The permanent magnet synchronous motor (PMSM) is a key power component in agricultural machinery. The harsh and variable working environments encountered during the operation of agricultural machinery pose significant challenges to the safe operation of PMSMs. Early diagnosis of inter-turn short-circuit (ITSC) faults is crucial for improving the safety of the motor. In this study, a fault diagnosis method based on an improved convolutional neural network (CNN) architecture is proposed, featuring two main contributions. First, a dilated convolutional neural network is combined with residual structures, multi-scale structures, and channel attention mechanisms to enhance the training efficiency of the model and the quality of feature extraction. Second, Bayesian optimization algorithms are applied for the automatic tuning of architecture hyperparameters in deep learning models, achieving automatic optimization of the hyperparameters for the fault diagnosis model of ITSCs. To validate the effectiveness of the proposed algorithm, 17 simulated tests of ITSC fault severities were conducted under both constant conditions and dynamic conditions. The results show that the proposed model achieves the best performance regarding the validation accuracy (98.2%), standard deviation, F1 scores, and feature learning capability compared to four other models with different architectures, demonstrating the effectiveness and superiority of the algorithm.

Investigating the seeding mechanism of precision seeders is of great significance for improving the quality of cotton sowing operations. This paper designs a rotary type-hole cotton precision mulching dibbler. The main factors influencing the entry of cotton seeds into the seed wheel holes during the seeding process are then theoretically analyzed. Following this, an accurate discrete element model of coated cotton seeds is established and combined with a discrete element method (DEM) and multi-body dynamics (MBD)-coupled simulation model of the seed drill for seed picking and planting. Simulation experiments on the seeding performance of the precision dibbler were performed to study the influence of the seed wheel structure and motion parameters on the picking and planting performance under different speeds. The optimal parameter combination for the seed wheel is obtained through optimization experiments, and a precision dibbler is manufactured for bench testing. The bench test results are consistent with the simulation test results. At the precision dibbler rotation speed of 16 r/min, the qualified index reaches a maximum value of 93.28%, the skip sowing index increases with the precision dibbler rotation speed, and the re-sowing index decreases as the speed increases. These optimization results significantly improved seeding precision and efficiency and are of great significance for the reliability and effectiveness of cotton sowing operations.

National Undergraduate Engineering Training Integration Ability Competition is essential in Chinese engineering colleges. Among these items, the carbon-free car competition is popular among college students because of its flexible track, challenging design tasks, and fair judging criteria. In this study, to design a reasonable carbon-free car structure according to the track given by the competition, a reverse analysis method was proposed for the double 8-shaped carbon-free car trajectory based on the space cam mechanism. First, the carbon-free car’s structure was designed, and its stress state was analyzed, with the results indicating that the driving force should be reduced as much as possible under the premise of satisfying the starting requirements to increase the travel distance of the carbon-free car with no slipping. Then, the function between the car track and the front wheel swing angle was established, and the displacement law of the push rod of the cam mechanism was calculated through the swing angle of the front wheel. Finally, the cam profile was got based on the operating law of the push rod. Research has shown that compared with the traditional forward design, this method was more accurate with strong feasibility and operability, which provided good technical support for designing the subsequent carbon-free car competition.