systems.Examples of proposed TeDP aircraft models and numerical modelling tools used to simulate the performance of TeDP models are reviewed.Finally,innovative turboelectric systems in which electric power savers and mechanical gear changers have been discarded for weight optimisation are presented along with other prospective models,engines,approaches,and architectures.The findings of this review indicate the knowledge gaps in the field of numerical modelling for NASA’s TeDP and its capability to increase the efficiency by up to 24%with a 50%reduction in emissions relative to those of conventional gas turbines.

Reduced-dimensional MPC controller for direct thrust control

With the development of the aircraft gas turbine engine, a control system should be able to achieve effective thrust control to gain better operability. The main contribution of this paper is to develop a novel direct thrust control approach based on an improved model predictive control method through a strategy that reduces the dimension of control sequence. It can not only achieve normal direct thrust control tasks but also maximize the thrust level within the safe operation boundaries. Only the action of switching the objective functions is required to achieve the switch of these two thrust control modes while there is no modification to the control structure. Besides,a shorter control sequence is defined for multivariable control by updating only one control variable at every simulation time instant. Therefore, the time requirement for the solving process of the optimal control sequence is reduced. The proposed controller is implemented to a twin-spool engine.Simulations are conducted in the wide flight envelope, and results show that the average timeconsumption can be reduced up to 65% in comparison with the standard model predictive control,and the thrust can be increased significantly when maximum thrust mode is implemented by using engine limit margins.

Performance assessment of simple and modified cycle turboshaft gas turbines

This paper focuses on investigations encompassing comparative assessment of gasturbine cycle options.More specifically,investigation was caried out of technical performanceof turboshaft engine cycles based on existing simple cycle(SC)and its projected modifiedcycles for civil helicopter application.Technically,thermal efficiency,specific fuel consump-tion,and power output are of paramount importance to the overall performance of gas urbineengines.In course of carrying out this research,turbomatch software established at CranfieldUniversity based on gas turbine theory was applied to conduct simulation of a simple cycle(baseline)two-spool helicopter turboshaft engine model with free power turbine.Similarly,some modified gas urbine cycle configurations incoporating unconventional components,such as engine cycle with low pressure compressor(LPC)zero-staged,recuperated enginecycle,and intercooled/recuperated(ICR)engine cycle,were also simulated.In doing so,designpoint(DP)and off-design point(OD)performances of the engine models were established.Thepercentage changes in performance parameters of the modified cycle engines over the simplecycle were evaluated and it was found that to a large extent,the modified engine cycles withunconventional components exhibit better performances in terms of thermal efficiency andspecific fuel consumption than the traditional simple cycle engine.This research made use ofpublic domain open source references.

A novel model-based multivariable framework for aircraft gas turbine engine limit protection control

Control technologies are innovated to satisfy increasingly complicated control demands of gas turbine engines.In terms of limit protection control,a novel model-based multivariable limit protection control method,which is achieved by adaptive command reconstruction and multiplecontrol loop selection and switch logic,is proposed in this paper to address the problem of balancing smaller thrust loss and safe operations by comparing with widely-used Min-Max logic.Five different combination modes of control loops,which represent the online control loop of last time instant and that of current time instant,is analyzed.Different command reconstructions are designed for these modes,which is based on static gain conversion of amplitude beyond limits by using an onboard model.The double-prediction based control loop selection and switch logic is developed to choose a control loop appropriately by comparing converted amplitude beyond limits regardless of one or more parameters tending to exceed limits.The proposed method is implemented in a twin-spool turbofan engine to achieve limit protection with direct thrust control,and the loss of thrust is improved by about 30% in comparison with the loss of thrust caused by Min-Max logic when limit protection control is activated,which demonstrates the effectiveness of the proposed method.

Performance of small-scale aero-derivative industrial gas turbines derived from helicopter engines

This paper considers comparative assessment of simple and advanced cycle small-scale aero-derivative industrial gas turbines derived from helicopter engines.More particularly,investigation was made of technical performance of the small-scale aero-derivative engine cycles based on existing and projected cycles for applications in industrial power generation,combined heat and power concept,rotating equipment driving,and/or allied processes.The investigation was done by carrying out preliminary design and performance simulation of a simple cycle(baseline)two-spool small-scale aero-derivative turboshaft engine model,and some advanced counterpart aero-derivative configurations.The advanced configurations consist of recuperated and intercooled/recuperated engine cycles of same nominal power rating of 1.567 MW.The baseline model was derived from the conversion of an existing helicopter engine model.In doing so,design point and off-design point performances of the engine models were established.In comparing their performances,it was observed that to a large extent,the advanced engine cycles showed superior performance in terms of thermal efficiency,and specific fuel consumption.In numerical terms,thermal efficiencies of recuperated engine cycle,and intercooled/recuperated engine cycles,over the simple cycle at DP increased by 13.5%,and 14.5%respectively,whereas specific fuel consumption of these cycles over simple cycle at DP decreased by 12.5%,and 13%respectively.This research relied on open access public literature for data.