Gas Turbine Health State Determination: Methodology Approach and Field Application

Pinelli, Michele; Spina, Pier Ruggero; Venturini, Mauro

doi:10.1155/2012/142173

Cited by 14 publications

(8 citation statements)

References 60 publications

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“…First, it allows simulating many fault scenarios and creating a fault classification (Loboda et al, 2007). Second, the model is the basis of GPA that involves nonlinear system identification techniques for the estimation of the parameters (Pinelli & Spina, 2002). These techniques look for those parameters that provide the best tuning of the model to the measurements of a particular engine.…”

Section: Gas Path Analysismentioning

confidence: 99%

Analysis of nonlinear gas turbine models using influence coefficients

González-Castillo

Loboda

2021

iit

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The limited availability of gas turbine data, especially faults data and the high costs and risks of using test benches to obtain it,causes that rarely have enough data for form a fault classification. These circumstances have created the need to develop models that can provide simulated data. The quality of the data generated depends on the complexity of the thermodynamic model and the mathematical solution. A method to evaluate the accuracy of the models and their linearization capacity is presented. The method is applied to the models of a turbo shaft and a turbo fan of the commercial software GasTurb 12, as an example. It was simulated a wide database with influence of fault parameters and condition operation, then it calculed the influence matrix ""H"" and ""G"" for prove the influence theirs on behavior of the models. The results show that if the model is sufficiently accuracy, it is possible to find an adequate interval where the linearization errors are not very large and it is just possible the linearization.

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Section: Gas Path Analysismentioning

confidence: 99%

Analysis of nonlinear gas turbine models using influence coefficients

González-Castillo

Loboda

2021

iit

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“…The data should be collected continuously throughout and periodically in the gas turbine life-cycle, because some vibration registers can indicate various failures type, the sample must be in a value capable of characterizing the equipment performance, and the values should always be carefully analyzed and if it is possible compared to other collected parameters [21].…”

Section: Inspection and Maintenance Planmentioning

confidence: 99%

A Statistical State Analysis of a Marine Gas Turbine

2019

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This paper describes the analysis, from a statistical point of view, of a maritime gas turbine, under various operating conditions, so as to determine its state. The data used concerns several functioning parameters of the turbines, such as temperatures and vibrations, environmental data, such as surrounding temperature, and past failures or quasi-failures of the equipment. The determination of the Mean Time Between Failures (MTBF) gives a rough estimate of the state of the turbine, but in this paper we show that it can be greatly improved with graphical and statistical analysis of data measured during operation. We apply the Laplace Test and calculate the gas turbine reliability using that data, to define the gas turbine failure tendency. Using these techniques, we can have a better estimate of the turbine’s state, and design a preventive observation, inspection and intervention plan.

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“…Maintaining high levels of availability and reliability is an essential objective for all production units, especially for those that are subject to high costs due to loss of production [3]. Many signal analysis methods are able to extract useful information from vibration data.…”

Section: Literature Surveymentioning

confidence: 99%

Condition Monitoring of Air Compressor in Steel Industry using ANFIS

B¹,

Tjprc²

2019

IJMPERD

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As large rotating machines are increasingly employed in continuous operations at high speeds and with heavy loads, vibration behavior of rotating systems is emerging as more complex phenomenon. Monitoring vibration behaviour of large rotating machinery is an effective way to reduce losses and enhance safety, reliability, availability and durability in manufacturing processes. This research focuses on condition monitoring of one of the vital and the most critical machine Air Compressor of steel industry. It considers vibration levels of the machinery based on ISO limit of vibration severity using Adoptive neuro fuzzy inference system (ANFIS). Two different data schemes were formulated based on preliminary experimentation on Sugeno type 3 inputs (v Hm , v Vm & v Am) and 1 output (i. e., Condition) ANFIS model. The performance criterion of the ANFIS classifier was evaluated using confusion matrix. The total classification accuracy of 95% obtained proves the validation of the Air Compressor model. ANFIS can also be extended to condition monitoring of various rotating machinery.

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Gas Turbine Health State Determination: Methodology Approach and Field Application

Cited by 14 publications

References 60 publications

Analysis of nonlinear gas turbine models using influence coefficients

Analysis of nonlinear gas turbine models using influence coefficients

A Statistical State Analysis of a Marine Gas Turbine

Condition Monitoring of Air Compressor in Steel Industry using ANFIS

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