PROJECT TITLE :

A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors

ABSTRACT:

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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