PROJECT TITLE :
A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors
One of the most common deficiencies of currently existing induction motor fault diagnosis techniques is their lack of automatization. Several of them depend on the qualitative interpretation of the results, a reality that needs important user experience, and that produces their implementation in portable condition monitoring devices troublesome. During this paper, we have a tendency to present an automatic technique for the detection of the amount of broken bars of an induction motor. The tactic relies on the transient analysis of the start-up current using wavelet approximation signal that isolates a characteristic part that emerges once a rotor bar is broken. After the isolation of this part, a range of stages are applied that rework the continual-valued signal into a discrete one. Subsequently, an intelligent icon-like approach is applied for condensing the relative information into a illustration which will be simply manipulated by a nearest neighbor classifier. The approach is tested using simulation furthermore experimental data, achieving high-classification accuracy.
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