A Model-Based Solution for Gas Turbine Diagnostics: Simulations and Experimental Verification

Zaccaria, Valentina; Stenfelt, Mikael; Sjunnesson, Anna; Hansson, Andreas; Kyprianidis, Konstantinos

doi:10.1115/gt2019-90858

Cited by 9 publications

(16 citation statements)

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“…Successful application of a fault detection and identification system on a micro gas turbine was achieved by integrating an adaptive model for GPA and data correction with a statistical algorithm [86]. Similarly, the diagnostic system was tested on a large unit from Siemens Industrial Turbomachinery, demonstrating the adaptability of the proposed method [87].…”

Section: Micro Gas Turbine Diagnostics and Decision Supportmentioning

confidence: 96%

A Review of Information Fusion Methods for Gas Turbine Diagnostics

et al. 2019

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The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.

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Section: Micro Gas Turbine Diagnostics and Decision Supportmentioning

confidence: 96%

A Review of Information Fusion Methods for Gas Turbine Diagnostics

et al. 2019

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“…A performance model was used to generate synthetic data to train and test the BNs. The performance model of the gas turbine system has been extensively described in previous work [11,[30][31][32]. Compared to [11,30], and similarly to [32], the model was modified…”

Section: Gas Turbine Modelmentioning

confidence: 99%

Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 1: Prior Probability Analysis

2021

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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.

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“…This information was then transformed into the functional Φ a (Equation (13)), and taking into account that in this case, the functionals Φ e and Φ a are homogeneous as they contain residuals by parameters of the same names. This facilitated the setting of weight coefficients in Equation (14). They had to relate to a scatter of measurements and a scatter of the engine parameters by series.…”

Section: Regularized Multi-criteria Identificationmentioning

confidence: 99%

“…The non-linear thermo-gas-dynamic models of turbine engines [6] and identification procedures have been applied in diagnostics for more than 40 years. Identification adjusts the model to make its output parameters as close as possible to the experimental data [7][8][9][10][11][12][13][14]. Besides the significant improvement in the gas path simulation, the estimated parameters contain information about the health of each component.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

et al. 2020

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Zero-dimensional models based on the description of the thermo-gas-dynamic process are widely used in the design of engines and their control and diagnostic systems. The models are subjected to an identification procedure to bring their outputs as close as possible to experimental data and assess engine health. This paper aims to improve the stability of engine model identification when the number of measured parameters is small, and their measurement error is not negligible. The proposed method for the estimation of engine components’ parameters, based on multi-criteria identification, provides stable estimations and their confidence intervals within known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularized identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Synthesis of objective functions and subsequent scalar convolutions of these functions are used to estimate gas-path components’ parameters. A comparison with traditional methods showed that the main advantage of the proposed approach is the high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions that do not correspond to a priori assumptions and also helps to implement the gas path analysis with a limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, gas path analysis and adapting dynamic models to the needs of the engine control system.

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A Model-Based Solution for Gas Turbine Diagnostics: Simulations and Experimental Verification

Cited by 9 publications

References 0 publications

A Review of Information Fusion Methods for Gas Turbine Diagnostics

A Review of Information Fusion Methods for Gas Turbine Diagnostics

Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 1: Prior Probability Analysis

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

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