Fault Detection and Isolation In Gas Turbine Engines

Aircraft engines are complex systems that require high reliability and adequate monitoring to ensure flight safety and performance. Moreover, timely maintenance has necessitated the need for intelligent capabilities and functionalities for detection and diagnosis of anomalies and faults. In this thesis, fault diagnosis in aircraft jet engines is investigated by using intelligent-based methodologies. Two different artificial neural network schemes are introduced for this purpose. The first fault detection and isolation (FDI) scheme for an aircraft jet engine is based on the multiple model approach and utilizes dynamic neural networks (DNN). Towards this end, multiple DNNs are constructed to learn the nonlinear dynamics of the aircraft jet engine. Each DNN represents a specific operating mode of the healthy or the faulty conditions of the jet engine. The inherent challenges in fault diagnosis systems is that their performance could be excessively reduced under sensor fault and sensor degradation conditions (such as drift and noise). This thesis proposes the use of data validation and sensor fault detection to improve the performance of the overall fault diagnosis system. In this regard the concept of nonlinear principle components analysis (NPCA) is exploited by using autoassociative neural networks. The second FDI scheme is developed by using autoassociative neural networks (ANN). A parallel bank of ANNs are proposed to diagnose sensor faults as well as component faults in the aircraft jet engine. Unlike most FDI techniques, the proposed solution simultaneously accomplishes sensor faults and component faults detection and isolation (FDI) within a unified diagnostic framework. In both proposed FDI approaches, by using the residuals that are generated from the difference between each network output and the measured jet engine output as well as selection of a proper threshold for each network, criteria are established for performing the fault diagnosis of the jet engines. The fault diagnosis tasks consists of determining the time as well as the location of a fault occurrence subject to the presence of disturbances and measurement noise. Simulation results presented, demonstrate and illustrate the effective performance of our proposed neural network-based FDI strategies

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

Fault Diagnosis of Gas Turbine Engines by Using Multiple Model Approach

The field of fault detection and isolation (FDI) has attracted much attention in control theory during the last three decades which has resulted in development of sophisticated FDI algorithms. However, increasing the complexity of FDI algorithms is not necessarily feasible. Particularly for on-line FDI, the FDI unit must have the minimum possible computation cost to prevent any long delays in fault detection. In this research, we try to address the FDI problem of a single spool jet engine by using a modified linear multiple model (MM). We first develop a novel symbolic computation-based method for linearization purposes such that the obtained linear models are subjected to the symbolic fault variables. By substituting certain values for these symbolic variables, one can obtain different linear models, which describe mathematically the healthy and faulty models. In order to select the operating point, we use this fact that for a given constant fuel flow (W_f), the system reaches a steady state, that is varying for different values of W_f. Therefore, the operating points for linearization can be determined by the level of the Power Level Angel (PLA) (different values of W_f). These operating points are selected such that an observer, which is designed as a candidate for the healthy mode, can accurately estimates the states of the system in healthy scenario and the number of false alarm then would be kept to minimum. If the system works at different operating points one can then discretize the W_f into different intervals such that in each interval a linear model represents the behavior of the original system. By using the obtained models for different operating points, one designs the corresponding FDI units. Second, we provide a modified multiple model (MM) approach to investigate the FDI problem of a single spool jet engine. The main advantage of this method lies in the fact that the proposed MM consists of a certain set of linear Kalman filter banks rather than using nonlinear Kalman filters such as the Extended Kalman Filter which requires more computational cost. Moreover, a hierarchical structural multiple model is used to detect and isolate multiple faults. The simulation results show the capability of the proposed method when it is applied to a single spool jet engine model

Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine

AIRCRAFT JET ENGINE CONDITION MONITORING THROUGH SYSTEM IDENTIFICATION BY USING GENETIC PROGRAMMING

In this thesis a new approach for aircraft jet engine condition monitoring is proposed based on system identification and by using Genetic Programming (GP). This approach consists of two fault detection and isolation parts. In the detection part, the relationship between the engine Exhaust Gas Temperature (EGT), as a major indicator of the engine health condition, and other engine parameters and operating conditions corresponding to different phases of the flight is modelled using the GP technique. Towards this end, flight characteristics are divided into several phases such as the take-off and the cruise. The GP scheme is then used to discover the structure of the interrelations among engine variables. The constructed model is then used to detect abrupt faults in the engine performance. For the isolation purpose, a hierarchical approach is proposed which narrows down the number of possible faults toward the target fault. The GP algorithm is then exploited to extract a series of nonlinear functions of the engine variables called fault indices. These indices attempt to magnify the signature of a fault in the engine by combining the effects of a fault on the engine parameters. These indices subsequently provide the necessary residuals for classifying the faults. The approaches developed in this thesis provide an effective strategy for inspecting the aircraft jet engine health condition without requiring any specific information on the engine internal characteristics. The main advantage of the proposed approaches over other data driven methods such as neural networks is that our approaches provide a simple and tangible mathematical model of the engine rather than a black box model. The performance of the proposed algorithms are demonstrated and illustrated by implementing them on a double spool jet engine data that is generated by using the Gas turbine Simulation Program (GSP) software

Gas turbine aero-engines real time on-board modelling: A review, research challenges, and exploring the future

On-board real time modelling for gas turbine aero-engines has been extensively used for engine performance improvement and reliability. This has been achieved by the utilization of on-board model for the engine's control and health management. This paper offers a historical review of on-board modelling applied on gas turbine engines and it also establishes its limitations, and consequently the challenges, which should be addressed to apply the on-board real time model to new and the next generation gas turbine aero-engines. For both applications, i.e. engine control and health management, claims and limitations are analysed via numerical simulation and publicly available data. Regarding the former, the methods for modelling clean and degraded engines are comprehensively covered. For the latter, the techniques for the component performance tracking and sensor/actuator diagnosis are critically reviewed. As an outcome of this systematic examination, two remaining research challenges have been identified: firstly, the requirement of a high-fidelity on-board modelling over the engine life cycle, especially for safety-critical control parameters during rapid transients; secondly, the dependability and reliability of on-board model, which is critical for the engine protection in case of on-board model failure. Multiple model-based on-board modelling and runtime assurance are proposed as potential solutions for the identified challenges and their potential and effectiveness are discussed in detail

Degradation Prognostics in Gas Turbine Engines Using Neural Networks

In complex systems such as aircraft engines, system reliability and adequate monitoring is of high priority. The performance of all physical systems degrades over time due to aging, the working and environmental conditions. Considering both time and safety, it is important to predict the system health condition in future in order to be able to assign a suitable maintenance policy. Towards this end, two artificial intelligence based methodologies are proposed and investigated in this thesis. The main objective is to predict degradation trends by studying their effects on the engine measurable parameters such as the temperature and pressure at critical points of a gas turbine engine. The first proposed prognostic scheme for the gas turbine engine is based on a recurrent neural networks (RNN) architecture. This closed-loop architecture enables the network to learn the increasing degradation dynamics using the collected data set. Training the neural networks and determining the suitable number of network parameters are challenging tasks. The other challenge associated with the prognostic problem is the uncertainty management. This is inherent in such schemes due to measurement noise and the fact that one is trying to project forward in time. To overcome this problem, upper and lower prediction bounds are defined and obtained in this thesis. The two bounds constitute a prediction band which helps one not to merely depend on the single point prediction. The prediction bands along with the prediction error statistical measures, allow one to decide on the goodness of the prediction results. The second prognostic scheme is based on a nonlinear autoregressive with exogenous input (NARX) neural networks architecture. This recurrent dynamical structure takes advantage of both features which makes it easy to manage the main objective. The network is trained with fewer examples and the prediction errors are lower as compared to the first architecture. The statistical error measures and the prediction bands are obtained for this architecture as well. In order to evaluate and compare the prediction results from the two proposed neural networks a metric known as the normalized Akaike information criterion (NAIC) is applied in this thesis. This metric takes into account the prediction error, the number of parameters used in the neural networks architecture and the number of samples in the test data set. A smaller NAIC value shows a better, more accurate and more effective prediction result. The NAIC values are found for each case and the networks are compared at the end of the thesis. Neural networks performance is based on the suitability of the data they are provided with. Two main causes of engine degradation are modelled in this thesis and a SIMULINK model is developed. Various scenarios and case studies are presented to illustrate and demonstrate the effectiveness of our proposed neural networks based prognostic approaches. The prognostic results can be employed for the engine health management purposes. This is a growing and an active area of research for the aircraft engines where only a few references exist in the literature

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

Robust sensor fault detection and isolation of gas turbine engines

An effective fault detection and isolation (FDI) technology can play a crucial role in improving the system availability, safety and reliability as well as reducing the risks of catastrophic failures. In this thesis, the robust sensor FDI problem of gas turbine engines is investigated and different novel techniques are developed to address the effects of parameter uncertainties, disturbances as well as process and measurement noise on the performance of FDI strategies. The efficiencies of proposed techniques are investigated through extensive simulation studies for the single spool gas turbine engine that is previously developed and validated using the GSP software. The gas turbine engine health degradation is considered in various forms in this thesis. First, it is considered as a part of the engine dynamics that is estimated off-line and updated periodically for the on-board engine model. Second, it is modeled as the time-varying norm-bounded parameter uncertainty that affects all the system state-space matrices and third as an unknown nonlinear dynamic that is approximated by the use of a dynamic recurrent neural network. In the first part of the thesis, we propose a hybrid Kalman filter (HKF) scheme that consists of a single nonlinear on-board engine model (OBEM) augmented with piecewise linear (PWL) models constituting as the multiple model (MM) based estimators to cover the entire engine operating regime. We have integrated the generalized likelihood ratio (GLR)-based method with our MM-based scheme to estimate the sensor fault severity under various single and concurrent fault scenarios. In order to ensure the reliability of our proposed HKF-based FDI scheme during the engine life cycle, it is assumed that the reference baselines are periodically updated for the OBEM health parameters. In the second part of the thesis, a novel robust sensor FDI strategy using the MM-based approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple PWL models. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (ARE) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The main objective is to propose a robust filter that satisfies the overall performance requirements and is not affected by system perturbations. The requirements include a quadratically stable filter that ensures bounded estimation error variances having predefined values. In the third part of the thesis, a novel hybrid approach is proposed to improve the robustness of FDI scheme with respect to different sources of uncertainties. For this purpose, a dynamic recurrent neural network (DRNN) is designed to approximate the gas turbine engine uncertainty due to the health degradations. The proposed DRNN is trained offline by using the extended Kalman filter (EKF) algorithm for an engine with different levels of uncertainty, but with healthy sensors. The convergence of EKF-based DRNN training algorithm is also investigated. Then, the trained DRNN with the fixed parameters and topology is integrated with our online model-based FDI algorithm to approximate the uncertainty terms of the real engine. In this part, the previously proposed HKF and RKF are integrated with the trained DRNN to construct the hybrid FDI structure