Hao Yin

Inadequate bearing lubrication poses a severe threat to the operational safety of rotating machinery. To tackle the diagnostic challenges arising from weak early-stage failure signals, insufficient mechanism interpretability, and the scarcity of field samples, this paper proposes a diagnostic framework integrating Multi-scale Spectral Morphological (MSM) features with adaptive ensemble learning. First, based on the elastohydrodynamic lubrication mechanism and vibration signal modulation characteristics, a multi-scale spectral morphological feature system is constructed. By mapping macro-level energy evolution, micro-level statistical properties, and spectral morphological structures, this system enhances the stability and interpretability of weak signals across evolving failure stages. Furthermore, to address the instability of decision boundaries in single classifiers under class imbalance and complex noise environments, an ADASYN-Stacking ensemble diagnostic model is developed. This model reshapes minority class decision boundaries via Adaptive Synthetic Sampling (ADASYN) and fuses heterogeneous classifiers through a Stacking strategy. Experimental results demonstrate that the proposed method exhibits superior performance on testbed data during the fault latency period. Notably, it maintains a diagnostic accuracy of 97.53% on real industrial field data containing multi-source interference, validating its robustness in early lubrication failure identification and its generalization capability under complex operating conditions.

Precision and non-damaging harvesting is a key direction for the development of mechanized fruit harvesting technologies. Blueberries, with their soft texture and delicate skin, present significant challenges for achieving precise and non-damaging mechanical harvesting. This paper proposes an intelligent recognition and prediction method based on machine vision. The method uses image recognition technology to extract the physical characteristics of blueberries, such as diameter and thickness, and estimates fruit hardness in real-time through a predictive model. The gripping force of the mechanical claw is dynamically adjusted to ensure non-destructive harvesting. Firstly, a chimpanzee optimization algorithm (ChOA) was used to optimize a prediction model that established a mapping relationship between fruit diameter, thickness, weight, and fruit hardness. The radial basis network optimized by the chimpanzee optimization algorithm (ChOA-RBF) model was compared with a non-optimized model, and the results showed that the ChOA-RBF prediction model has significant advantages in predicting fruit hardness. Next, an orthogonal experiment further verified the model, showing that the prediction error between the model’s values and actual values was less than 5%. Additionally, considering practical applications, a simple and efficient two-parameter method was proposed, removing the weight parameter and predicting fruit hardness using only diameter and thickness. Although the two-parameter method increases the prediction error by 0.36% compared to the three-parameter method, it reduces the number of convergence steps by 71 and shortens the computation time by one-third, significantly improving iteration speed. Finally, further crushing experiments showed that using the two-parameter method for hardness prediction through parameter extraction via visual recognition resulted in a relative error of less than 8%, with an average relative error of 3.91%. The error falls within the acceptable range for the safety factor design. This method provides a novel solution for the non-damaging mechanized harvesting of soft fruits.

The preload has a significant impact on the output performance of ultrasonic motors. To achieve optimal output from the ultrasonic motor, this paper proposes a novel multi-objective optimization method for preload, utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the problem of determining the optimal preload. Firstly, a platform for preload adjustment and testing is established to conduct experimental tests on the motor’s characteristics. Based on the experimental data, the influence of preload on the motor’s output performance is analyzed. Secondly, linear regression models are established for three performance indicators: no-load speed, stall torque, and output efficiency with respect to preload, and the objective functions are fitted accordingly. Finally, the NSGA-II algorithm is used for multi-objective optimization with three objectives to obtain the Pareto front and determine the optimal preload for the ultrasonic motor. Simulation and experimental results show that after preload optimization, the no-load speed increased by 4.93%, stall torque increased by 26.10%, and output efficiency increased by 11.28%. Compared to existing optimization methods, this approach has lower computational complexity and better optimization performance, ensuring higher optimization accuracy and precision. The preload optimization method based on NSGA-II can improve the performance of ultrasonic motors in practical applications.

Steer-by-wire (SBW) is a crucial chassis electronic control system. Accurate and rapid wheel angle control is key to enhancing system performance. This paper first establishes a dynamics model of the steering actuator. Then, a co-simulation vehicle model with CarSim and Simulink is built, and the feasibility of the SBW model is experimentally verified. Based on this, a front wheel angle compensation difference tracking strategy is proposed using the front wheel steering angle control method. This strategy compensates for the difference between the actual and ideal front wheel steering angles to achieve the desired front wheel angle progressively. A dual-layer controller is designed in the Simulink module and integrated with the CarSim module to achieve closed-loop feedback control of the front wheel angle. The upper layer controller employs a fuzzy PID control method to execute the front wheel angle compensation strategy, enabling two corrections of the front wheel angle and yaw rate. The lower layer controller corrects wheel angle information to improve control effects. Simulation results under double lane change and single lane change conditions show that the fuzzy PID controller with front wheel angle compensation significantly enhances vehicle steering stability. Notably, at high speeds, the peak yaw rate is lower, convergence speed is faster, and response time is shorter.

In China, most blueberry varieties are characterized by tightly clustered fruits, which pose challenges for achieving precise and non-destructive automated harvesting. This complexity limits the design of robots for this task. Therefore, this paper proposes adding a jetting step during harvesting to separate fruit clusters and increase the operational space for mechanical claws. First, a combined approach of flow field analysis and pressure-sensitive experiments was employed to establish design criteria for the number, diameter, and inclination angle parameters of two types of nozzles: flat tip and round tip. Furthermore, fruit was introduced, and a fluid–structure coupling method was employed to calculate the deformation of fruit stems. Simultaneously, a mechanical analysis was conducted to quantify the relationship between jet characteristics and separation gaps. Simulation and pressure-sensitive experiments show that as the number of holes increases and their diameter decreases, the nozzle’s convergence becomes stronger. The greater the inclination angle of the circular nozzle holes, the more the gas diverges. The analysis of the output characteristics of the working section indicates that the 8-hole 40° round nozzle is the optimal solution. At an air compressor working pressure of 0.5 MPa, force analysis and simulation results both show that it can increase the picking space for the mechanical claw by about 5–7 mm without damaging the blueberries in the jet area. The final field experiments show that the mean distance for Type I (mature fruit) is 5.41 mm, for Type II (red fruit) is 6.42 mm, and for Type III (green fruit) is 5.43 mm. The short and curved stems of the green fruit are less effective, but the minimum distance of 4.71 mm is greater than the claw wall thickness, meeting the design requirements.

Due to their advantages of good stability, adaptability, and flexibility, multi-legged robots are increasingly important in fields such as rescue, military, and healthcare. This study focuses on the millipede, a multi-segmented organism, and designs a novel multi-segment biomimetic robot based on an in-depth investigation of the millipede’s biological characteristics and locomotion mechanisms. Key leg joints of millipede locomotion are targeted, and a mathematical model of the biomimetic robot’s leg joint structure is established for kinematic analysis. Furthermore, a central pattern generator (CPG) control strategy is studied for multi-jointed biomimetic millipede robots. Inspired by the millipede’s neural system, a simplified single-loop CPG network model is constructed, reducing the number of oscillators from 48 to 16. Experimental trials are conducted using a prototype to test walking in a wave-like gait, walking with a leg removed, and walking on complex terrain. The results demonstrate that under CPG waveform input conditions, the robot can walk stably, and the impact of a leg failure on overall locomotion is acceptable, with minimal speed loss observed when walking on complex terrain. The research on the structure and motion control algorithms of multi-jointed biomimetic robots lays a technical foundation, expanding their potential applications in exploring unknown environments, rescue missions, agriculture, and other fields.

Highlights The relationship between fruit size and gripping force was found by BP algorithm. The error between the optimized holding force and the optimal value is not more than 8%, that is, the fruit integrity rate is greater than 92%. The simplified two-parameter method is more suitable for efficient field operation. Abstract. The flourishing development of the blueberry industry has urgently demanded mechanized harvesting, and the rational design of the clamping force of mechanical claws is a challenging aspect for achieving automatic and flexible harvesting. This article proposes a method to predict fruit hardness based on blueberry shape parameters, enabling dynamic adjustment of clamping force. Fruit diameter, height, weight, and rupture pressure data were collected, and using the classical BP neural network algorithm, a mapping relationship between fruit hardness and shape parameters was established. Experimental verification was conducted using an orthogonal table, and the results indicate that the predicted blueberry fruit hardness using this method deviates by less than 8% from the actual values. Further simplification into a two-parameter method was performed to enhance practical field operation efficiency. Although the error slightly increased, it still met the requirements of actual field operations.

The contact interface variables are difficult to measure for an ultrasonic motor. When the ultrasonic motor works under different preloads, the error between the traditional efficiency model and the real output is quite large. In order to solve these two problems, we propose a novel efficiency model. It takes measured preload and the feedback voltage data as the input, which may offer better accuracy and on-line ability. Firstly, the effect of the preload on the drive characteristics is investigated, and the relationship between preload and the change in motor energy input is analyzed. Secondly, a contact model based on measured preload and feedback voltage is built, providing a more accurate description of the contact variables. Finally, an efficiency model was developed with a new composite stator structure. A preload test rig for a 60 mm ultrasonic motor is built and real operating conditions are measured. The results show that the correlation coefficient of the present model is 0.991, larger than 0.925 of the conventional model. The proposed model is more consistent with the real working conditions for the motor.