Surface defects are usually the early phenomenon of rail failure, which threatens the safety of railroad transportation critically, and the timely detection of surface defects helps to eliminate the potential risk of rail and reduce the chance of railroad safety accidents. The existing methods of detecting surface defects on rails suffer from a large performance degradation in the application of rails containing pollutions such as rust and oil. Therefore, this article proposes a multilevel, end-to-end fast rail surface defect detection method. First, rail extraction was performed based on the stability of the standard deviation of the edge pixels. Then, differential box-counting (DBC) and GrabCut algorithm are then combined for defect segmentation to boost the speed and accuracy of extracting complex defects. Finally, YOLO v2 is used to precisely locate and detect defects. The experimental results show that the proposed method performs well, with an average accuracy of 97.11%, an average recall of 96.10%, and an average frame rate of 0.0064 s. In addition, the proposed method offers a high robustness in the tests of different use cases.

As one of the most essential earth-moving equipment, cable shovels significantly influence the efficiency and economy in the open-pit mining industry. The optimal digging trajectory planning for each cycle is the base for achieving effective and energy-saving operation, especially for robotic excavation, in which case, the digging trajectory can be precisely tracked. In this paper, to serve the vision of cable shovel automation, a two-phase multi-objective genetic algorithm was established for optimal digging trajectory planning. To be more specific, the optimization took digging time and energy consumption per payload as objects with the constraints of the limitations of the driving system and geometrical conditions. The WK-55-type cable shovel was applied for the validation of the effectiveness of the multi-objective optimization method for digging trajectories. The digging performance of the WK-55 cable shovel was tested in the Anjialing mining site to establish the constraints. Besides, the digging parameters of the material were selected based on the tested data to make the optimization in line with the condition of the real digging operations. The optimization results for different digging conditions indicate that the digging time decreased from an average of $20\mathrm{s}$ to $10\mathrm{s}$ after the first phase optimization, and the energy consumption per payload reduced by $\mathrm{13.28\%}$ after the second phase optimization, which validated the effectiveness and adaptivity of the optimization algorithm established in this paper.

This study constructs a nonlinear dynamic model of articulated vehicles and a model of hydraulic steering system. The equations of state required for nonlinear vehicle dynamics models, stability analysis models, and corresponding eigenvalue analysis are obtained by constructing Newtonian mechanical equilibrium equations. The objective and subjective causes of the snake oscillation and relevant indicators for evaluating snake instability are analysed using several vehicle state parameters. The influencing factors of vehicle stability and specific action mechanism of the corresponding factors are analysed by combining the eigenvalue method with multiple vehicle state parameters. The centre of mass position and hydraulic system have a more substantial influence on the stability of vehicles than the other parameters. Vehicles can be in a complex state of snaking and deviating. Different eigenvalues have varying effects on different forms of instability. The critical velocity of the linear stability analysis model obtained through the eigenvalue method is relatively lower than the critical velocity of the nonlinear model.

Local failure of rail track commonly grow from surface defects. Hence, timely detection of surface defects helps to identify potential hazards on the track and reduce the occurrence of railroad accidents. Since track surface defects are scattered and diverse, and different service life leads to different types of defects, it is crucial to detect surface defects in real time, with efficiency, reliability and robustness. To this end, a pixel-level defects segmentation method is proposed. In this paper, features are tessellated together at the Channel level to form denser features, allowing additional information on surface defects textures to be propagated among high-resolution layers. Dropout is performed on the weak correlations learned during the convolution, so that the convolution blocks can share a uniform weight matrix, reducing computation redundancy and model complexity. Firstly, the track datasets of different ages are categorized into four sets, then, the samples are normalized to grey scale by pre-processing, and fed into the proposed network for training. An evaluation of the proposed model on defective samples was performed to demonstrate the performance of the method, with an Accuracy of 97.47%, a Loss of 0.0061 and an Average Frame Rate of 0.033s. The results of different networks tested on the same dataset show that the proposed model exhibits strong stability, adaptability and robustness. In addition, the proposed model is assessed on two different datasets with distinct challenges, with Mean Intersection over Union yielding 2.13% and 3.77% boosts respectively.