Three-phase induction motor fault detection based on thermal image segmentation

Abstract Induction motors are widely used in many industrial applications. Hence, it is very important to monitor and detect any faults during their operation in order to alert the operators so that potential problems could be avoided before they occur. In general, a fault in the induction motor causes it to get hot during its operation. Therefore, in this paper, thermal condition monitoring has been applied for detecting and identifying the faults. The main contribution of this study is to apply new colour model identification namely Hue, Saturation and Value (HSV), rather than using the conventional grayscale model. Using this new model the thermal image was first converted into HSV. Then, five image segmentation methods namely Sobel, Prewitt, Roberts, Canny and Otsu was used for segmenting the Hue region, as it represents the hottest area in the thermal image. Later, different image matrices containing the best fault information extracted from the image were used in order to discriminate between the motor faults. The values which were extracted are Mean, Mean Square Error and Peak Signal to Noise Ratio, Variance, Standard Deviation, Skewness and Kurtosis. All the above features were applied in three different motor bearing fault conditions such as outer race, inner race and ball bearing defects with different load conditions namely No load, 50% load and 100% load. The results showed that the proposed HSV colour model based on image segmentation was able to detect and identify the motor faults correctly. In addition, the method described here could be adapted for further processing of the thermal images.