This paper is focused into the design of a new approach dedicated to solve classification problems for the detection of Broken Rotor Bar (BRB) fault in induction motors. This new method finds its origins in a novel combination of both, recursive undecimated wavelet packet transform (RUWPT) and directed acyclic graph support vector machines (DAG SVM). Most often, BRB frequency components are hardly detected in the stator current due to its low magnitude and its closeness to the supply frequency component. To overcome this drawback, the RUWPT is applied to extract one parameter able to detect the fault with arbitrary working conditions and a great concern of low load cases. Different multi-class support vector machines (MSVM) methods are evaluated with respect to accuracy, number of support vectors, and testing time. The experimental results confirm that the DAG SVM and symlet wavelet kernel function are fast, robust and give the best classification accuracy of 99%. ).A. Braham is with University of Carthage and the MMA research laboratory, Tunisia
This paper proposes an original combination of Stationary Wavelet Packet Transform (SWPT) and Multiclass Wavelet Support Vector Machines (MWSVM) to detect broken rotor bar (BRB) in induction motor (1M). The SWPT is used for feature extraction under lower sampling rate. MWSVM is developed to perform the faults recognition. Different binary Multiclass SVM strategies are compared with various wavelet kernel functions in terms of classification accuracy, training and testing complexity. The experimental results show that the proposed method is able to detect the faulty conditions with high accuracy.
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