Accurate prediction of cutter head torque is of great significance to ensure the safe tunneling of the shield tunneling machine. However, because geological conditions are complex and changeable, frequent outliers and time-varying non-stationary characteristics exist in the data of the cutterhead torque. Existing predictive models fixed their model structures and parameters, which can not catch the above characteristics. Thus, to solve this problem, we propose a predictive model based on the Singular Spectrum Analysis (SSA) and the Autoregressive Integrated Moving Average (ARIMA) model with adaptive orders. First, SSA is utilized to decompose the original data, and the trend quantity of the original data is adopted to represent the original data to reduce the influence of outliers. Second, to catch the time-varying non-stationary characteristics of data, the Augmented Dickey-Fuller (ADF) test and Bayesian Information Criterion (BIC) are adopted to construct the adaptive orders identification algorithm for the ARIMA model. Finally, sufficient results illustrate that the proposed model can accurately predict the data of cutterhead torque in real time under different geological conditions. Specifically, the average values of Coefficient of Determination (R2) obtained by the proposed method in 1-5 steps are 0.842, 0.800, 0.758, 0.706, and 0.654, which outperform existing models.

Environment perception is a crucial technology of the super large type of intelligent electric shovel in open-pit mining for realizing automatic excavation. However, poor field view, variable lighting, and high-density dust at construction sites lead great technological challenges beyond the current human manipulation capability. The article proposes a multimodal semantic segmentation method based on LiDAR-camera fusion to classify each element of the construction site and accurately segment the ore piles. Specifically, the correspondence between point cloud points and pixels through perspective projection is established, and a parallel feature fusion model (PFFM) and a minimum cost function for feature extraction and optimization is built. Subsequently, radial basis function interpolation is used to reconstruct the surfaces of the segmented ore piles. Moreover, the excavation trajectory of electric shovel is optimized based on the reconstructed surface. The average segmentation accuracy and the per-frame segmentation time of PFFM reach 95.72% and 0.193s, respectively. Simulations and experiments show that trajectory planning based on the proposed method can significantly improve the operation performance.

Metal roof sheathings are widely employed in large-span buildings because of their light weight, high strength and corrosion resistance. However, their severe working environment may lead to deformation, leakage and wind-lift, etc. Thus, predicting these damages in advance and taking maintenance measures accordingly has become important to avoid economic losses and personal injuries. Conventionally, the health monitoring of metal roofs mainly relies on manual inspection, which unavoidably compromises the working efficiency and cannot diagnose and predict possible failures in time. Thus, we proposed a novel damage monitoring scheme implemented by laying bend sensors on vital points of metal roofs to precisely monitor the deformation in real time. A fast reconstruction model based on improved Levy-type solution is established to estimate the overall deflection distribution from the measured data. A standing seam metal roof under wind pressure is modeled as an elastic thin plate with a uniform load and symmetrical boundaries. The superposition method and Levy solution are adopted to obtain the analytical model that can converge quickly through simplifying an infinite series. The truncation error of this model is further analyzed. Simulation and experiments are carried out. They show that the proposed model is in reasonable agreement with the experimental results.

Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs. However, pure electric-driven mining excavators are prone to unexpected power outages in mines because of drastic changes in load power, leading to significant fluctuations in the power demand of the grid, which in turn affects production. To solve the above problem, a pure electric-driven mining hydraulic excavator based on electric-motor-driven swing platform and hydraulic pumps was used as the research object. Moreover, supercapacitors and DC/DC converter, as the energy storage system (ESS) adjust the output power of the grid and recover the braking kinetic energy of the swing platform. Subsequently, a novel integrated energy management strategy for a DC bus voltage predictive controller based on the power feedforward of fuzzy rules is proposed to run mining excavators efficiently and reliably. Specifically, the working modes of the ESS are determined by the DC bus voltage and state of charge (SOC) of the supercapacitor. Next, the output power of the supercapacitor and the DC bus voltage were controlled by adjusting the charging and discharging currents of the DC/DC converter using a predictive controller and fuzzy rules. In addition, a digital prototype of the excavator was verified using an original machine test. The performance of the different strategies and driven systems were analyzed using digital prototypes. The results showed that, compared with traditional excavators with diesel engines, the operational cost of the developed excavators was reduced by 54.02%. Compared to pure electric-driven excavators without an ESS, the peak power of the grid for the developed excavators was reduced by 10%. This study designed an integrated energy management strategy for a pure electric mining excavator that can regulate the power output of the grid and maintain the stability of the bus voltage and SOC of the ESS.