Application of a neural network in gas turbine control sensor fault detection

Simani, Silvio; Fantuzzi, Cesare; Spina, R.

doi:10.1109/cca.1998.728322

Cited by 19 publications

(12 citation statements)

References 13 publications

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“…After transforming the real-valued responses of the neural fault classifier presented in Figs. [27][28][29][30] to the binary ones by using Eq. ( 17), the fault isolation results for the test phase are summarised in Tables 6 and 7.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In order to compare our proposed robust FDI method with other developed FDI techniques on this gas turbine benchmark, the studies [25,16,24,27] are taken into consideration. An overall comparison between our work and the other ones shows that in most of the considered studies, no attempt has been made to propose a nonlinear RFDI method.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques

et al. 2012

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques

et al. 2012

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“…The healthy state of the engine and the cost reduction of its maintenance and repair could be obtained with the confirmation or the early detection of defects. [1,5,6] Additionally, the increased stability, maneuverability, and reliability of an in-flight aircraft would be acquired with prevention of unexpected failure of the engine. [7,8] In order to develop the defect diagnostic system, ANN, GA, and SVM algorithms have been commonly used.…”

Section: Introductionmentioning

confidence: 99%

“…[7,8] In order to develop the defect diagnostic system, ANN, GA, and SVM algorithms have been commonly used. [2,5,[9][10][11][12] Among them, the ANN algorithm has been widely used to solve the pattern recognition problem for defect diagnostic systems. [5,12,13] The neural network algorithm is able to predict the characteristics of uncertain groups based on the specific information.…”

Section: Introductionmentioning

confidence: 99%

Defect diagnostics of SUAV gas turbine engine using hybrid SVM-artificial neural network method

2009

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A hybrid method of an artificial neural network (ANN) combined with a support vector machine (SVM) has been developed for the defect diagnostic system applied to the SUAV gas turbine engine. This method has been suggested to overcome the demerits of the general ANN with the local minima problem and low classification accuracy in case of many nonlinear data. This hybrid approach takes advantage of the reduction of learning data and converging time without any loss of estimation accuracy because the SVM classifies the defect location and reduces the learning data range. The results of test data have shown that the hybrid method is more reliable and suitable algorithm than the general ANN for the defect diagnosis of the gas turbine engine.

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“…• Neural networks, soft computing, and principal component analysis (PCA) [16][17][18][19][20][21][22][23];…”

Section: Introductionmentioning

confidence: 99%

Feature-based fault detection of industrial gas turbines using neural networks

Rasaienia¹,

Moshiri²,

Moezzi³

2013

Turk J Elec Eng & Comp Sci

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Gas turbine (GT) fault detection plays a vital role in the minimization of power plant operation costs associated with power plant overhaul time intervals. In other words, it is helpful in generating pre-alarms and paves the way for corrective actions in due time before incurring major equipment failures. Hence, finding an efficient fault detection technique that is applicable in the online operation of power plants involved with minor computations is an urgent need in the power generation industry. Such a method is studied in this paper for the V94.2 class of GTs. As the most leading stage for developing a feature-based fault detection system and moving from a fixed time-scheduled maintenance to a condition-based one, principal component analysis is used for dimension reduction in the sensor data space and dimensionless key features are employed instead. One healthy condition and 6 faulty conditions are used to provide a realistic data set that is used for feature extraction, training, and testing artificial neural networks. In the proposed method, multilayer perceptron (MLP) and learning vector quantization (LVQ) networks are used for the fault classification. The good performance of the LVQ networks is presented by properly selecting the network architecture and respective initial weight vectors. When comparing the results of the MLP and LVQ networks for the fault classification, the LVQ network shows better classification results.

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Application of a neural network in gas turbine control sensor fault detection

Cited by 19 publications

References 13 publications

Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques

Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques

Defect diagnostics of SUAV gas turbine engine using hybrid SVM-artificial neural network method

Feature-based fault detection of industrial gas turbines using neural networks

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