Induction Motor Centrifugal Blower Health Diagnostic Based on Color Segmentation of Thermal Image and Vibration Signal Feature

Abstract

The rotating machinery requires condition monitoring which its measurement without being intrusive operation, especially on the equipment needed to continue running. One such machinery is a centrifugal blower induction motor. Infrared thermography and vibration are important and effective technologies to diagnose of health condition it without destructive or disturb of operations. The diagnostics of induction motor are based on the analysis results data onto vibration and processing thermal image. This paper focused on thermography image processing based on color segmentation which it will produce ROI (region of interest) images. The ROI image is extracted based on HSV color and shape feature. Feature extraction is intended to determine value of mean, standard deviation, kurtosis, skewness and entropy HSV and shape features (area, perimeter, metric, and eccentricity). The highest RMS (root mean square) vibration data is used as reference to classify data into normal and abnormal. Parameters that can be used to classify normal and abnormal conditions based on data analysis are standard deviation Hue, kurtosis HS, skewness HSV, entropy HSV and metric.