Automatic Fault Diagnosis System for a Gas Turbine using a Simulation before Test Approach

Catelani, Marcantonio; Fort, Ada; Grande, Vito; Mugnaini, Marco; Trotta, I.

doi:10.1109/imtc.2006.328180

Cited by 1 publication

(2 citation statements)

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“…Coding schemes presented in Section 2.2 were implemented to design the RBF network, train it on the set L and test using the set T. The first operation is done according to the Simulation Before Test (SBT) [24], [25] paradigm, i.e. when knowledge is extracted prior to the diagnostic module implementation in the industrial environment.…”

Section: Coding Schemes Verificationmentioning

confidence: 99%

“…Figure 10 presents comparison between different coding schemes of the optimal RBF network structure for different sets of accessible nodes. Besides the node 9 (always observable, therefore usable in the input-output analysis [24]), all others can be theoretically made accessible. Because the filter has the sequential structure, it is reasonable to assume that more information can be recovered from the nodes close to the output of the SUT.…”

Section: Testability Verificationmentioning

confidence: 99%

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Analysis of the RBF ANN-based classifier for the diagnostics of electronic circuit

Bilski

Połok

2018

ACTA IMEKO

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<span lang="EN-GB">The paper presents the application of the Radial Basis Function (RBF) Artificial Neural Network (ANN) to the diagnostics of the analog circuit. Such networks are in most cases useful in the approximation tasks as the alternative to multilayered perceptrons (MLP) or Support Vector Machines (SVM). In this work the analysis of various RBF ANN-based classifier configurations for the fault detection and identification module are is conducted. The considered parameters included the optimal number of neurons in the hidden layer, coding schemes for the output layer neurons and operation duration during the training and testing the classifier. The efficiency of the diagnostic system is verified using the fifth order lowpass filter. The circuit was also analyzed in terms of the testability, depending on the set of accessible nodes, confronted against the output node only. Experiments cover also accuracy comparison between the RBF, MLP and SVM classifiers. Results show advantages and drawbacks of the RBF ANN-based diagnostic module, compared to other available solutions.</span>

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Section: Coding Schemes Verificationmentioning

confidence: 99%

Section: Testability Verificationmentioning

confidence: 99%