۱- Introduction

۲- Proposed feature extraction methods for thermal image processing

۳- Experimental study

۴- Conclusion

References

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.

Introduction

Different faults are likely to occur on the induction motor during its operation causing it to fail. However, through condition monitoring the lifetime of the motor could be increased by receiving up-to-date information about its behavior. In general, induction motors have two main types of faults namely electrical and mechanical, which are caused by heavy loading conditions. The most common and frequent fault is the mechanical fault as it accounts for about 53% of faults [1,2]. Generally, bearing defects are classified as mechanical faults referred to as inner race, outer race and ball bearing defects [3–۵]. Recently, with the purpose of keeping the rotating machinery working in safe and reliable mode, the area of fault diagnosis based on condition monitoring has received the researchers’ attention to develop novel fault detection and classification methods which are capable of overcoming the limitations of the current condition monitoring methods. In recent years, Infrared Thermography (IRT) has been used as a condition monitoring technique, as it is non-intrusive, non-contact, single sensor based on the temperature measurements and fine-grained system [6]. Thus, infrared thermography has been adopted for monitoring the induction motors and diagnose its faults by comparing the hot region of the healthy motor image (reference image) with the hot region of the faulty motor image (faulty image). The thermal image contains much information about the motor and this information could be extracted using different image processing techniques [7].