Fastener screws are critical components of rail fasteners. For the fastener screw maintenance robot, an image-based fast fastener screw detection method is urgently needed. In this paper, we propose a light-weight model named FSS-YOLO based on YOLOv5n for rail fastener screw detection. The C3Fast module is presented to replace the C3 module in the backbone and neck to reduce Params and FLOPs. Then, the SIoU loss is introduced to enhance the convergence speed and recognition accuracy. Finally, for the enhancement of the screw detail feature fusion, the shuffle attention (SA) is incorporated into the bottom-up process in the neck part. Experiment results concerning CIoU and DIoU for loss, MobileNetv3 and GhostNet for light-weight improvement, simple attention mechanism (SimAM), and squeeze-and-excitation (SE) attention for the attention module, and YOLO series methods for performance comparison are listed, demonstrating that the proposed FSS-YOLO significantly improves the performance, with higher accuracy and lower computation cost. It is demonstrated that the FSS-YOLO is 7.3% faster than the baseline model in FPS, 17.4% and 19.5% lower in Params and FLOPs, respectively, and the P, mAP@50, Recall, and F1 scores are increased by 10.6% and 6.4, 13.4%, and 12.2%, respectively.

Aiming to extract the weak composite fault characteristics of a rolling bearing under harsh operation conditions, a novel composite fault diagnosis method for bearings based on adaptive circulant singular spectrum analysis (ACiSSA) is proposed. The proposed method is able to adaptively obtain the eigenvalue of a non-stationary vibration signal in any dimension, and effectively reassemble the same frequency components and improve the signal-to-noise ratio (SNR). Specifically, circulant singular spectrum analysis is utilized to decompose the raw signal, and the optimal parameters, i.e. the embedding dimension and threshold value of cumulative contribution, are selected to maximum kurtosis through the grey wolf optimization method. The signal is reconstructed with high SNR according to the effective singular spectrum components. Envelope demodulation analysis is then implemented to extract the characteristic defect frequency in the reconstructed signal. Finally, feature extraction performance is quantitatively evaluated, and experimental results show that the proposed ACiSSA method is able to extract more sensitive features under more noisy conditions compared with other common methods, with higher computational efficiency.

The railway fastener, as a crucial component of railway tracks, directly influences the safety and stability of a railway system. However, in practical operation, fasteners are often in low-light conditions, such as at nighttime or within tunnels, posing significant challenges to defect detection equipment and limiting its effectiveness in real-world scenarios. To address this issue, this study proposes an unsupervised low-light image enhancement algorithm, CES-GAN, which achieves the model’s generalization and adaptability under different environmental conditions. The CES-GAN network architecture adopts a U-Net model with five layers of downsampling and upsampling structures as the generator, incorporating both global and local discriminators to help the generator to preserve image details and textures during the reconstruction process, thus enhancing the realism and intricacy of the enhanced images. The combination of the feature-consistency loss, contrastive learning loss, and illumination loss functions in the generator structure, along with the discriminator loss function in the discriminator structure, collectively promotes the clarity, realism, and illumination consistency of the images, thereby improving the quality and usability of low-light images. Through the CES-GAN algorithm, this study provides reliable visual support for railway construction sites and ensures the stable operation and accurate operation of fastener identification equipment in complex environments.