Steady State Detection in Industrial Gas Turbines for Condition Monitoring and Diagnostics Applications

Celis, César; Xavier, Érica; Teixeira, Tairo; Pinto, Gustavo R. S.

doi:10.1115/gt2014-25007

Cited by 10 publications

(5 citation statements)

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“…Ref. (10) uses a multilayer perceptron feedforward neural network with backpropagation algorithm, while Ref. (6) uses a more complex hierarchical set of 10 neural networks based on eleptic functions to classify a particular flight regime from flight parameter data, though the authors note that the number of networks can be reduced when considering only RTB regimes.…”

Section: Regime Recognition Techniquesmentioning

confidence: 99%

Automatic regime detection for Rotor Track and Balance using vibration only sensor data

Asher¹,

Toshkova²,

Lieven

2020

Aeronaut. j.

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Rotor Track and Balance (RTB) is an important part of regular helicopter maintenance. The ability to perform this service assessment during normal operations, rather than with a series of explicit RTB flights, would greatly reduce the time the vehicle is non-operational and the maintenance costs associated with these flights and adjustments. This paper presents a novel methodology for identifying the RTB-related flight regimes, using a minimal number of vibration signals and comparing these to repeatable and stable characteristic vibration profiles. The technique is stable, with an 81% success in correct identification of the flight regime, when applied to a whole flight with a number of unknown regime events. The method can be run in real time, making it an effective way of identifying periods of flight that are suitable for RTB measurements. A new technique for visually representing any real-time flight signal, such as vibration, is also presented.

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Section: Regime Recognition Techniquesmentioning

confidence: 99%

Automatic regime detection for Rotor Track and Balance using vibration only sensor data

Asher¹,

Toshkova²,

Lieven

2020

Aeronaut. j.

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“…The importance of identifying the steady-state operation of engineering systems in general has been recognized, and efforts towards developing algorithms that are able to identify such operation have been presented for many years (for example, [9]) using statistical concepts. The features of the problem of determining whether an operating condition can be characterized as steady-state, for the particular case of gas turbines, is also well known, and methods for its solution have been presented in the literature until very recently [10][11][12][13][14][15][16][17][18]. The motivation of exploiting test data for the purpose of engine condition monitoring was the driver of earlier methods [10,12].…”

Section: Introductionmentioning

confidence: 99%

“…The features of the problem of determining whether an operating condition can be characterized as steady-state, for the particular case of gas turbines, is also well known, and methods for its solution have been presented in the literature until very recently [10][11][12][13][14][15][16][17][18]. The motivation of exploiting test data for the purpose of engine condition monitoring was the driver of earlier methods [10,12]. Optimizing engine test cell trials was another motive [11], as the ability to characterize the operation reduces the testing time (and thus cost) while leading to better-quality data.…”

Section: Introductionmentioning

confidence: 99%

Determining Steady-State Operation Criteria Using Transient Performance Modelling and Steady-State Diagnostics

Mathioudakis,

Aretakis,

Alexiou

2024

Applied Sciences

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Data from the steady-state operation of gas turbine engines are used in gas path diagnostic procedures. A method to identify steady-state operation is thus required. This paper initially explains and demonstrates the factors that cause a deviation in engine health when transient data are used for diagnosis and shows that there is a threshold in the slope of time traces, below which the variation in engine health parameters is acceptable. A methodology for deriving a criterion for steady-state operation based on actual flight data is then presented. The slope of the exhaust gas temperature variation with time and the size of its time-series window, from which this slope is determined, are the required parameters that must be specified when applying this criterion. It is found that the values of these parameters must be selected so that a sufficient number of steady-state points are available without compromising the accuracy of the diagnostic procedure.

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“…In [ 31 ], Davison described a technique that measures how close an engine is to steady state while operating. Ceils et al [ 32 ] developed a signal analysis module that can determine the operating regimes of industrial gas turbines. Its use, however, is intended for monitoring and diagnostics of steady state operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Operational Modes Detection in Industrial Gas Turbines Using an Ensemble of Clustering Methods

Ghazvini

Sànchez–Marrè

Bahilo

et al. 2021

Sensors

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Operational modes of a process are described by a number of relevant features that are indicative of the state of the process. Hundreds of sensors continuously collect data in industrial systems, which shows how the relationship between different variables changes over time and identifies different modes of operation. Gas turbines’ operational modes are usually defined regarding their expected energy production, and most research works either are focused a priori on obtaining these modes solely based on one variable, the active load, or assume a fixed number of states and build up predictive models to classify new situations as belonging to the predefined operational modes. However, in this work, we take into account all available parameters based on sensors’ data because other factors can influence the system status, leading to the identification of a priori unknown operational modes. Furthermore, for gas turbine management, a key issue is to detect these modes using a real-time monitoring system. Our approach is based on using unsupervised machine learning techniques, specifically an ensemble of clusters to discover consistent clusters, which group data into similar groups, and to generate in an automatic way their description. This description, upon interpretation by experts, becomes identified and characterized as operational modes of an industrial process without any kind of a priori bias of what should be the operational modes obtained. Our proposed methodology can discover and identify unknown operational modes through data-driven models. The methodology was tested in our case study with Siemens gas turbine data. From available sensors’ data, clusters descriptions were obtained in an automatic way from aggregated clusters. They improved the quality of partitions tuning one consistency parameter and excluding outlier clusters by defining filtering thresholds. Finally, operational modes and/or sub-operational modes were identified with the interpretation of the clusters description by process experts, who evaluated the results very positively.

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Steady State Detection in Industrial Gas Turbines for Condition Monitoring and Diagnostics Applications

Cited by 10 publications

References 0 publications

Automatic regime detection for Rotor Track and Balance using vibration only sensor data

Automatic regime detection for Rotor Track and Balance using vibration only sensor data

Determining Steady-State Operation Criteria Using Transient Performance Modelling and Steady-State Diagnostics

Operational Modes Detection in Industrial Gas Turbines Using an Ensemble of Clustering Methods

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