Lei Hengda, Cao Li, Li Xiuhua
School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China.
Wuhan Huamu Information Technology Co., Ltd., Wuhan 430070, China.
Sensors (Basel). 2023 Aug 22;23(17):7311. doi: 10.3390/s23177311.
The state of angle cocks determines the air connectivity of freight trains, and detecting their state is helpful to improve the safety of the running trains. Although the current research for fault detection of angle cocks has achieved high accuracy, it only focuses on the detection of the closed state and non-closed state and treats them as normal and abnormal states, respectively. Since the non-closed state includes the fully open state and the misalignment state, while the latter may lead to brake abnormally, it is very necessary to further detect the misalignment state from the non-closed state. In this paper, we propose a coarse-to-fine localization method to achieve this goal. Firstly, the localization result of an angle cock is obtained by using the YOLOv4 model. Following that, the SVM model combined with the HOG feature of the localization result of an angle cock is used to further obtain its handle localization result. After that, the HOG feature of the sub-image only containing the handle localization result continues to be used in the SVM model to detect whether the angle cock is in the non-closed state or not. When the angle cock is in the non-closed state, its handle curve is fitted by binarization and window search, and the tilt angle of the handle is calculated by the minimum bounding rectangle. Finally, the misalignment state is detected when the tilt angle of the handle is less than the threshold. The effectiveness and robustness of the proposed method are verified by extensive experiments, and the accuracy of misalignment state detection for angle cocks reaches 96.49%.
折角塞门的状态决定了货物列车的空气连通性，检测其状态有助于提高列车运行的安全性。虽然目前折角塞门故障检测的研究已取得较高精度，但仅关注折角塞门的关闭状态与非关闭状态检测，并分别将它们视为正常与异常状态。由于非关闭状态包括完全打开状态和错位状态，而后者可能导致制动异常，因此从非关闭状态中进一步检测错位状态非常必要。本文提出一种由粗到精的定位方法来实现这一目标。首先，利用YOLOv4模型获得折角塞门的定位结果。接着，将结合折角塞门定位结果的HOG特征的支持向量机（SVM）模型用于进一步获得其手柄的定位结果。之后，仅包含手柄定位结果的子图像的HOG特征继续用于支持向量机模型中，以检测折角塞门是否处于非关闭状态。当折角塞门处于非关闭状态时，通过二值化和窗口搜索对手柄曲线进行拟合，并通过最小外接矩形计算手柄的倾斜角度。最后，当手柄倾斜角度小于阈值时，检测到错位状态。大量实验验证了所提方法的有效性和鲁棒性，折角塞门错位状态检测的准确率达到96.49%。
