We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in the time and frequency domains of four bearing failure forms, which addresses the issue of ambiguous fault identification caused by their nonlinearity and nonstationarity. The extracted feature vectors are then divided into training and test sets as SVM inputs for fault diagnosis. To optimize the SVM, we construct a hybrid kernel SVM using a polynomial kernel function and radial basis kernel function. BO is used to optimize the extreme values of the objective function and determine their weight coefficients. We create an objective function for the Gaussian regression process of BO using training and test data as inputs, respectively. The optimized parameters are used to rebuild the SVM, which is then trained for network classification prediction. We tested the proposed diagnostic model using the bearing dataset of the Case Western Reserve University. The verification results show that the fault diagnosis accuracy is improved from 85% to 100% compared with the direct input of vibration signal into the SVM, and the effect is significant. Compared with other diagnostic models, our Bayesian-optimized hybrid kernel SVM model has the highest accuracy. In laboratory verification, we took sixty sets of sample values for each of the four failure forms measured in the experiment, and the verification process was repeated. The experimental results showed that the accuracy of the Bayesian-optimized hybrid kernel SVM reached 100%, and the accuracy of five replicates reached 96.7%. These results demonstrate the feasibility and superiority of our proposed method for fault diagnosis in rolling bearings.

Agriculture is a labor-intensive industry. However, with the demographic shift toward an aging population, agriculture is increasingly confronted with a labor shortage. The technology for autonomous operation of agricultural equipment in large fields can improve productivity and reduce labor intensity, which can help alleviate the impact of population aging on agriculture. Nevertheless, significant challenges persist in the practical application of this technology, particularly concerning adaptability, operational precision, and efficiency. This review seeks to systematically explore the advancements in unmanned agricultural operations, with a focus on onboard environmental sensing, full-coverage path planning, and autonomous operational control technologies. Additionally, this review discusses the challenges and future directions of key technologies for the autonomous operation of agricultural equipment in large fields. This review aspires to serve as a foundational reference for the development of autonomous operation technologies for large-scale agricultural equipment.

As an important part of rotating machinery, bearings play an important role in large-scale mechanical equipment. Abnormal bearing conditions may cause the machine to malfunction, or even evolve into a serious accident. Therefore, the accurate and timely fault diagnosis of the bearing is of great significance. Based on EMD, this paper introduces the working principles and characteristics of EEMD and CEEMDAN, respectively. Then the signal was decomposed by EEMD and CEEMDAN respectively. The simulation results show that CEEMDAN has better effect on signal decomposition. Then, comparing the effect of CEEMDAN and EEMD on bearing fault feature frequency extraction, the experiment proves that CEEMDAN has a better ability to preserve original signal and eliminate noise than EEMD method, and can extract bearing fault feature more accurately and timely.

Accurate path tracking is crucial for greenhouse robots operating in complex environments. However, traditional curve tracking algorithms suffer from low tracking accuracy and large tracking errors. This study aim to develop a high precision greenhouse autonomous following robot, use ANSYS Workbench 19.2 to perform stress and deformation analysis on the robot, then propose a path tracking method based on Linear Quadratic Regulator (LQR) to optimize the pure tracking to ensure high precision curved path tracking for curved tracking, finally perform a comparative simulation analysis in MATLAB R2024a. The structural analysis shows that the maximum equivalent stress is 196 MPa and the maximum deformation is 1.73 mm under a load of 600 kg, which are within the yield limit of 45 steel. Simulation results demonstrate that at a speed of 2 m/s, the conventional Pure Pursuit algorithm incurs a maximum lateral error of 0.3418 m and a heading error of 0.2669 rad under high curvature conditions. By contrast, the LQR–Pure Pursuit algorithm reduces the peak lateral error to 0.0904 m and confines the heading error to approximately 0.0217 rad. Experimental validation yielded an RMSE of 0.018 m for lateral error and 0.016 m for heading error. These findings confirm that the designed robot can sustain its payload under most operating scenarios and that the proposed tracking strategy effectively suppresses deviations and improves path-following accuracy.

This study proposes an attitude stabilization method based on an improved adaptive Kalman filter (AKF). The aim is to address attitude fluctuations and rollover risks in rail-based agricultural transport platforms on hilly terrain caused by slope changes, load shifts and vibrations. A dynamic model integrating the load distribution and center-of-mass migration was established, and an adaptive noise covariance mechanism was used to precisely estimate the roll and pitch angles in real time. A dual-channel proportional–integral–derivative controller was designed for automatic leveling, and a rollover risk index (RRI) was adopted for safety evaluation. Simulations revealed the ability of the improved AKF to decrease the roll estimation (RMSE) from 1.2684° to 0.8670° and the stabilization time from 0.6250 to 0.3830 s for the roll and from 0.6930 to 0.4110 s for the pitch. Under 10–30° slope disturbances, the average RRI decreased from 0.1861 to 0.1506. Field tests further demonstrated decreases in the peak roll and pitch angles from 4.8° and 4.1° to 3.1° and 2.7°, respectively, and a decrease in the average RRI from 0.203 to 0.169. The improvements in estimation accuracy, leveling performance, and operational safety under complex disturbances indicate the strong engineering potential of the proposed method.