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When a gearbox transmission system fails, the multi-scale mean permutation entropy （MMPE） of different vibration signals corresponds to the fault state to a certain extent. However, the effect of multi-scale mean permutation entropy extraction fault features depends on the selection of parameters. Therefore, this study proposes a gearbox fault identification method based on the improved multi-scale mean permutation entropy and the parameter optimization support vector machine（SVM）. Firstly, the particle swarm optimization （PSO） is referenced to optimize parameters of multi-scale mean permutation entropy. Secondly, the multi-scale mean permutation entropy of the collected gear vibration signals is calculated.Finally, the particle swarm optimization is used to optimize the support vector machine to identify the fault state of the gear. Experimental analysis results are conducted to validate the effectiveness of this proposed method.

The presence of karst formations significantly impacts the load-bearing capacity of pile foundations in karst geological environments, posing a challenge to their design. This study investigated the bearing characteristics of karst pile foundations using the physical model test and numerical analysis. First, the influence of cave height and span on the bearing capacity of pile foundations is examined using model tests. The results demonstrate that the height of karst caves greatly affects the bearing capacity of karst pile foundations. Subsequently, numerical analysis further explores the bearing characteristics of these foundations. It reveals that as the top load on pile increases, an arch-shaped tensile damage zone forms at the top of karst cave and gradually expands. The rock failure in this area leads to a decrease in adhesion between rock strata and pile foundation, consequently reducing its load-bearing capacity. Finally, experimental results are compared with numerical results to validate consistency and mutual verifiability between physical model tests and numerical analyses. The outcomes of the research provide valuable insights for designing rock-socketed pile foundations in similar karst areas.

[Objective]A key challenge for the harmless treatment center of sick and dead animal is to prevent secondary environmental pollution, especially during the process of transporting the animals from cold storage to intelligent treatment facilities. In order to solve this problem and achieve the intelligent equipment process of transporting sick and dead animal from storage cold storage to harmless treatment equipment in the harmless treatment center, it is necessary to conduct in-depth research on the key technical problems of path planning and autonomous walking of transport robots.[Methods]A * algorithm is mainly adopted for the robot path planning algorithm for indoor environments, but traditional A * algorithms have some problems, such as having many inflection points, poor smoothness, long calculation time, and many traversal nodes. In order to solve these problems, a path planning method for the harmless treatment of diseased and dead animal using transport robots based on the improved A algorithm was constructed, as well as a motion control method based on fuzzy proportional integral differential (PID). The Manhattan distance method was used to replace the heuristic function of the traditional A * algorithm, improving the efficiency of calculating the distance between the starting and ending points in the path planning process. Referring to the actual location of the harmless treatment site for sick and dead animal, vector cross product calculation was performed based on the vector from the starting point to the target point and the vector from the current position to the endpoint target. Additional values were added and dynamic adjustments were implemented, thereby changing the value of the heuristic function. In order to further improve the efficiency of path planning and reduce the search for nodes in the planning process, a method of adding function weights to the heuristic function was studied based on the actual situation on site, to change the weights according to different paths. When the current location node was relatively open, the search efficiency was improved by increasing the weight. When encountering situations such as corners, the weight was reduced to improve the credibility of the path. By improving the heuristic function, a driving path from the starting point to the endpoint was quickly obtained, but the resulting path was not smooth enough. Meanwhile, during the tracking process, the robot needs to accelerate and decelerate frequently to adapt to the path, resulting in energy loss. Therefore, according to the different inflection points and control points of the path, different orders of Bessel functions were introduced to smooth the planning process for the path, in order to achieve practical application results. By analyzing the kinematics of robot, the differential motion method of the track type was clarified. On this basis, a walking control algorithm for the robot based on fuzzy PID control was studied and proposed. Based on the actual operation status of the robot, the fuzzy rule conditions were recorded into a fuzzy control rule table, achieving online identification of the characteristic parameters of the robot and adjusting the angular velocity deviation of robot. When the robot controller received a fuzzy PID control signal, the angular velocity output from the control signal was converted into a motor rotation signal, which changed the motor speed on both sides of the robot to achieve differential control and adjust the steering of the robot.[Results and Discussions]Simulation experiments were conducted using the constructed environmental map obtained, verifying the effectiveness of the path planning method for the harmless treatment of sick and dead animal using the improved A algorithm. The comparative experiments between traditional A * algorithm and improved algorithm were conducted. The experimental results showed that the average traversal nodes of the improved A * algorithm decreased from 3 067 to 1 968, and the average time of the algorithm decreased from 20.34 s to 7.26 s. Through on-site experiments, the effectiveness and reliability of the algorithm were further verified. Different colors were used to identify the planned paths, and optimization comparison experiments were conducted on large angle inflection points, U-shaped inflection points, and continuous inflection points in the paths, verifying the optimization effect of the Bessel function on path smoothness. The experimental results showed that the path optimized by the Bessel function was smoother and more suitable for the walking of robot in practical scenarios. Fuzzy PID path tracking experiment results showed that the loading truck can stay close to the original route during both straight and turning driving, demonstrating the good effect of fuzzy PID on path tracking. Further experiments were conducted on the harmless treatment center to verify the effectiveness and practical application of the improved algorithm. Based on the path planning algorithm, the driving path of robot was quickly planned, and the fuzzy PID control algorithm was combined to accurately output the angular velocity, driving the robot to move. The transport robots quickly realized the planning of the transportation path, and during the driving process, could always be close to the established path, and the deviation error was maintained within a controllable range.[Conclusions]A path planning method for the harmless treatment of sick and dead animal using an transport robots based on an improved A * algorithm combined with a fuzzy PID motion control was proposed in this study. This method could effectively shorten the path planning time, reduce traversal nodes, and improve the efficiency and smoothness of path planning.

Considering the important effect of mineral composition, this article deduces the similarity criterion based on the dimensional method. Similar materials suitable for tuffs with different degrees of weathering in karst areas are made. By virtue of the orthogonal experiment, the mechanical behaviors of the similar materials with various mix proportions are systematically investigated. It provides an important reference and basis for the next proposed model tests on the stability of tuff strata in karst areas. The results indicate that a stable mechanical behavior can be achieved for the similar material made of quartz sand, cement, gypsum, limestone powder, diatomite, red clay, and water. Moreover, it is also found that the aggregate and the binder have an observable influence on the mechanical behavior of the similar material. With an increase in the amount of cement and gypsum, the homogeneity of the sample increases, failure mode in which the specimen shows lateral compression damage. However, with increasing the amount of quartz sand, there is a tendency that a weak structural plane will be generated within the sample. Thus, this similar material can be satisfactorily used for simulating limestone of different weathering degrees in a scaled model test.

When a gearbox transmission system fails, the multi-scale mean permutation entropy (MMPE) of different vibration signals corresponds to the fault state to a certain extent. However, the effect of multi-scale mean permutation entropy extraction fault features depends on the selection of parameters. Therefore, this paper proposes a gearbox fault identification method based on improved multi-scale mean permutation entropy and parameter optimization support vector machine. The particle swarm optimization (PSO) is referenced to optimize parameters of multi-scale mean permutation entropy; the multi-scale mean permutation entropy of the collected gear vibration signals wis calculated; the particle swarm optimization is used to optimize the support vector machine to identify the fault state of the gear. Experimental analysis results are conducted to validate the effectiveness of this proposed.

Environmental quality of facilities affects the healthy growth and production of laying hens. The breeding environment of laying hens is a complex and non-linear system in which multiple environmental factors interact and restrict each other. It is difficult to make an accurate and effective evaluation on the suitability of laying hens with a single breeding environment parameter. In order to solve the above problem, an improved cuckoo search algorithm optimization neural network (CS-BP) model for the evaluation and prediction of the environmental suitability of laying hen facility was proposed in this research. In this model, the effects of environmental factors such as temperature, humidity, light intensity and ammonia concentration were comprehensively analyzed, and the problem of high prediction accuracy caused by BP neural network easily falling into local minimum value was overcome. In the experiment, the model was compared with BP neural network, genetic algorithm optimized BP neural network (GA-BP) and particle swarm optimization BP neural network (PSO-BP). The results showed that the mean absolute error (MAE), mean relative error (MAPE) and the coefficient of determination (R2) of the prediction model based on the improved CS-BP were 0.0865, 0.0159 and 0.8569, respectively. The prediction model based on the improved CS-BP had a strong generalization ability and a high testing precision, and its index performance was better than the above three comparison models. The classification accuracy of the improved CS-BP model was tested, and the result was 0.9333. The model constructed in this research can provide more comprehensive and effective scientific evaluation for the environmental quality of laying hens facility, which is of great significance to realize the optimal control of the production environment and promote the production performance of layers.