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

Derivative-driven window-based regression method for gas turbine performance prognostics

The domination of gas turbines in the energy arena is facing many challenges from environmental regulations and the plethora of renewable energy sources. The gas turbine has to operate under demand-driven modes and its components consume their useful life faster than the engines of the base-load operation era. As a result the diagnostics and prognostics tools should be further developed to cope with the above operation modes and improve the condition based maintenance (CBM). In this study, we present a derivative-driven diagnostic pattern analysis method for estimating the performance of gas turbines under dynamic conditions. A real time model-based tuner is implemented through a dynamic engine model built in Matlab/Simulink for diagnostics. The nonlinear diagnostic pattern is then partitioned into data-windows. These are the outcome of a data analysis based on the second order derivative which corresponds to the acceleration of degradation. Linear regression is implemented to locally fit the detected deviations and predict the engine behavior. The accuracy of the proposed method is assessed through comparison between the predicted and actual degradation by the remaining useful life (RUL) metric. The results demonstrate and illustrate an improved accuracy of our proposed methodology for prognostics of gas turbines under dynamic modes. © 2017 Elsevier Lt

A dynamic prognosis scheme for flexible operation of gas turbines

The increase in energy demand has led to expansion of renewable energy sources and their integration into a more diverse energy mix. Consequently the operation of thermal power plants, which are spearheaded by the gas turbine technology, has been affected. Gas turbines are now required to operate more flexible in grid supporting modes that include part-load and transient operations. Therefore, condition based maintenance should encapsulate this recent shift in the gas turbine's role by taking into account dynamic operating conditions for diagnostic and prognostic purposes. In this paper, a novel scheme for performance-based prognostics of industrial gas turbines operating under dynamic conditions is proposed and developed. The concept of performance adaptation is introduced and implemented through a dynamic engine model that is developed in Matlab/Simulink environment for diagnosing and prognosing the health of gas turbine components. Our proposed scheme is tested under variable ambient conditions corresponding to dynamic operational modes of the gas turbine for estimating and predicting multiple component degradations. The diagnosis task developed is based on an adaptive method and is performed in a sliding window-based manner. A regression-based method is then implemented to locally represent the diagnostic information for subsequently forecasting the performance behavior of the engine. The accuracy of the proposed prognosis scheme is evaluated through the Probability Density Function (PDF) and the Remaining Useful Life (RUL) metrics. The results demonstrate a promising prospect of our proposed methodology for detecting and predicting accurately and efficiently the performance of gas turbine components as they degrade over time. © 2015 Elsevier Ltd

Machine-learning-based condition assessment of gas turbine: a review

Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machinelearning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.This research was funded by Siemens Energy.Peer ReviewedPostprint (published version

Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies

Maintenance is crucial for aircraft engines because of the demanding conditions to which they are exposed during operation. A proper maintenance plan is essential for ensuring safe flights and prolonging the life of the engines. It also plays a major role in managing costs for aeronautical companies. Various forms of degradation can affect different engine components. To optimize cost management, modern maintenance plans utilize diagnostic and prognostic techniques, such as Engine Health Monitoring (EHM), which assesses the health of the engine based on monitored parameters. In recent years, various EHM systems have been developed utilizing computational techniques. These algorithms are often enhanced by utilizing data reduction and noise filtering tools, which help to minimize computational time and efforts, and to improve performance by reducing noise from sensor data. This paper discusses the various mechanisms that lead to the degradation of aircraft engine components and the impact on engine performance. Additionally, it provides an overview of the most commonly used data reduction and diagnostic and prognostic techniques

A Scientometric Methodology Based on Co-Word Analysis in Gas Turbine Maintenance

Evaluation of scientific journals has a profound effect on the future of scientific research so that different institutes and countries can set appropriate goals and invest with less risk in various scientific fields. Accordingly, this article presents a new method based on a combination of co-word analysis and social network analysis to extract the hotspot topics. Using HistCite, NodeXL, and VOSviewer, then combining their results, the desired analysis is conducted for six time periods. Based on the bibliographic parameters in HistCite and by defining an index, the first five periods are selected such that both quantity and quality of articles in each period are maximum compared to other years, while the sixth time period contains the latest research. For each of the six periods, the co-word networks as created in VOSviewer are analyzed. Next, based on a combination of network centralities developed in NodeXL, the hotspot keywords are specified which are then validated and aggregated using the bibliographic parameters in HistCite. The results reveal five important time periods in gas turbine maintenance. The hotspot keywords obtained for the last period show that in recent years, some topics including gas turbine fault prognosis, neural network-based approaches, big data analysis, sensor fault diagnosis, blade availability, economic analysis and useful life estimation are prominent subjects in gas turbine maintenance

Aircraft Exhaust Gas Temperature Value Mining with Rough Set Method

Aircrafts are one of the most important means of transportation today. For aircrafts to be able to serve safely, their maintenance must be done in a timely and complete manner. In addition to regular maintenance, it may appear suddenly; there is also irregular maintenance performed in cases such as lightning strikes, bird strikes, and hard landings. Engine failures and maintenance has great importance in aircraft maintenance. Using the data recorded during the flight by flight data recorder, the engine health condition is monitored and the necessary maintenance procedures are carried out. In this study, the exhaust gas temperature was estimated using various data mining algorithms. Because exhaust gas temperature is one of the important parameters used to monitor the aircraft engine health condition. The obtained mining results show the Random Forest Algorithm has best estimation performance. With mining of exhaust gas temperature value, faults can be detected before costly maintenance and accidents. So preventive maintenance methods will be applied, aircraft engines will remain healthy, a significant reduction in the maintenance cost of the operator will be achieved, as well as flight safety and environmental protection

Neural nonlinear autoregressive model with exogenous input (Narx) for turboshaft aeroengine fuel control unit model†

none3noOne of the most important parts of a turboshaft engine, which has a direct impact on the performance of the engine and, as a result, on the performance of the propulsion system, is the engine fuel control system. The traditional engine control system is a sensor-based control method, which uses measurable parameters to control engine performance. In this context, engine component degradation leads to a change in the relationship between the measurable parameters and the engine performance parameters, and thus an increase of control errors. In this work, a nonlinear model predictive control method for turboshaft direct fuel control is implemented to improve engine response ability also in presence of degraded conditions. The control objective of the proposed model is the prediction of the specific fuel consumption directly instead of the measurable parameters. In this way is possible decentralize controller functions and realize an intelligent engine with the development of a distributed control system. Artificial Neural Networks (ANN) are widely used as data-driven models for modelling of complex systems such as aeroengine performance. In this paper, two Nonlinear Autoregressive Neural Networks have been trained to predict the specific fuel consumption for several transient flight maneuvers. The data used for the ANN predictions have been estimated through the Gas Turbine Simulation Program. In particular the first ANN predicts the state variables based on flight conditions and the second one predicts the performance parameter based on the previous predicted variables. The results show a good approximation of the studied variables also in degraded conditions.openDe Giorgi M.G.; Strafella L.; Ficarella A.De Giorgi, M. G.; Strafella, L.; Ficarella, A

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