Gas Turbine Fault Detection and Isolation Using Adaptive Neurofuzzy Inference System (ANFIS)

Shahriari, Behrooz; Shahrbabaki, Amin Nazarian; Shahriari, Ali

doi:10.4028/www.scientific.net/amr.1016.721

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Therefore, hybrid versions of ANN and other systems, such as fuzzy systems, are often used. A combination of the adaptive neuro-fuzzy inference system (ANFIS) allows one to benefit from both fuzzy rules -e.g., expert knowledge about known input or output distributions -and ANN for nonlinear and nonparametric estimation [15][16].…”

Section: Related Workmentioning

confidence: 99%

Monitoring the Gas Turbine Start-Up Phase on a Platform Using a Hierarchical Model Based on Multi-Layer Perceptron Networks

Niksa-Rynkiewicz

Witkowska

Głuch

et al. 2022

Polish Maritime Research

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Very often, the operation of diagnostic systems is related to the evaluation of process functionality, where the diagnostics is carried out using reference models prepared on the basis of the process description in the nominal state. The main goal of the work is to develop a hierarchical gas turbine reference model for the estimation of start-up parameters based on multi-layer perceptron neural networks. A functional decomposition of the gas turbine start-up process was proposed, enabling a modular analysis of selected parameters of the process. Real data sets obtained from observations of the turbo-generator set located on a North Sea platform were used.

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Section: Related Workmentioning

confidence: 99%

Monitoring the Gas Turbine Start-Up Phase on a Platform Using a Hierarchical Model Based on Multi-Layer Perceptron Networks

Niksa-Rynkiewicz

Witkowska

Głuch

et al. 2022

Polish Maritime Research

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“…With regard to this, many combined AI techniques are proposed by different authors including fuzzy-neural networks (FNNs), neural-fuzzy systems, Genetic fuzzy logic, Genetics neural network [3,33,37]. Combined CI techniques are applied for diagnostics purpose and showed a better performance [37,50,51,52,53].…”

Section: Combined/hybrid Ai Methodsmentioning

confidence: 99%

Gas turbine gas path diagnostics: A review

2016

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Abstract. In this competitive business world one way to increase profitability of a power production unit is to reduce the operation and maintenance expenses. This is possible if the gas turbine availability and reliability is improved using the appropriate maintenance action at the right time. In that case, fault diagnostics is very critical and effective and advanced methods are essential. Gas turbine diagnostics has been studied for the past six decades and several methods are introduced. This paper aims to review and summarise the published literature on gas path diagnostics, giving more emphasis to the recent developments, and identify advantages and limitations of the methods so that beginners in diagnostics can easily be introduced. Towards this end, this paper, identifies various diagnostic methods and point out their pros and cons. Finally, the paper concludes the review along with some recommended future works.

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“…ANFIS has been used extensively since its invention, for instance, [10] has developed a datadriven fault detection methodology for an industrial steam turbine using three ANFIS classifiers fed with the most influential diagnostic information. A model-based fault diagnosis system has been created in [11] on a gas turbine power plant by applying ANFIS to approximate the non-linear Rowen's model. [12] has also investigated condition monitoring and fault diagnosis in Francis turbine based on integration of numerical modelling of turbine runner with multiple ANNs and multiple ANFIS techniques.…”

Section: Introductionmentioning

confidence: 99%

Start-up vibration analysis for novelty detection on industrial gas turbines

Zhang

Cruz-Manzo

Latimer

2016

2016 International Symposium on Industrial Electronics (INDEL)

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Abstract-This paper focuses on industrial application of start-up vibration signature analysis for novelty detection with experimental trials on industrial gas turbines (IGTs). Firstly, a representative vibration signature is extracted from healthy start-up vibration measurements through the use of an adaptive neuro-fuzzy inference system (ANFIS). Then, the first critical speed and the vibration level at the critical speed are located from the signature. Finally, two (s-and v-) health indices are introduced to detect and identify different novel/fault conditions from the IGT start-ups, in addition to traditional similarity measures, such as Euclidean distance and cross-correlation measures. Through a case study on IGTs, it is shown that the presented approach provides a convenient and efficient tool for IGT condition monitoring using start-up field data.

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Gas Turbine Fault Detection and Isolation Using Adaptive Neurofuzzy Inference System (ANFIS)

Cited by 4 publications

References 4 publications

Monitoring the Gas Turbine Start-Up Phase on a Platform Using a Hierarchical Model Based on Multi-Layer Perceptron Networks

Monitoring the Gas Turbine Start-Up Phase on a Platform Using a Hierarchical Model Based on Multi-Layer Perceptron Networks

Gas turbine gas path diagnostics: A review

Start-up vibration analysis for novelty detection on industrial gas turbines

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