Proper design decoupling in the context of Axiomatic Design (AD) is essential to transforming customer needs to functional requirements, design parameters, and process variables using matrices. It is common that design decoupling is performed by adopting methods that are philosophically and methodologically different from AD, such as TRIZ. However, transformation of coupling problems following the approach lacks a uniform description for design coupling and also a systematic methodology for decoupling. This article presents a novel method that allows problems involving design coupling to be described by their attributes using the basic concepts of Extenics. The resulted descriptions are well-defined in the basic-element form with a set of uniform characteristics, enabling the decoupling process to be general, structured and effective in search for the knowledge needed for solving the coupling problem. Three decoupled models each exploring AD and Extenics for following a particular decoupling direction of AD are considered. A comprehensive analysis along with a few illustrative examples is considered to illustrate the validity of the decoupling method which is a synergy of AD with Extenics.

Aiming at the need to prevent agricultural machinery from colliding with obstacles in the operation of unmanned agricultural machinery, an obstacle detection algorithm using 2D lidar was proposed, and a pre-collision system was designed using this algorithm, which was tested on a harvester. The method uses the differences between lidar data frames to calculate the collision times between the farm machinery and the obstacles. The algorithm consists of the following steps: pre-processing to determine the region of interest, median filtering, and DBSCAN (density-based spatial clustering of applications with noise) to identify the obstacle and calculate of the collision time according to the 6σ principle. Based on this algorithm, a pre-collision system was developed and integrated with agricultural machinery navigation software. The harvester was refitted electronically, and the system was tested on a harvester. The results showed that the system had an average accuracy rate of 96.67% and an average recall rate of 97.14% for being able to stop safely for obstacles in the area of interest, with a summed average of 97% for both the accuracy and recall rates. The system can be used for an emergency stop when encountering obstacles in the automatic driving of agricultural machinery and provides a basis for the unmanned driving of agricultural machinery in more complex scenarios.

In field harvesting conditions, the non-stationary random vibration characteristics of the harvester are rarely considered, and the results of vibration frequency calculated by different time–frequency transformation methods are different. In this paper, the harvester’s vibration characteristics under the time-varying mass were studied, and the correlation between vibration frequency and modal frequency was analyzed. Firstly, under the conditions of time-varying mass (field harvesting conditions) and non-time-varying mass (empty running condition), the non-stationarity characteristics of vibration signals at 16 measurement points of a combined corn harvester frame were studied. Then, fast Fourier transform (FFT), short-time Fourier transform (STFT), and continuous wavelet transform (CWT) were used to calculate the vibration frequency distribution characteristics of the corn harvester. Finally, based on the EFDD (enhanced frequency domain decomposition) algorithm, the correlation between the primary vibration frequency and the operating mode frequency is studied. The results show that the mean, variance, and maximum difference of the vibration amplitude under harvesting conditions (mass time-varying system) are 0.10, 26.5, and 1.0, respectively, at different harvesting periods (0~10 s, 10~20 s, 20~30 s). The harvesting conditions’ vibration signals conform to the characteristics of non-stationary randomness. The FFT algorithm is used to obtain more dense vibration frequencies, while the frequencies based on STFT and CWT algorithms are sparse. The correlation between the FFT method and the EFDD algorithm is 0.98, and the correlation between the STFT, CWT, and the EFDD algorithm is 0.99 and 0.98. Therefore, the primary frequency of the STFT methods is closer to the modal frequency. Our research laid the foundation for further study and application of mass time-varying combined harvester system non-stationary random vibration modal frequency identification and vibration control.

As off-road vehicles, in addition to field transportation, another vital function of agricultural tractors is to provide power for field machinery. Therefore, the dynamic performance of the power take-off (PTO) driveline directly affects the field reliability of tractors. Firstly, a torsional vibration coupled spatial dynamics model of the power take-off driveline is proposed according to the classical machine driveline dynamics and gear dynamics theory. In the dynamics model, the interactions among the vertical, lateral, and rotational motions of the driveline parts are fully included. The coupling vibrations from internal excitations (such as tooth surface friction, gear time-varying mesh, and engine pulse) and external excitations (such as field machinery load) are also considered. Secondly, the simulation results of the model are obtained using the numerical solving algorithm ode15s. The actual experiment is carried out on the indoor Tractor PTO Test Bench. Then, the model is verified by comparing the test results with the simulation results. Finally, the dynamic characteristics of the whole driveline are revealed under different drive modes, especially strong interactions between the driveline and field machinery in low-speed and heavy-load mode. The gear mesh forces and the root mean square (RMS) values of the acceleration amplitude for the main parts generally decrease gradually with the increase in the PTO rotation speed and the decrease in PTO torque. Furthermore, the model can be applied to reliability assessment, for instance, vibration, damage, and fatigue of off-road vehicles considering gear transmissions, particularly in a field working environment.