Bo Chen, Husheng Yang, Jiarui Mei, Yueming Wang, Hao Zhang
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The result of the difference operation was compared with the preset threshold to determine whether the trolley wheel swings. When a wheel swing fault occurs, the image of the side plate at the time of the fault is collected, and the number on the side plate is identified so as to accurately locate the faulty trolley and to assist the field personnel in troubleshooting the fault. The experimental results show that this method can detect wheel swing faults in the industrial field, and the detection accuracy of wheel swing faults was 93.33%. The trolley side plate numbers’ average precision was 99.2% in fault localization. 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To solve this problem, this paper proposes a fault detection and localization method based on the You Only Look Once version 9 (YOLOv9) object detection algorithm and frame difference method for detecting sintering machine trolley wheel swing. The wheel images transmitted from the camera were sent to a trolley wheel and side panel number detection model that was trained on YOLOv9 for recognition. The wheel recognition boxes of the previous and subsequent frames were fused into the wheel region of interest. In the wheel region of interest, the difference operation was carried out. The result of the difference operation was compared with the preset threshold to determine whether the trolley wheel swings. When a wheel swing fault occurs, the image of the side plate at the time of the fault is collected, and the number on the side plate is identified so as to accurately locate the faulty trolley and to assist the field personnel in troubleshooting the fault. The experimental results show that this method can detect wheel swing faults in the industrial field, and the detection accuracy of wheel swing faults was 93.33%. The trolley side plate numbers’ average precision was 99.2% in fault localization. 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引用次数: 0
摘要
在炼铁生产的烧结过程中,车轮摆动是烧结机小车轴故障的一种表现,严重时可能导致车轮脱落,影响烧结机系统的生产运行。为解决这一问题,本文提出了一种基于 You Only Look Once version 9(YOLOv9)对象检测算法和帧差法的故障检测与定位方法,用于检测烧结机小车车轮摆动。摄像头传输的车轮图像被发送到基于 YOLOv9 训练的小车车轮和侧板编号检测模型中进行识别。前一帧和后一帧的车轮识别框被融合到车轮感兴趣区域中。在感兴趣的车轮区域内,进行差分运算。将差分运算的结果与预设阈值进行比较,以确定手推车车轮是否摆动。当发生车轮摆动故障时,采集故障发生时侧板的图像,并识别侧板上的编号,从而准确定位故障小车,协助现场人员排除故障。实验结果表明,该方法可以检测工业现场的车轮摆动故障,车轮摆动故障的检测准确率为 93.33%。小车侧板编号在故障定位中的平均精度为 99.2%。利用上述方法构建车轮摆动检测系统,可为烧结机小车车轴的故障检测提供技术支持。
Research on Sintering Machine Axle Fault Detection Based on Wheel Swing Characteristics
During the sintering process in iron production, wheel swing is a sign of sintering machine trolley axle faults, which may lead to the wheel falling off and affect the production operation of the sintering machine system in serious cases. To solve this problem, this paper proposes a fault detection and localization method based on the You Only Look Once version 9 (YOLOv9) object detection algorithm and frame difference method for detecting sintering machine trolley wheel swing. The wheel images transmitted from the camera were sent to a trolley wheel and side panel number detection model that was trained on YOLOv9 for recognition. The wheel recognition boxes of the previous and subsequent frames were fused into the wheel region of interest. In the wheel region of interest, the difference operation was carried out. The result of the difference operation was compared with the preset threshold to determine whether the trolley wheel swings. When a wheel swing fault occurs, the image of the side plate at the time of the fault is collected, and the number on the side plate is identified so as to accurately locate the faulty trolley and to assist the field personnel in troubleshooting the fault. The experimental results show that this method can detect wheel swing faults in the industrial field, and the detection accuracy of wheel swing faults was 93.33%. The trolley side plate numbers’ average precision was 99.2% in fault localization. Utilizing the aforementioned method to construct a system for detecting wheel swing can provide technical support for fault detection of the trolley axle on the sintering machine.