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.

Hydrogen is the most promising alternative fuel in the field of engines. Exhaust heat-assisted methanol dissociation is an attractive approach for generating hydrogen. In this work, simulations are conducted on a compression ignition engine fueled with different proportions of diesel-dissociated methanol gas (DMG) blends at intermediate engine speed, full load, and 0% EGR ratio. The results reveal that the indicated thermal efficiency and indicated mean effective pressure are greatly enhanced, combustion efficiency is increased, and regular emissions of CO, HC, and soot are reduced, while NOx emissions are reduced with increased DMG substitution. In addition, a simulation is conducted at an intermediate engine speed, full load, 15% DMG substitution ratio, and varying EGR ratios of 0–20%. The results indicate that the dual-fuel engine outperforms the original engine with respect to power, fuel economy, and regular emissions, once an optimal EGR rate is adopted.

The purpose of this work is to provide support for the design of cushioning packaging materials for products with unbalanced mass distributions. A mathematical model of the cushioning packaging system is established and the fourth-order Runge–Kutta method is used to compare the vibration response of products with balanced and unbalanced mass. Finite element simulations are conducted to demonstrate the feasibility of the proposed method of optimizing the design of cushioning packaging materials for products with unbalanced mass distributions using various damping materials. The simulation results are verified by experiments. The results show that the magnitude of the mass offset has a large effect on the acceleration of damped cushioning packaging systems for products with unbalanced mass. The vibration response can be corrected by appropriately increasing the damping coefficient at the mass offset end, thus providing an effective method of optimizing the design of cushioning packaging for products with unbalanced mass.

Rolling bearings are essential parts for manufacturing machines. Vast quantities of features are often extracted from measured signals to comprehensively reflect the conditions of bearings, which may cause high dimensionality, information redundancy, and time consumption. In addition, it is extremely difficult, expensive, and time-consuming to collect samples with label information during the bearing fault diagnosis in real-world scenarios. In this study, a novel bearing defect diagnosis method for small sample size is proposed based on modified local joint sparse marginal embedding (MLJSME) and Wasserstein generative adversarial networks (WGANs). MLJSME can effectively extract intrinsic sparse discriminant features of high-dimensional dataset by preserving both global and local structures. Graph embedding and Gaussian kernel function are adopted to preserve the locality structure of dataset. The global structure and discriminate information are preserved by maximum margin criterion which can also avoid small sample-size problem. Moreover, joint sparsity is applied to preserve the sparse property and improve the robustness to noise and outliers. An abundance of artificial samples can be obtained with WGAN and a few labeled samples. Firstly, a high-dimensional feature dataset consisting of time-domain and frequency-domain features is extracted from original vibration signals, then MLJSME is utilized to extract sensitive low-dimensional features, and a small number of low-dimensional features are fed into WGAN to generate a large number of artificial samples that used to train the classifier, and the bearing fault types can be finally identified. The effectiveness and feasibility of the proposed method is validated by analyzing the different experimental cases.