Comparing different schemes in a combined technique of Kalman filter, artificial neural network and fuzzy logic for gas turbines online diagnostics

The paper presents research on the online performance-based diagnostics by implementing a novel methodology, which is based on the combination of Kalman Filter, Artificial Neural Network, Neuro-Fuzzy Logic and Fuzzy Logic. These methods are proposed to improve the success rate, increase the flexibility, and allow the detection of single and multiple failures. The methodology is applied to a 2-shaft industrial gas turbine engine for the automated early detection of single and multiple failures with the presence of measurement noise. The methodology offers performance prediction and the possibility of utilizing multiple schemes for the online diagnostics. The architecture leads to three possible schemes. The first scheme includes the base methodology and enables Kalman Filter for data filtering, Artificial Neural Network for the component efficiency prediction, the Neuro-Fuzzy logic for the failure quantification and the Fuzzy Logic for the failure classification. For this scheme, a performance simulation tool (Turbomatch) is used to calculate the thermodynamic baseline. The second scheme replaces Turbomatch with the Artificial Neural Network, that is used to calculate the deteriorated efficiencies and the reference efficiencies. The third scheme is identical to the first one but excludes the shaft power measurements, which are not available in aero engines or might not be usable for some power plant configurations. The paper compares the performance of the three methodologies, with the presence of measurement noise (0.4% reference noise and 2.0% reference noise), and 24 types of random single and multiple failures, with variable magnitude. The first methodology has been already presented by Togni et al. [10], whereas the other two methodologies and results are part of the PhD thesis presented by Togni [18] and they extend the applicability of the method. The success rate, targeting the correct detection of the of the failure magnitude ranges between 92% and 100% without measurement noise and ranges between 66% and 83% with measurement noise. Instead, the success rate of the classification, targeting the correct detection of the type of failure ranges between 93% and 100% without measurement noise and between 85% and 100% with measurement noise

Damage Detection Using a Graph-based Adaptive Threshold for Modal Strain Energy and Improved Water Strider Algorithm

Damage detection through an inverse optimization problem has been investigated by many researchers. Recently, Modal Strain Energy (MSE) has been utilized as an index (MSEBI) for damage localization that serves to guide the optimization. This guided approach considerably reduces the computational cost and increases the accuracy of optimization. Although this index mostly exhibits an acceptable performance, it fails to find some damaged elements' locations in some cases. The aim of this paper is twofold. Firstly, a Graph-based Adaptive Threshold (GAT) is proposed to identify some of those elements that are not detected by basic MSEBI. GAT relies on the concepts from graph theory and MSE working as a simple anomaly detection technique. Secondly, an Improved version of the Water Strider Algorithm (IWSA) is introduced, applied to the damage detection problems with incomplete modal data and noise-contaminated inputs. Several optimization algorithms, including the newly-established Water Strider Algorithm (WSA), are utilized to test the proposed method. The investigations on several damage detection problems demonstrate the GAT and IWSA's satisfactory performance compared to the previous methods

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

Improving Aircraft Engine Maintenance Effectiveness And Reliability Using Intelligent Based Health Monitoring

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2009Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2009Minimum bakım maliyeti ile uçakların kullanılabilirliğini artırmak için, Motor durumunu izleme (MDİ) çok rağbet görür hale gelmiştir. Bu çalışma, uçak bakım etkinliği ve güvenilirliğini artırmak için, arızaların olmadan önce saptanmasına imkan sağlayacak, uçuş sırasında MDİ için bir metod geliştirmeyi amaçlamaktadır. Yaklaşan motor arızaları, yakıt akışı (FF), egzoz gaz sıcaklığı (EGT), motor fan devri (N1), motor kompressör devri (N2) vs. parametrelerinin değişmesine sebep olduğundan, motor kötüleşmeleri veya bozulmaları, bunların izlenmesi ile tespit edilebilir. Bu çalışmada, motor durumunu uçuşta izlemek için, bulanık mantık ve sinir ağları kullanılarak, hava yolları tarafından yapılan mevcut manüel MDİ’nin otomasyonu geliştirilmiştir. Daha sonra, MDİ otomasyonu için, çok kullanışlı bir metod olan bulanık mantık seçilmiştir. Farklı motor arızaları için, Türk Hava Yolları’ndaki gerçek veriler ve uzman bilgilerine dayanarak bulanık mantık kural tabanı oluşturulmuştur. MDİ’nin tüm çevrimi MATLAB’teki bulanık mantık modülü ve Visual Basic’te yazılan bir program kullanılarak otomatikleştirilmiştir. Sonuçta, bu metod Türk Hava Yollarındaki motorların izlenmesi için çalıştırılmıştır. Sonuçlar, bu metodun, MDİ’nin kolaylaştırılması ve ekstra adam-saat, insan hatası ve mühendislik uzmanlığı gerekliliği gibi dezavantajları minimuma indirmek için, hava yolları tarafından kullanılabileceği göstermiştir. Bu metot, uçak motorları dışında, uçaklardaki yardımcı güç üniteleri, yapısal elemanlar vb. komponetlere uygulanabilir. Her motor tipi farklı karakterlere sahip olabileceği için, farklı motor tiplerinde bu metot kullanırken kural tabanının revize edilmesi gerekir.Engine Health monitoring (EHM) has been a very popular subject to increase aircraft availability with minimum maintenance cost. The study is aimed at providing a method to monitor the aircraft engine health during the flight with the aim of providing an opportunity for early fault detection to improve airline maintenance effectiveness and reliability. Since the impending engine failures may cause to change the engine parameters such as Fuel Flow (FF), Exhaust Gas Temperature (EGT), engine fan speed (N1), engine compressor speed (N2), etc., engine deteriorations or faults may be identified before they occur by monitoring them. So as to monitor engine health in flight, the automation of current work for EHM which is done manually by airlines is developed by using fuzzy logic (FL) and neural network (NN) models. FL is selected to develop an Automated EHM system (AEHMS), since it is very useful method for automation health monitoring. The fuzzy rule inference system for different engine faults is based on the expert knowledge and real life data in Turkish Airlines fleet. The complete loop of EHM is automatically performed by visual basic programs and Fuzzy Logic Toolbox in MATLAB. Finally, the method is utilized to run for monitoring the engines in Turkish Airlines fleet. This study has shown that AEHMS can be used by airlines or engine manufacturers efficiently to simplify the EHM system and minimize the drawbacks of it, such as extra labor hour, human error and requirement for engineering expertise. This method may also be applicable other than aircraft engines such as auxiliary power unit, structures. Since every engine type has different characters, it is required to revise the fuzzy rules for the concerning engine types.DoktoraPh

Development, implementation and testing of an expert system for detection of defects in gas turbine engines

Unbalance and misalignment are the major causes of vibration in rotating machinery, yet only limited research has been conducted on misalignment. The literature reports that misalignment results in an increase in the vibration at a frequency corresponding to two times the rotating speed (2x responses). The research on misalignment conducted so far has modeled the rotor as two coupled shafts supported on linear and non linear bearings, while misalignment is at the coupler. The results reported to date are inconsistent and the vibration response of a misaligned rotor system is not clearly understood. This dissertation presents a study on the effects of a single shaft misalignment on the dynamic response of a rotor-shaft system. A rotor system supported on two rigid bearings with unbalance and misalignment is modeled using the energy method, and Lagrange formulation is used to establish the equations of motion. The misalignment is modeled through introduction of pre-load and nonlinear shaft stiffness in the direction of pre-load. The model is validated by comparing the natural frequencies predicted using the simulation to the rotor system eigenvalue and the forced response from the simulation is verified using finite element method. A response due to perfectly aligned case is compared with those for parallel and angular misalignments of various magnitudes. Simulations are carried out for a speed range of 0 to 10,000 rpm, and the response of the rotor at the 2x is carefully examined to establish the effects of various misalignment and non-linear parameters on the response. Experiments are conducted using a rig test to compare with analytically predicted trends. Various gas turbine engine data gathered from the field are also used to confirm the vibration pattern predicted by the simulations. The simulated results are finally used to develop an expert system that can identify unbalance and misalignment in a rotor system. The expert system is developed using Neural Network. Two types of Neural Networks are explored, the back-propagation and the Logicon Projection Network. Finally, both networks are modified, trained and tested using simulation data. The Logicon projection network showed superior performance during training, and was chosen over the back-propagation network. The developed expert system is tested using field test data of gas turbine engines to demonstrate its effectiveness

An integrated combined methodology for the outline gas turbines performance-based diagnostics and signal failure isolation.

The target of this research is the performance-based diagnostics of a gas turbine for the online automated early detection of components malfunctions with the presence of measurements malfunctions. The research 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 rate of success for single and multiple failures with the presence of measurement malfunctions – measurement noise. A combination of Kalman Filter, Artificial Neural Network, Neuro-Fuzzy Logic and 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 failure treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile, the neuro-fuzzy logic the estimation precision, used for the quantification and the fuzzy logic the categorization flexibility, which are used to classify the components failure. All contributors are also a valid technique for online diagnostics, which is a key objective of the methodology. In the area of 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 research investigates the key contribution of each component of the methodology and reaches a success rate for the component health estimation above 92.0% and a success rate for the failure type classification above 95.1%. The results are obtained with the first configuration, running with the reference random simulation of 203 points with different level of deterioration magnitude and different combinations of failures type. If a measurement noise 5 times higher than the nominal is considered, the component health estimation drop to a minimum of 70.1% (reference scheme 1) while the classification success rate remains above 88.9% (reference scheme 1). Moreover, the speed of the data processing – minimum 0.23 s / maximum 1.7 s per every single sample – proves the suitability of this methodology for online diagnostics. The methodology is extensively tested against components failure and measurement issues. The tests are repeated with constant simulations, random simulation and a deterioration schedule that is reproducing several months of engine operations.PhD in Aerospac

Diagnóstico Termoeconômico de uma Central de Cogeração do Setor Siderúrgico Utilizando o Modelo H&s

O Diagnóstico é a arte de descobrir e entender sinais de Malfunções e quantificar seus efeitos. No caso da Termoeconomia, o efeito da Malfunção é quantificado em termos de recursos adicionais consumidos para obter a mesma produção, ambos em qualidade e quantidade (Uche, 2000). A realização de um Diagnóstico Termoeconômico de uma Central de Cogeração do Setor Siderúrgico utilizando o modelo H&S é o foco deste trabalho. Trata-se de uma central de cogeração operando em ciclo rankine, com três aquecedores regenerativos de alimentação fechados e um aberto, cuja função é atender as demandas de energia mecânica (para produção de ar soprado), energia elétrica e energia térmica (vapor de processo) da usina. O Diagnóstico Termoeconômico foi realizado através do Modelo Termoeconômico H&S com o intuito de determinar a Malfunções do ciclo, verificando assim as anomalias presentes nos componentes do sistema e a contribuição de cada equipamento no consumo adicional de insumos. Os resultados obtidos foram analisados considerando aspectos gerais da Termodinâmica, Termoeconomia e da Cogeração que permitiram o cálculo de eficiências, destruição de exergia nos equipamentos e indicadores de desempenho da planta. O modelo Termoeconômico utilizado ainda não tinha sido aplicado em Diagnósticos Termoecômicos em plantas reais e de maior complexidade, e portanto este trabalho apresenta metodologia e análise da aplicação deste modelo no Diagnóstico Termoeconômico. Palavras chave: Diagnóstico Termoeconômico, Modelo H&S, Termoeconomia, Cogeração, Setor Siderúrgico

Gas turbine diagnostics using a soft computing approach

A fuzzy system is developed using a linearized performance model of the gas turbine engine for performing gas turbine fault isolation from noisy measurements. By using a priori information about measurement uncertainties and through design variable linking, the design of the fuzzy system is posed as an optimization problem with low number of design variables which can be solved using the genetic algorithm in considerably low amount of computer time. The faults modeled are module faults in five modules: fan, low pressure compressor, high pressure compressor, high pressure turbine and low pressure turbine. The measurements used are deviations in exhaust gas temperature, low rotor speed, high rotor speed and fuel flow from a base line 'good engine'. The genetic fuzzy system (GFS) allows rapid development of the rule base if the fault signatures and measurement uncertainties change which happens for different engines and airlines. In addition, the genetic fuzzy system reduces the human effort needed in the trial and error process used to design the fuzzy system and makes the development of such a system easier and faster. A radial basis function neural network (RBFNN) is also used to preprocess the measurements before fault isolation. The RBFNN shows significant noise reduction and when combined with the GFS leads to a diagnostic system that is highly robust to the presence of noise in data. Showing the advantage of using a soft computing approach for gas turbine diagnostics