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As a consequence of globalization and advances in digital tools, synchronous or asynchronous distance courses are becoming an integral part of universities’ educational offers. The design of an online course introduces more challenges compared to a traditional on campus course with face to face lectures. This is true especially for engineering subjects where problem or project-based courses may be preferred to stimulate critical thinking and engage the learners with real-life problems. However, realizing this with distance learning implies that a similar study pace should be kept by the learners involved. This may not be easy, since individual pace is often a motivation for choosing a distance course. Student engagement in group projects, collaborations, and the proper design of examination tasks are only some of the challenges in designing a distance course for an engineering program. 

A series of web-based courses on measurement techniques, control, and diagnostics were developed and delivered to groups of learners. Each course comprised short modules covering key points of the subject and aimed at getting learners to understand both the fundamental concepts that they do not typically learn or understand in the respective base courses and to build on that knowledge to reach a more advanced cognitive level. 

The experience obtained in the courses on what strategies worked better or worse for the learners is presented in this paper. A comparison between the courses provides an interesting outlook on how the learners reacted to slightly different requirements and incentives in each course. The results from the evaluation of the courses are also used as a base for discussion.

The background and availability of the learners is closely linked to how a course should be designed to optimally fit the learning group, without compromising on the achievement of the learning outcomes. This series of courses is a good example of continuous professional development courses in the field of control, diagnostics, and instrumentation (CDI), and brings with it a number of challenges and opportunities for the development of online courses. 

Fouling on gas turbine blades is detrimental to process operation as it may, over a period of time, reduce the blade efficiency and consequently the turbine’s efficiency. With the limitation of today’s technology, experimental study or real-life observation of fouling in a gas turbine is beyond imagination of maintenance engineers. Hence, the effect of fouling cannot be fully quantified for the engineers to come out with mitigation or intervention plans. Nevertheless, computational fluid dynamics (CFD) may provide a good simulation to understand the phenomena. In this chapter, recent effort involving CFD study on the influence of fouling on gas turbine performance is presented. Firstly, the nature of fouling on the gas turbine and the general consequences are discussed. This is followed by an elaboration on how CFD study has been conducted by the authors. Finally, the findings from the study are discussed.

Gas turbine performance is very responsive to ambient and operational conditions. If the engine is not operating atits optimum conditions, there will be high energy consumption and environmental pollution. Hence, a precise simulationmodel of a gas turbine is needed for performance evaluation and fault detection and diagnostics. This paper presents a twinshaft industrial gas turbine modeling and validation. To develop the simulation model component maps are important,however they are property of the manufacturers and classified documents. In this case, known the compressor pressureratio, speed, and flow rate, the missing design parameters, namely turbines inlet temperatures and pressure ratios werepredicted using GasTurb simulation software. Once the design parameters are developed, the nearest compressor andturbine maps were selected from GasTurb map collection. Beta lines were introduced on each map so that the exactcorresponding value can be picked for a given two parameters of a given map. After the completion of components model,a simulation model was developed in Matlab environment. The equations governing the operation of individual componentwere solved using iteration method. The simulation model has modular nature; it can be modified easily when a change isrequired. The parameters that the model can predict include terminal temperature and pressure, flow rate, specific fuelconsumption, thermal efficiency and heat ratio. To demonstrate the validity of the developed model, the performance ofGE LM2500 twin shaft gas turbine operating in a gas oil industry at Resak PETRONAS platform in Malaysia waspredicted and compared with operational data. The results showed that an average of 5, 3.8 and 3.7 % discrepancies forcompressor discharge temperature and pressure, and fuel flow rate, respectively. This comparison of results showed goodagreement between the measured and predicted parameters. Thus, the developed model can be helpful in performanceevaluation of twin shaft gas turbines and generation of data for training and validation of a fault detection and diagnosticmodel.

Purpose – The purpose of this paper is to present a quantitative fault diagnostic technique for a two-shaft gas turbine engine applications.Design/methodology/approach – Nested artificial neural networks (NANNs) were used to estimate the progressive deterioration of single andmultiple gas-path components in terms of mass flow rate and isentropic efficiency indices. The data required to train and test this method areattained from a thermodynamic model of the engine under steady-state conditions. To evaluate the tolerance of the method against measurementuncertainties, Gaussian noise values were considered.Findings – The test results revealed that this proposed method is capable of quantifying single, double and triple component faults with asufficiently high degree of accuracy. Moreover, the authors confirmed that NANNs have derivable advantages over the single structure-basedmethods available in the public domain, particularly over those designed to perform single and multiple faults together.Practical implications – This method can be used to assess engine’s health status to schedule its maintenance.Originality/value – For complicated gas turbine diagnostic problems, the conventional single artificial neural network (ANN) structure-based faultdiagnostic technique may not be enough to get robust and accurate results. The diagnostic task can rather be better done if it is divided and sharedwith multiple neural network structures. The authors thus used seven decentralized ANN structures to assess seven different component faultscenarios, which enhances the fault identification accuracy significantly.

Studying gas turbine degradation causes and their consequences helps to obtain profound comprehension in howperformance deterioration affects the dependent parameters and to explore relevant information about the nature of thefault signatures for fault diagnostics purpose. In this paper, the effects of compressor fouling, gas generator turbine erosion,and power turbine erosion on the engine dependent parameters were considered separately and together. In this regard,firstly, performance prediction model was developed to LM2500 engine using gas turbine simulation program. It was thenused to simulate the deterioration effects by means of artificially implanted fault case patterns. Comparison of the cleanand deteriorated measurement gives the deviation due to performance degradation. Accordingly, sensitivity order of the gaspath parameters to the corresponding performance deterioration was assessed. This helps to select the key parameters,which are crucial in the process of fault detection and isolation. The results showed that, in most of the cases, air mass flowrate, compressor delivery pressure and temperature, gas generator rotational speed, power turbine inlet pressure, andexhaust gas temperature showed significant deviations. Particularly, the compressor delivery pressure and exhaust gastemperature were the parameters highly influenced by all the fault cases. Moreover, faults that have similar impacts areidentified, in order to show the difficulty of gas turbine health assessment through direct observation to the measurementdeviations.

There are many challenges against an accurate gas turbine fault diagnostics, such as the nonlinearity of the engine health,the measurement uncertainty, and the occurrence of simultaneous faults. The conventional methods have limitations ineffectively handling these challenges. In this paper, a hybrid intelligent technique is devised by integrating an autoassociativeneural network (AANN), nested machine learning (ML) classifiers, and a multilayer perceptron (MLP). The AANNmodule is used as a data preprocessor to reduce measurement noise and extract the important features for visualisation andfault diagnostics. The features are extracted from the bottleneck layer output values based on the concept of the nonlinearprincipal component analysis (NLPCA). The nested classifier modules are then used in such a manner that fault and no-faultconditions, component and sensor faults, and different component faults are distinguished hierarchically. As part of the classification,evaluation of the fault classification performance of five widely used ML techniques aiming to identify alternativeapproaches is undertaken. In the end, the MLP approximator is utilised to estimate the magnitude of the isolated componentfaults in terms of flow capacity and isentropic efficiency indices. The developed system was implemented to diagnose up tothree simultaneous faults in a two-shaft industrial gas turbine engine. Its robustness towards the measurement uncertaintywas also evaluated based on Gaussian noise corrupted data. The test results show the derivable benefits of integrating twoor more methods in engine diagnostics on the basis of offsetting the weakness of the one with the strength of another.

Gas-path diagnostics is an essential part of gas turbine (GT) condition-based maintenance (CBM). There exists extensive literature on GT gas-path diagnostics and a variety of methods have been introduced. The fundamental limitations of the conventional methods such as the inability to deal with the nonlinear engine behavior, measurement uncertainty, simultaneous faults, and the limited number of sensors available remain the driving force for exploring more advanced techniques. This review aims to provide a critical survey of the existing literature produced in the area over the past few decades. In the first section, the issue of GT degradation is addressed, aiming to identify the type of physical faults that degrade a gas turbine performance, which gas-path faults contribute more significantly to the overall performance loss, and which specific components often encounter these faults. A brief overview is then given about the inconsistencies in the literature on gas-path diagnostics followed by a discussion of the various challenges against successful gas-path diagnostics and the major desirable characteristics that an advanced fault diagnostic technique should ideally possess. At this point, the available fault diagnostic methods are thoroughly reviewed, and their strengths and weaknesses summarized. Artificial intelligence (AI) based and hybrid diagnostic methods have received a great deal of attention due to their promising potentials to address the above-mentioned limitations along with providing accurate diagnostic results. Moreover, the available validation techniques that system developers used in the past to evaluate the performance of their proposed diagnostic algorithms are discussed. Finally, concluding remarks and recommendations for further investigations are provided.

In this competitive business world one way to increase profitability of a power production unit is to reduce the operation and maintenance expenses. This is possible if the gas turbine availability and reliability is improved using the appropriate maintenance action at the right time. In that case, fault diagnostics is very critical and effective and advanced methods are essential. Gas turbine diagnostics has been studied for the past six decades and several methods are introduced. This paper aims to review and summarise the published literature on gas path diagnostics, giving more emphasis to the recent developments, and identify advantages and limitations of the methods so that beginners in diagnostics can easily be introduced. Towards this end, this paper, identifies various diagnostic methods and point out their pros and cons. Finally, the paper concludes the review along with some recommended future works.

An effective and reliable gas path diagnostic method that could be used to detect, isolate, and identify gas turbinedegradations is crucial in a gas turbine condition-based maintenance. In this paper, we proposed a new combinedtechnique of artificial neural network and support vector machine for a two-shaft industrial gas turbine engine gaspath diagnostics. To this end, an autoassociative neural network is used as a preprocessor to minimize noise and generatenecessary features, a nested support vector machine to classify gas path faults, and a multilayer perceptron to assess themagnitude of the faults. The necessary data to train and test the method are obtained from a performance model of thecase engine under steady-state operating conditions. The test results indicate that the proposed method can diagnoseboth single- and multiple-component faults successfully and shows a clear advantage over some other methods in termsof multiple fault diagnosis. Moreover, 5-8 sets of measurements have been used to assess the prediction accuracy, andonly a 2.3% difference was observed. This result indicates that the proposed method can be used for multiple faultdiagnosis of gas turbines with limited measurements.

In a gas turbine fault diagnostics, the removal of measurementnoise and data outliers prior to the fault analysis is very essential. Theconventional filtering methods, particularly the linear ones, are notsufficiently accurate, which might possibly lead to the loss of criticallyimportant features in the fault analysis process. Conversely, the recordedaccuracies obtained from the non-linear filters are promising. Recently, thefocus has been shifted to the artificial neural network (ANN) based nonlinearfilters due to their capability of providing a robust identity map between theinput and output data, which can be efficiently exploited in the process offault diagnosis. This paper aims to present combined auto-associative neuralnetwork (AANN) and K-nearest neighbor (KNN) based noise reduction andfault detection method for a gas turbine engine application. The performanceof the developed method has been evaluated using data obtained from amodel simulation. The test results revealed that the developed hybrid methodis more effective and reliable than the conventional methods for the faultdetection of the gas turbine engine with negligible false alarms and misseddetections.

Gas turbines play a vital role in various industries. Timely and accurately predicting their degradation is essential for efficient operation and optimal maintenance planning. Diagnostic and prognostic outcomes aid in determining the optimal compressor washing intervals. Diagnostics detects compressor fouling and estimates the trend up to the current time. If the forecast indicates fast progress in the fouling trend, scheduling offline washing during the next inspection event or earlier may be crucial to address the fouling deposit comprehensively. This approach ensures that compressor cleaning is performed based on its actual health status, leading to improved operation and maintenance costs. This paper presents a novel prognostic method for gas turbine degradation forecasting through a time-series analysis. The proposed approach uses the Temporal Fusion Transformer model capable of capturing time-series relationships at different scales. It combines encoder and decoder layers to capture temporal dependencies and temporal-attention layers to capture long-range dependencies across the encoded degradation trends. Temporal attention is a self-attention mechanism that enables the model to consider the importance of each time step degradation in the context of the entire degradation profile of the given health parameter. Performance data from multiple two-spool turbofan engines is employed to train and test the method. The test results show promising forecasting ability of the proposed method multiple flight cycles into the future. By leveraging the insights provided by the method, maintenance events and activities can be scheduled in a proactive manner. Future work is to extend the method to estimate remaining useful life.

Our objective was to develop a Fuzzy logic (FL) based industrial two-shaft gas turbine gas path diagnostic method based on gas pathmeasurement deviations. Unlike most of the available FL based diagnostic techniques, the proposed method focused on a quantitativeanalysis of both single and multiple component faults. The data required to demonstrate and verify the method was generated from asimulation program, tuned to represent a GE LM2500 engine running at an existing oil & gas plant, taking into account the two mostcommon engine degradation causes, fouling and erosion. Gaussian noise is superimposed into the data to account measurement uncertainty.Finally, the fault isolation and quantification effectiveness of the proposed method was tested for single, double and triple componentfault scenarios. The test results show that the implanted single, double and triple component fault case patterns are isolated with anaverage success rate of 96 %, 92 % and 89 % and quantified with an average accuracy of 83 %, 80 % and 78.5 %, respectively.

The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy.

Advanced engine health monitoring and diagnostic systems greatly benefit users helping them avoid potentially expensive and time-consuming repairs by proactively identifying shifts in engine performance trends and proposing optimal maintenance decisions. Engine health deterioration can manifest itself in terms of rapid and gradual performance deviations. The former is due to a fault event that results in a short-term performance shift and is usually concentrated in a single component. Whereas the latter implies a gradual performance loss that develops slowly and simultaneously in all engine components over their lifetime due to wear and tear. An effective engine lifecycle monitoring and diagnostic system is therefore required to be capable of discriminating these two deterioration mechanisms followed by isolating and identifying the rapid fault accurately. In the proposed solution, this diagnostic problem is addressed through a combination of adaptive gas path analysis and artificial neural networks. The gas path analysis is applied to predict performance trends in the form of isentropic efficiency and flow capacity residuals that provide preliminary information about the deterioration type. Sets of neural network modules are trained to filter out noise in the measurements, discriminate rapid and gradual faults, and identify the nature of the root cause, in an integrated manner with the gas path analysis. The performance of the proposed integrated method has been demonstrated and validated based on performance data obtained from a three-shaft turbofan engine. The improvement achieved by the combined approach over the gas path analysis technique alone would strengthen the relevance and long-term impact of our proposed method in the gas turbine industry. © 2021, Prognostics and Health Management Society. All rights reserved.

Data-driven algorithms require large and comprehensive training samples in order to provide reliable diagnostic solutions. However, in many gas turbine applications, it is hard to find fault data due to proprietary and liability issues. Operational data samples obtained from end-users through collaboration projects do not represent fault conditions sufficiently and are not labeled either. Conversely, model-based methods have some accuracy deficiencies due to measurement uncertainty and model smearing effects when the number of gas path components to be assessed is large. The present paper integrates physics-based and data-driven approaches aiming to overcome this limitation. In the proposed method, an adaptive gas path analysis (AGPA) is used to correct measurement data against the ambient condition variations and normalize. Fault signatures drawn from the AGPA are used to assess the health status of the case engine through a Bayesian network (BN) based fault diagnostic algorithm. The performance of the proposed technique is evaluated based on five different gas path component faults of a three-shaft turbofan engine, namely intermediate-pressure compressor fouling (IPCF), high-pressure compressor fouling (HPCF), high-pressure turbine erosion (HPTE), intermediate-pressure turbine erosion (IPTE), and low-pressure turbine erosion (LPTE). Robustness of the method under measurement uncertainty has also been tested using noise-contaminated data. Moreover, the fault diagnostic effectiveness of the BN algorithm on different number and type of measurements is also examined based on three different sensor groups. The test results verify the effectiveness of the proposed method to diagnose single gas path component faults correctly even under a significant noise level and different instrumentation suites. This enables to accommodate measurement suite inconsistencies between engines of the same type. The proposed method can further be used to support the gas turbine maintenance decision-making process when coupled with overall Engine Health Management (EHM) systems.

The utilization of hydrogen fuel in gas turbines brings significant changes to the thermophysical properties of flue gas, including higher specific heat capacities and an enhanced steam content. Therefore, hydrogen-fueled gas turbines are susceptible to health degradation in the form of steam-induced corrosion and erosion in the hot gas path. In this context, the fault diagnosis of hydrogen-fueled gas turbines becomes indispensable. To the authors' knowledge, there is a scarcity of fault diagnosis studies for retrofitted gas turbines considering hydrogen as a potential fuel. The present study, however, develops an artificial neural network (ANN)-based fault diagnosis model using the MATLAB environment. Prior to the fault detection, isolation, and identification modules, physics-based performance data of a 100 kW micro gas turbine (MGT) were synthesized using the GasTurb tool. An ANN-based classification algorithm showed a 96.2% classification accuracy for the fault detection and isolation. Moreover, the feedforward neural network-based regression algorithm showed quite good training, testing, and validation accuracies in terms of the root mean square error (RMSE). The study revealed that the presence of hydrogen-induced corrosion faults (both as a single corrosion fault or as simultaneous fouling and corrosion) led to false alarms, thereby prompting other incorrect faults during the fault detection and isolation modules. Additionally, the performance of the fault identification module for the hydrogen fuel scenario was found to be marginally lower than that of the natural gas case due to assumption of small magnitudes of faults arising from hydrogen-induced corrosion.

In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in mining operations must have meticulous approaches and uncompromised security. By ensuring a secure transition, the mining industry can navigate the transformative shift towards autonomy while upholding the highest standards of safety and operational reliability. Experiments involving autonomous pathways for mining machinery that utilize AI for route optimization demonstrate a higher speed capacity than manually operated approaches; this translates to enhanced productivity, subsequently fostering increased production capacity to meet the rising demand for metals. Nonetheless, accelerated wear on crucial elements like tires, brakes, and bearings on mining machines has been observed. Autonomous mining processes will require smarter machines without humans that guide and support actions prior to a hazardous situation occurring. This paper will delve into a comprehensive perspective on the safety of autonomous mining machines by using Bayesian networks (BN) to detect possible hazard fires. The BN is tuned with a combination of empirical field data and laboratory data. Various faults have been recognized, and their correlation with the measurements has been established.

Due to the intense price-based global competition, rising operating cost, rapidly changing economic conditions and stringent environmental regulations, modern process and energy industries are confronting unprecedented challenges to maintain profitability. Therefore, improving the product quality and process efficiency while reducing the production cost and plant downtime are matters of utmost importance. These objectives are somewhat counteracting, and to satisfy them, optimal operation and control of the plant components are essential. Use of optimization not only improves the control and monitoring of assets, but also offers better coordination among different assets. Thus, it can lead to extensive savings in the energy and resource consumption, and consequently offer reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks. In this chapter, a generic learning system architecture is presented that can be retrofitted to existing automation platforms of different industrial plants. The architecture offers flexibility and modularity, so that relevant functionalities can be selected for a specific plant on an as-needed basis. Various functionalities such as soft-sensors, outputs prediction, model adaptation, control optimization, anomaly detection, diagnostics and decision supports are discussed in detail.

The study focused on the development of -gas turbine full- and part-load operation diagnostics. The gas turbine performance model was developed using commercial software and validated using the engine manufacturer data. Upon the validation, fouling, erosion, and variable inlet guide vane drift were simulated to generate faulty data for the diagnostics development. Because the data from the model was noise-free, sensor noise was added to each of the diagnostic set parameters to reflect the actual scenario of the field operation. The data was normalized. In total, 13 single, and 61 double, classes, including 1 clean class, were prepared and used as input. The number of observations for single faults diagnostics were 1092, which was 84 for each class, and 20,496 for double faults diagnostics, which was 336 for each class. Twenty-eight machine learning techniques were investigated to select the one which outperformed the others, and further investigations were conducted with it. The diagnostics results show that the neural network group exhibited better diagnostic accuracy at both full- and part-load operations. The test results and its comparison with literature results demonstrated that the proposed method has a satisfactory and reliable accuracy in diagnosing the considered fault scenarios. The results are discussed, following the plots.

This study presents a comprehensive analysis of the impact of variable inlet guide vanes and physical faults on the performance of a three-shaft gas turbine engine operating at full load. By utilizing the input data provided by the engine manufacturer, the performance models for both the design point and off-design scenarios have been developed. To ensure the accuracy of our models, validation was conducted using the manufacturer’s data. Once the models were successfully validated, various degradation conditions, such as variable inlet guide vane drift, fouling, and erosion, were simulated. Three scenarios that cause gas turbine degradation have been considered and simulated: First, how would the variable inlet guide vane drift affect the gas turbine performance? Second, how would the combined effect of fouling and variable inlet guide vane drift cause the degradation of the engine performance? Third, how would the combined effect of erosion and variable inlet guide vane drift cause the degradation of the engine performance? The results revealed that up-VIGV drift, which is combined fouling and erosion, shows a small deviation because of offsetting the isentropic efficiency drop caused by fouling and erosion. It is clearly observed that fouling affects more upstream components, whereas erosion affects more downstream components. Furthermore, the deviation of performance and output parameters due to the combined faults has been discussed.

The gas turbine was one of the most important technological developments of the early 20th century, and it has had a significant impact on our lives. Although some researchers have worked on predicting the performance of three-shaft gas turbines, the effects of the deteriorated components on other primary components and of the physical faults on the component measurement parameters when considering the variable inlet guide valve scheduling and secondary air system for three-shaft gas turbine engines have remained unexplored. In this paper, design point and off-design performance models for a three-shaft gas turbine were developed and validated using the GasTurb 13 commercial software. Since the input data were limited, some engineering judgment and optimization processes were applied. Later, the developed models were validated using the engine manufacturer's data. Right after the validation, using the component health parameters, the physical faults were implanted into the non-linear steady-state model to investigate the performance of the gas turbine during deterioration conditions. The effects of common faults, namely fouling and erosion in primary components of the case study engine, were simulated during full-load operation. The fault simulation results demonstrated that as the severity of the fault increases, the component performance parameters and measurement parameters deviated linearly from the clean state. Furthermore, the sensitivity of the measurement parameters to the fault location and type were discussed, and as a result they can be used to determine the location and kind of fault during the development of a diagnosis model.

The power demand from gas turbines in electrical grids is becoming more dynamic due to the rising demand for power generation from renewable energy sources. Therefore, including the transient data in the fault diagnostic process is important when the steady-state data are limited and if some component faults are more observable in the transient condition than in the steady-state condition. This study analyses the transient behaviour of a three-shaft industrial gas turbine engine in clean and degraded conditions with consideration of the secondary air system and variable inlet guide vane effects. Different gas path faults are simulated to demonstrate how magnified the transient measurement deviations are compared with the steady-state measurement deviations. The results show that some of the key measurement deviations are considerably higher in the transient mode than in the steady state. This confirms the importance of considering transient measurements for early fault detection and more accurate diagnostic solutions.

A gas path analysis approach of dynamic modelling was used to examine the gas turbine performance. This study presents an investigation of the effect of physical faults on the performance of a three-shaft gas turbine at full-load and part-load operation. A nonlinear steady state performance model was developed and validated. The datasheet from the engine manufacturer was used to gather the input and validation data. Some engineering judgement and optimization were used. Following validation of the engine performance model with the engine manufacturer data using physical fault and component health parameter relationships, physical faults were implanted into the performance model to evaluate the performance characteristics of the gas turbine at degradation state at full- and part-load operation. The impact of erosion and fouling on the gas turbine output parameters, component measurement parameters, and the impact of degraded components on another primary component of the engine have been investigated. The simulation results show that the deviation in the output parameters and component isentropic efficiency due to compressor fouling and erosion is linear with the load variation, but it is almost nonlinear for the downstream components. The results are discussed following the plots.

The reliability and cost-effectiveness of energy conversion in gas turbine systems are strongly dependent on an accurate diagnosis of possible process and sensor anomalies. Because data collected from a gas turbine system for diagnosis are inherently uncertain due to measurement noise and errors, probabilistic methods offer a promising tool for this problem. In particular, dynamic Bayesian networks present numerous advantages. In this work, two Bayesian networks were developed for compressor fouling and turbine erosion diagnostics. Different prior probability distributions were compared to determine the benefits of a dynamic, first-order hierarchical Markov model over a static prior probability and one dependent only on time. The influence of data uncertainty and scatter was analyzed by testing the diagnostics models on simulated fleet data. It was shown that the condition-based hierarchical model resulted in the best accuracy, and the benefit was more significant for data with higher overlap between states (i.e., for compressor fouling). The improvement with the proposed dynamic Bayesian network was 8 percentage points (in classification accuracy) for compressor fouling and 5 points for turbine erosion compared with the static network.

There are many challenges that an effective diagnostic system must overcome for successful fault diagnosis in gas turbines. Among others, it has to be robust to engine-to-engine variations in the fleet, it has to discriminate between gradual deterioration and abrupt faults, and it has to identify sensor faults correctly and be robust in case of such faults. To combine their benefits and overcome their limitations, two diagnostic methods were integrated in this work to form a multi-layer system. An adaptive performance model was used to track gradual deterioration and detect rapid or abrupt anomalies, while a series of static and dynamic Bayesian networks were integrated to identify component degradation, component abrupt faults, and sensor faults. The proposed approach was tested on synthetic data and field data from a single-shaft gas turbine of 50 MW class. The results showed that the approach could give acceptable accuracy in the isolation and identification of multiple faults, with 99% detection and isolation accuracy and 1% maximum error in the identified fault magnitude. The approach was also proven robust to sensor faults, by replacing the faulty signal with an estimated value that had only 3% error compared to the real measurement.

Prognosis, or the forecasting of remaining operational life of a component, is a fundamental step for predictive maintenance of turbomachines. While diagnostics gives important information on the current conditions of the engine, it is through prognostics that a suitable maintenance interval can be determined, which is critical to minimize costs. However, mature prognostic models are still lacking in industry, which still heavily relies on human experience or generic statistical quantifications. Predicting future conditions is very challenging due to many factors that introduce significant uncertainty, including unknown future machine operations, interaction between multiple faults, and inherent errors in diagnostic and prognostic models. Given the importance to quantify this uncertainty and its impact on operational decisions, this work presents an information fusion approach for gas turbine prognostics. Condition monitoring performed by a Bayesian network is fused with a particle filter for prognosis of gas turbine degradation, and the effect of diagnostic models uncertainty on the prognosis are estimated through probabilistic analysis. Gradual and rapid degradation are simulated on a gas turbine performance model and the impact of sensor noise and initial conditions for the particle filter estimation are assessed. This work demonstrates that the combination of Bayesian networks and particle filters can give good results for short-term prognosis.

Since aeronautic transportation is responsible for a rising share of polluting emissions, it is of primary importance to minimize the fuel consumption any time during operations. From this perspective, continuous monitoring of engine performance is essential to implement proper corrective actions and avoid excessive fuel consumption due to engine deterioration. This requires, however, automated systems for diagnostics and decision support, which should be able to handle large amounts of data and ensure reliability in all the multiple conditions the engines of a fleet can be found in. In particular, the proposed solution should be robust to engine-to-engine deviations and dierent sensors availability scenarios. In this paper, a probabilistic Bayesian network for fault detection and identification is applied to a fleet of engines, simulated by an adaptive performance model. The combination of the performance model and the Bayesian network is also studied and compared to the probabilistic model only. The benefit in the suggested hybrid approach is identified as up to 50% higher accuracy. Sensors unavailability due to manufacturing constraints or sensor faults reduce the accuracy of the physics-based method, whereas the Bayesian model is less aected.