Tri-Axial Vibration Analysis Using Data Mining for Multi Class Fault Diagnosis in Induction Motor

Abstract

Induction motor frame vibration is believed to contain certain crucial information which not only helps detecting faults but also capable of diagnosing different types of faults that occur. The vibration data can be in radial, axial and tangential directions. The frequency content of the three different directions are compared and analyzed using data mining techniques to find the most informative vibration data and to extract the vital information that can be effectively used to diagnose multiple induction motor faults. The vibration data is decomposed using powerful signal processing tools like Continuous Wavelet Transform (CWT) and Hilbert Transform (HT). Statistical features are extracted from the decomposition coefficients obtained. Finally, data mining is applied to extract knowledge. Three types of data mining tools are deployed: sequential greedy search (GS), heuristic genetic algorithm (GA) and deterministic rough set theory (RST). The classification accuracy is judged by five types of classifiers: k-Nearest Neighbors (k-NN), Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Support Vector Machine (SVM), and Simple logistic. The benefits of using all the tri-axial data combined for vibration monitoring and diagnostics is also explored. The results indicate that tri-axial vibration combined provides the most informative knowledge for multi-class fault diagnosis in induction motor. However, it was also found that multi-class fault diagnosis can also be done quite effectively using only the tangential vibration signal with the help of data mining knowledge discovery.