Defect detection in rotating machine elements by using an improved image segmentation technique

In industrial applications rotating machinery is widely used. Therefore it becomes necessary to diagnose the health of rotating machinery and detect any fault during their working. The pre-determination of defects can alert the operators and overall system for any type of incident and loss. Generally, fault in the rotating machine causes a rise in temperature of the rotating elements of the machine during their working. In this work, the image segmentation technique is used on thermal images of rotating elements of a machine to identify and detect the defects in them. The main focus of this investigation is to apply linear transformation and improved hyper smoothing based local binary pattern(LTIHLBP) image segmentation technique for defect detection and compare its performance with other conventional defect detection techniques namely improved hyper-smoothing function based local binary pattern (ILHBP), Sobel and Canny. In this paper, three tests are performed to identify the effectiveness of the proposed method. The first test is performed on synthetic images to identify the accuracy and Jaccard similarity index(JSI) of the proposed segmentation method, the second test is performed on healthy moving element images and the third test is performed on faulty moving element images. The outcome of this investigation revealed that the proposed method of LTIHLBP outperforms the conventional image segmentation-based defect detection techniques and found to be capable and accurate in diagnosing and detecting defects.