Fault diagnosis of induction motors using signal processing based methods and optimal feature selection

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Nội dung chi tiết: Fault diagnosis of induction motors using signal processing based methods and optimal feature selection

Fault diagnosis of induction motors using signal processing based methods and optimal feature selection

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Fault diagnosis of induction motors using signal processing based methods and optimal feature selection ault Diagnosis of Induction Motors using Signal Processing based Methods and Optimal Feature Selection0|>S|1Xlisa-2008 y 12 s?!!*1-Al-eiỄS-éJ AF¥I£4èJ

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SelectionNgoc-Tu NguyenA thesis submitted to the School of Electrical Engineering 111 fulfillment of the thesis requirements for the degree ofDoctoi o

Fault diagnosis of induction motors using signal processing based methods and optimal feature selection

f Philosophy in the Graduate School. University of Ulsan39783AbstractFault detection and diagnosis in rotating machines have been used widely U1 comme

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Fault diagnosis of induction motors using signal processing based methods and optimal feature selection nalysis) method, the vibration-based methods, etc The purpose of these methods IS to detect and diagnose faults in an early stage and therefore allow

contingency plans to be put into place before the problems worsen. The dynamic and vibratory behaviours of the machine, such as vibration, sound, and Fault diagnosis of induction motors using signal processing based methods and optimal feature selection

temperature... are affected if the naming condition is changed. The behaviours can be useful indicators to delect problems within the machine as they

Fault diagnosis of induction motors using signal processing based methods and optimal feature selection

vary abnormally from a standard when a fault occurs. Of the many signals which can be measured, the vibration signal has been the most usefill to moni

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Fault diagnosis of induction motors using signal processing based methods and optimal feature selection .The signal processing methods for induction motor fault detection have recently received great attention because they do not need a typical mathemati

cal model Many signal processing diagnostic procedures have been studied in this work to identify faults of the machines The decision tree, support ve Fault diagnosis of induction motors using signal processing based methods and optimal feature selection

ctor machine (SVM), artificial neural network (ANN), adaptive neuro* fuzzy inference system (ANFIS). and k-nearest neighbour (K-NN) have been applied

Fault diagnosis of induction motors using signal processing based methods and optimal feature selection

to diagnose the condition of machines with rather high accuracy. These methods have used vibration data as an indicator for monitoring the fault condi

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Fault diagnosis of induction motors using signal processing based methods and optimal feature selection is proposed in this work for improving the classification performance of the diagnostics system. The classification results have proved the efficienc

y of the proposed optimal feature selection and the suitability of vibration data as an indicator for induction motor fault diagnosis.1 Fault diagnosis of induction motors using signal processing based methods and optimal feature selection

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