Recursive Undecimated Wavelet Packet Transform and DAG SVM for Induction Motor Diagnosis PROJECT TITLE :Recursive Undecimated Wavelet Packet Transform and DAG SVM for Induction Motor DiagnosisABSTRACT:This paper is concentrated on the design of a brand new approach dedicated to solve classification issues for the detection of broken rotor bar (BRB) fault in induction motors (IM). This new method finds its origins in an exceedingly novel combination of both recursive undecimated wavelet packet rework (RUWPT) and directed acyclic graph support vector machines (DAG SVMs). Most typically, BRB frequency elements are hardly detected in the stator current due to its low magnitude and closeness to the availability frequency part. To beat this downside, the RUWPT is applied to extract one parameter able to detect the fault with arbitrary operating conditions and a great concern of low load cases. Completely different multiclass support vector machines (MSVMs) ways are evaluated with respect to accuracy, number of support vectors, and testing time. The experimental results ensure that the DAG SVMs and Symlet wavelet kernel operate are quick, robust, and give the best classification accuracy of ninety ninep.c. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest High-Efficiency Multilevel Flying-Capacitor DC/DC Converter for Distributed Renewable Energy Systems UWB-Aided Inertial Motion Capture for Lower Body 3-D Dynamic Activity and Trajectory Tracking
