Wang Haoguang, Du Wangzhe, Xu Guanhua, Sun Yangfan, Shen Hongyao
The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
Sci Rep. 2024 May 9;14(1):10666. doi: 10.1038/s41598-024-61396-6.
The railway rivet is one of the most important and easily damaged parts of the connection. If rivets develop cracks during the production process, their load-bearing capacity will be reduced, thereby increasing the risk of failure. Fluorescent magnetic particle flaw detection (FMPFD) is a widely used inspection method for train fasteners. Manual inspection is not only time-consuming but also prone to miss detection, therefore intelligent detection system has important application value. However, the fluorescent crack images obtained by FMPFD present challenges for intelligent detection, such as the dense, multi-scaled and uninstantiated cracks. In addition, there is limited research on fluorescent rivet crack detection. This paper adopts instance segmentation to achieve automatic cracks detection of rivets. A decentralized target center and low overlap rate labeling method is proposed, and a Gaussian-weighted correction post-processing method is introduced to improve the recall rate in the areas of dense cracks. An efficient channel spatial attention mechanism for feature extraction is proposed in order to enhance the detection of multi-scale cracks. For uninstantiated cracks, an improvement of crack detection in uninstantiated regions based on multi task feature learning is proposed, thoroughly utilizing the semantic and spatial features of the fluorescent cracks. The experimental results show that the improved methods are better than the baseline and some cutting-edge algorithms, achieving a recall rate and mAP of 86.4% and 90.3%. In addition, a single coil non-contact train rivet composite magnetization device is built for rivets that can magnetize different shapes of rivets and has universality.
铁路铆钉是连接件中最重要且最易损坏的部件之一。如果铆钉在生产过程中出现裂纹，其承载能力将会降低，从而增加失效风险。荧光磁粉探伤（FMPFD）是一种广泛应用于列车紧固件的检测方法。人工检测不仅耗时，而且容易漏检，因此智能检测系统具有重要的应用价值。然而，FMPFD获取的荧光裂纹图像给智能检测带来了挑战，例如裂纹密集、多尺度且不完整。此外，关于荧光铆钉裂纹检测的研究有限。本文采用实例分割实现铆钉裂纹的自动检测。提出了一种分散目标中心和低重叠率标注方法，并引入高斯加权校正后处理方法以提高密集裂纹区域的召回率。提出了一种高效的通道空间注意力机制用于特征提取，以增强对多尺度裂纹的检测。对于不完整裂纹，提出了一种基于多任务特征学习的不完整区域裂纹检测改进方法，充分利用荧光裂纹的语义和空间特征。实验结果表明，改进后的方法优于基线方法和一些前沿算法，召回率和平均精度均值分别达到86.4%和90.3%。此外，针对铆钉构建了一种单线圈非接触式列车铆钉复合磁化装置，该装置能够对不同形状的铆钉进行磁化，具有通用性。
