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.

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.

With the development and upgrading of intelligent mines, research on the unmanned walking of intelligent electric shovels (ES) has been carried out to improve the moving efficiency of extra-large excavators. This paper first introduces an electric shovel’s primary moving condition in an open-pit mine. According to the moving characteristics of the heavy-duty crawler, the artificial potential field (APF) algorithm is improved to plan the moving trajectory of the electric shovel and carry out simulation verification. A dynamic model of an electric shovel is established. A fuzzy control tracking method is proposed based on preview displacement and centroid displacement deviation. The robustness of the tracking algorithm is verified by multi-condition simulation. Finally, the electric shovel prototype is tested through path planning and tracking experiments. The experimental results show that the improved artificial potential field algorithm can plan an obstacle-free path that satisfies the movement of an electric shovel, and the electric shovel can quickly track the preset trajectory. The maximum deviation of the track tracking center of mass is no more than 10 cm, and the deviation of the heading angle when the shovel reaches the endpoint is within 2°.

To address the challenges arising from external load uncertainties and the limitations of hydraulic system modeling accuracy, this article proposes a novel cascaded nonlinear integral sliding mode control (CNISMC) scheme, enhanced by an improved extended state observer (ESO). The proposed solution, in contrast to conventional sliding mode control (SMC) approaches that are susceptible to chattering, incorporates a novel convergence law and a modified sliding surface, thereby substantially mitigating chattering effects and enhancing transient performance. Furthermore, by implementing a cascaded control structure, the hydraulic system is segmented into two subsystems, each aligned with specific disturbance sources and integrated with an advanced ESO for accurate disturbance compensation. The proposed scheme is validated through a series of simulations and experimental studies, showing a significant improvement in control performance compared to conventional controllers.