Fault Diagnosis for Variable Frequency Drive-Fed Induction Motors Using Wavelet Packet Decomposition and Greedy-Gradient Max-Cut Learning

Fault Diagnosis for Variable Frequency Drive-Fed Induction Motors Using Wavelet Packet Decomposition and Greedy-Gradient Max-Cut Learning

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In this paper, a novel fault diagnosis method for variable frequency drive (VFD)-fed induction motors is proposed using Wavelet Packet Decomposition (WPD) and greedy-gradient max-cut (GGMC) learning algorithm.The proposed method is developed using experimental ben nye sienna powder stator current data in the lab for two 0.25 HP induction motors fed by a VFD, subjected to healthy and faulty cases under various operating frequencies and motor loadings.

The features are extracted from stator current signals using WPD by evaluating gator phi gator energy eigenvalues and feature coefficients at decomposition levels.The proposed method is validated by comparing with other graph-based semi-supervised learning (GSSL) algorithms, local and global consistency (LGC) and Gaussian field and harmonic function (GFHF).To enable fault diagnosis for untested motor operating conditions, mathematical equations to calculate features for untested cases are developed through surface fitting using features extracted from tested cases.