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This paper presents three intelligent methods for condition monitoring of induction motors in real-time. A structured neural network has been designed to prognosis of instantaneous faults. The inputs of neural network are the standard deviation and mean of feature signal obtained by Hilbert transform of one phase current signal. The stator related faults have been diagnosed by designing fuzzy logic...
Fuzzy integral is an information aggregation and combination process in a multi-criteria environment using fuzzy measures. This paper presents a new data fusion method using fuzzy integral for fault diagnosis. The method consists of two frameworks. The first framework was employed to identify the relations between features and a specified fault. The second framework was implemented to integrate different...
Early detection and diagnosis of incipient faults are desired for online condition monitoring and improved operational efficiency of induction motors. In this study, an artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm is developed to detect broken rotor bar and broken connector faults in induction motors. The proposed algorithm uses only one phase...
The use of induction motors is widespread in industry. Many researchers have studied the condition monitoring and detecting the faults of induction motors at an early stage. Early detection of motor faults results in fast unscheduled maintenance. In this study, a new artificial immune based support vector machine algorithm is proposed for fault diagnosis of induction motors. Support vector machines...
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