Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography

Abstract

For a belt conveyor, a belt with mini-cracks influences the safety of the operation of the belt conveyor devices significantly. The lumpy material rubbing against a belt with mini-cracks will lead to detrimental tears on the belt while the conveyor works at high speeds. In order to detect mini-cracks in a timely manner, before the occurrence of large tears, a new mini-crack detection method for conveyor belts based on laser excited thermography is proposed. A laser beam is applied to generate a heat wave on the belt surface, so the corresponding thermal images can be recorded by an infrared camera. Then, an algorithm for extracting and fusing the features of mini-crack belt’s thermal images is firstly proposed by using time sequence analysis. It could transform multiple sequential thermal images into a single thermal image according to the frame difference method, so that the infrared thermal response of the mini-crack over time could be recorded quickly. To reveal the features of mini-crack, the power function image enhancement algorithm is proposed. After that, the threshold segmentation and Hough transform algorithm are used to determine the size of the mini-crack. The experimental results show that the mini-cracks with length of 50–140 mm, width of 1–10 mm, and depth of 5 mm can be successfully detected, and the detection accuracy could reach 96%. The detection results of different sizes of mini-crack show that the laser excited thermography has high damage sensitivity (1 mm diameter mini-crack detection), high accuracy (96%), lower power consumption (15 W laser excitation), and high SNR (signal-to-noise ratio).

Identification of Selected Failures in a Pipe Conveyor’s Operation with the Use of the Discrimination Method Based on Continuous Measurement

“…The principle of discrimination is that, based on different combinations of CF values at three positions, one of the properties is assigned to the device: failure-free state, missing roller including its position, or other failures. Compared to the results presented in [21][22][23][24][25], the solution is more complex and more innovative. However, this research did not examine a single method application to identify one type of damage.…”

“…Until now, most of the presented papers have dealt with the detection of various failures in continuous transport systems, for example, [21][22][23][24][25]. Their common feature is the use of specific methods to identify various types of failures in continuous transport systems.…”

Crack detection in metallic materials using a mobile robot

“…No metal surface preparation or other treatment is required. The technological advances there is an increasing production of sensors, these have multiple applications in different fields such as monitoring and/or control of industrial processes, detection of oil leaks, medicine, and deformation of aeronautical and civil structures [6][7][8]. In the latter, it is associated with the deformation caused by a crack, discontinuity, or residual stress, where a specific design of a microsensor is required to find the correlation between both variations.…”

A conveyor belt characterization information extraction method based on YOLOv5 and the skeleton method

“…Che et al [11] performed damage detection by fusing conveyor belt images and sound features, and using machine learning algorithms. Zeng et al [12] proposed a thermal image feature extraction and fusion algorithm for microcracked belts based on time-series seams to implement the detection of microcracks on conveyor belt surfaces. However, the actual working environment of a belt conveyor is usually accompanied by considerable dust, smoke and uneven light, which can cause the conveyor belt image inspection to be vulnerable to dust interference, motion blur and uneven light.…”