GUO Yanqiu, MIAO Changyun, LIU Yi. Research on fault detection of belt conveyor roller based on thermal infrared image[J]. Journal of Mine Automation,2023,49(10):52-60. DOI: 10.13272/j.issn.1671-251x.2022120051
Citation: GUO Yanqiu, MIAO Changyun, LIU Yi. Research on fault detection of belt conveyor roller based on thermal infrared image[J]. Journal of Mine Automation,2023,49(10):52-60. DOI: 10.13272/j.issn.1671-251x.2022120051

Research on fault detection of belt conveyor roller based on thermal infrared image

  • Currently, the inspection robot for belt conveyors equipped with infrared acquisition devices is limited in movement. There are problems such as inability to collect data, process data, upload data to monitoring terminals in real-time and complete remote fault detection, insufficient endurance and so on. A fault detection method of belt conveyor roller based on thermal infrared images has been proposed. The belt conveyor inspection robot is equipped with a roller fault detector and an infrared thermal imager. The infrared thermal imager transmits the collected roller thermal infrared image sequence and temperature data to the roller fault detector for roller fault detection. The WH-L101 wireless transmission module in the roller fault detector is used to send the detection results to the upper computer. A belt conveyor roller fault detection algorithm is proposed. The algorithm uses the YOLOv5s object detection algorithm to extract the region of interest (ROI) of the roller thermal infrared image. The image of the ROI is filtered using Wiener filtering and adaptive median filtering algorithms. The filtered ROI image is enhanced by using adaptive histogram equalization and image sharpening algorithms. The Otsu image segmentation algorithm based on morphology is used to segment the enhanced ROI image, obtaining the roller image to be detected. The Harris corner detection algorithm is used to extract the features of the roller image, and obtain the position information of the roller. The temperature information of the corresponding position is extracted, and a roller fault detection algorithm based on the relative temperature difference method is used to determine the idler fault. The experimental results show: ① The average accuracy of object detection in the roller ROI extracted by YOLOv5s network model is 99.12%. ② The proposed roller fault detection algorithm has an average accuracy of 97.625% and a frame rate of 16 frames per second for detecting roller faults (no faults, bearing rust, roller jamming, and cylinder wear). ③ The detection results are transmitted to the upper computer through a wireless transmission module, which can display the fault type and key area temperature, and provide an alarm.
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