Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

Castro-Cros, Martí de; Velasco, Manel; Ángulo, Cecilio

doi:10.3390/en14248468

Cited by 18 publications

(6 citation statements)

References 95 publications

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“…NARX (nonlinear autoregressive with exogenous input) is one of most widely used artificial neural network (ANN) modeling methods. As a recurrent neural network (RNN), the NARX model can capture the dynamics of complex systems such as GTs [14]. NARX model is defined by Once the training, testing and validating datasets are defined, the number of neurons needs to be determined for the NARX model.…”

Section: B Narx Methodologymentioning

confidence: 99%

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Towards Predictive Maintenance of a Heavy-Duty Gas Turbine A New Hybrid Intelligent Methodology for Performance Simulation

Liu¹,

Jiang²

2022

PHM_CONF

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As the increasing demands of global clean power for the main purpose of lowering environmental pollution, heavy-duty gas turbines are playing an increasingly important role in energy fields because of their low emission, high thermal efficiency and flexible start-up capacity. Accurate modeling and simulation of the turbine performance is extremely needed to precisely design a large-watt industrial heavy-duty gas turbine, which is still very challenging due to the high nonlinearity, system complexity, varying conditions, and strong coupling interaction of high-dimension parameters under the harsh operation environment of the turbine. In order to improve the accuracy and efficiency of the performance simulation model for a heavy-duty gas turbine at various operating conditions, this paper presents a new hybrid intelligent methodology to adeptly integrate system thermodynamic balance mechanism with multivariate data by seamlessly combing advanced signal processing, machine learning and artificial neural network modeling techniques. The thermodynamic model of a complicated single-shaft gas turbine is first created based on the balances of both flow and power in various subsystems including inlet, compressor, turbine, combustor and exhaust. The characteristic curves of compressor and turbine are utilized to accurately represent the physical mechanism and effectively simulate the high nonlinear behaviors of subsystems. Wavelet signal processing and machine learning based feature extraction are employed to preprocess the multivariate raw data of the turbine. Multilayer artificial neural network models are explored to efficiently simulate the start-up transient process of the turbine, thus improve the simulation efficiency and accuracy of the complicated system. Multivariate data collected from a real-world industrial heavy-duty gas turbine is employed to illustrate the effectiveness and feasibility of the proposed methodology.

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Section: B Narx Methodologymentioning

confidence: 99%

“…This means that the previous time-steps are (t-1) and (t-2), according to the analysis performed in Ref. [14].…”

Section: B Narx Methodologymentioning

confidence: 99%

Towards Predictive Maintenance of a Heavy-Duty Gas Turbine A New Hybrid Intelligent Methodology for Performance Simulation

Liu¹,

Jiang²

2022

PHM_CONF

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“…Lastly, recent studies by Hong and Kim (2023); De Castro-Cros et al (2021) employed machine learning techniques to predict gas turbine engine performance. Hong and Kim (2023) utilized operational parameters like fuel flow and compressor discharge pressure, employing artificial neural networks and support vector regression algorithms.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for the Prediction of Gas Turbine Transients

Fadja,

Cota,

Bertasi

et al. 2024

Journal of Computer Science

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Gas Turbine (GT) emergency shutdowns can lead to energy production interruption and may also reduce the lifespan of a turbine. In order to remain competitive in the market, it is necessary to improve the reliability and availability of GTs by developing predictive maintenance systems that are able to predict future conditions of GTs within a certain time. Predicting such situations not only helps to take corrective measures to avoid service unavailability but also eases the process of maintenance and considerably reduces maintenance costs. Huge amounts of sensor data are collected from (GTs) making monitoring impossible for human operators even with the help of computers. Machine learning techniques could provide support for handling large amounts of sensor data and building decision models for predicting GT future conditions. The paper presents an application of machine learning based on decision trees and k-nearest neighbors for predicting the rotational speed of gas turbines. The aim is to distinguish steady states (e.g., GT operation at normal conditions) from transients (e.g., GT trip or shutdown). The different steps of a machine learning pipeline, starting from data extraction to model testing are implemented and analyzed. Experiments are performed by applying decision trees, extremely randomized trees, and k-nearest neighbors to sensor data collected from GTs located in different countries. The trained models were able to predict steady state and transient with more than 93% accuracy. This research advances predictive maintenance methods and suggests exploring advanced machine learning algorithms, real-time data integration, and explainable AI techniques to enhance gas turbine behavior understanding and develop more adaptable maintenance systems for industrial applications.

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“…An analysis of the diagnostic of effective power and efficiency of the GTU was proposed in [7][8][9]. In recent years, the usage of computer neural networks has been growing for parametric analysis of the technical systems state [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Acoustic Monitoring of Gas Turbine Units by Fiber Optic Sensors

Stepanov

Zhirnov

Sazonkin

et al. 2022

Sensors

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In this article, we study the possibility of gas turbine unit (GTU) monitoring using interferometric fiber optic sensors. We used the Mach–Zehnder interferometer (MZI) scheme, which can be easily implemented and simply installed on the turbine, and also allows us to solve the problem of phase unwrapping conveniently. In this research, the following main steps were carried out: an experimental scheme based on the MZI was assembled, and its sensitive arm was fixed on the GTU under study; data on various operation modes of the GTU was collected; the data were subjected to frequency FFT analysis, based on which the main rotational speeds of the turbine were identified. With FFT analysis, we also demonstrated multiples harmonics, which appear in the case of GTU after operating time, caused by the number of blades. The possibility of GTU monitoring and analysis using a non-invasive compact fiber-optic sensor is demonstrated: spectral analysis is used to detect the rotor speed, as well as the presence or absence of high-order multiple frequencies indicating blade and bearing defects, which are determined by the number of GTU’s blades and rolling bearing used as turbines rotor supports.

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Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

Cited by 18 publications

References 95 publications

Towards Predictive Maintenance of a Heavy-Duty Gas Turbine A New Hybrid Intelligent Methodology for Performance Simulation

Towards Predictive Maintenance of a Heavy-Duty Gas Turbine A New Hybrid Intelligent Methodology for Performance Simulation

Machine Learning Approaches for the Prediction of Gas Turbine Transients

Non-Invasive Acoustic Monitoring of Gas Turbine Units by Fiber Optic Sensors

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