Planetary gear is the key component of the transmission system of electromechanical equipment for energy industry, and it is easy to damage, which affects the reliability and operation efficiency of electromechanical equipment of energy industry. Therefore, it is of great significance to extract the useful fault features and diagnose faults based on raw vibration signals. In this paper, a novel deep feature learning method based on the fused-stacked autoencoders (AEs) for planetary gear fault diagnosis was proposed. First, to improve the data learning ability and the robustness of feature extraction process of AE model, the sparse autoencoder (SAE) and the contractive autoencoder (CAE) were studied, respectively. Then, the quantum ant colony algorithm (QACA) was used to optimize the specific location and key parameters of SAEs and CAEs in deep learning architecture, and multiple SAEs and multiple CAEs were stacked alternately to form a novel deep learning architecture, which gave the deep learning architecture better data learning ability and robustness of feature extraction. The experimental results show that the proposed method can address the raw vibration signals of planetary gear. Compared with other deep learning architectures and shallow learning architecture, the proposed method has better diagnosis performance, and it is an effective method of deep feature learning and fault diagnosis.

In an attempt to react to the increasing imbalance of assembly line due to the high uncertainty of assembly resources in the cloud manufacturing environment, this study investigates the reconfigurable assembly line balancing problem (ALBP) in a cloud manufacturing environment based on the actual production process. We designed the assembly precedence relation model on the basis of analyzing the characteristics and categories of the reconfigurable ALBP. Thereafter, an optimization model of ALBP under traditional mode is established. Combined with the dynamic and collaborative operation of cloud manufacturing, a workstation information sharing framework for cloud manufacturing is designed, and an equilibrium optimization model of ALBP in cloud manufacturing environment is developed to obtain the maximum productivity and the minimum the load smoothness. Moreover, an improved memetic algorithm is proposed to solve the optimization model, which has strong global and local search capabilities compared with the general algorithm. Finally, performance of the proposed approach is tested on a set of examples, and distinguished results can be acquired by comparing with particle swarm optimization algorithm, simulated annealing and genetic algorithm.

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Aiming at the planting characteristics of hemp in southern hilly regions, a two-wheeled walking hemp harvester suitable for harvesting hemp in southern hilly regions is studied and designed. The harvester mainly consists of a header frame, single-moving cutter, cutter mechanical transmission, stalk lifter and reel, stalk divider, stalk horizontal conveyor, wheeled chassis, motor, gearbox, etc. To improve the cutting performance of the two-wheeled walking hemp harvester, response surface tests of three levels are conducted for three factors influencing the operation quality, including the cutting speed, blade length, and forward speed, on the constructed hemp cutting test bench. Moreover, test results are analyzed with the response surface method, and multi-objective optimization is carried out for the regression mathematical model with Design-Expert software. Results show that when the cutting speed is 1.2 m/s, the blade length is 120 mm, forward speed is 0.6 m/s, the cutting efficiency is 38.92 stalks/s, the cutting power is 776.37 W and the failure rate is 6.24%. Trial production of sample machine and field trial are finished according to the optimized parameters and structural design scheme, and the test results reveal that the cutting rate can reach 92.5%, the rate of transmission can reach 86.7%, the productivity is 0.18 hm 2 /h, and all performance indexes can meet the design requirements. This research can provide references for resolving the mechanical harvesting of hemp.

Finite element numerical simulations provide a visual and quantitative approach to studying the interaction between rigid mechanical components and flexible agricultural crops. This method is an important tool for the design of modern agricultural production equipment. Obtaining accurate material model parameters for crops is a prerequisite for ensuring the reliability and accuracy of numerical simulations. To address the issue of unclear mechanical constitutive model parameters for industrial hemp stalks, this study utilized the theory of composite materials to establish a mechanical constitutive relationship model for industrial hemp stalks. Compression, tensile, and bending tests on different components of the stalk were conducted, using a computer-controlled universal testing machine, to obtain their elastic parameters. Combined with the measured basic material parameters and contact parameters of industrial hemp stalks, a finite-element numerical simulation model of industrial hemp stalks was established. By conducting Plackett–Burman and central composite experiments, it was determined that among the six measured parameters, the anisotropic plane Poisson’s ratio of the phloem and the isotropic plane Poisson’s ratio of the xylem have a significant influence on the maximum bending force of the stalk. Parameter optimization was carried out, using the relative error of the maximum bending force as the optimization objective, resulting in an anisotropic plane Poisson’s ratio of 0.054 for the phloem and an isotropic plane Poisson’s ratio of 0.28 for the xylem of industrial hemp stalks. To validate the accuracy and reliability of the optimized parameters, a numerical simulation was conducted and compared with the physical experiments. The simulated value obtained was 405.81 N while the actual measured value was 392.55 N. The error between the simulated and measured values was only 3.4%, confirming the effectiveness of the model. The precise parameters for the mechanical characteristics of industrial hemp stalk material obtained in this study can provide a parameter basis for future research on the numerical simulation of mechanized industrial hemp harvesting and retting.