Fault Diagnosis of Train Clamp Based on Faster R-CNN and One-class Convolutional Neural Network

Zhang, Zonghong; Ma, Junjie; Huang, Deqing; Zhou, Zhonghe; Wan, Zipeng; Qin, Na

doi:10.1109/iciea51954.2021.9516167

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A Fault Detection Of Freight Train Images1

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2023

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“…The detection accuracy reached 88.72% and had good robustness to complex noise environments. Zhang et al [10] proposed a fault detection method to detect rod springs of fixtures with an experimental accuracy of 91.98%. Zhou et al [11] proposed a detection algorithm for height valve faults, which could detect faults with an accuracy of 97%.…”

Section: A Fault Detection Of Freight Train Imagesmentioning

confidence: 99%

Visual Fault Detection of Multiscale Key Components in Freight Trains

Zhang

Zhou

Pan

et al. 2023

IEEE Trans. Ind. Inf.

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Fault detection for key components in the braking system of freight trains is critical for ensuring railway transportation safety. Despite the frequently employed methods based on deep learning, these fault detectors are extremely reliant on hardware resources and complex to implement. In addition, no train fault detectors consider the drop in accuracy induced by scale variation of fault parts. This paper proposes a lightweight anchor-free framework to solve the above problems. Specifically, to reduce the amount of computation and model size, we introduce a lightweight backbone and adopt an anchor-free method for localization and regression. To improve detection accuracy for multi-scale parts, we design a feature pyramid network to generate rectangular layers of different sizes to map parts with similar aspect ratios. Experiments on four fault datasets show that our framework achieves 98.44% accuracy while the model size is only 22.5 MB, outperforming state-of-the-art detectors.

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Section: A Fault Detection Of Freight Train Imagesmentioning

confidence: 99%