To improve the driving comfort of combine harvesters, driver seat low-frequency vibration and related driver ride-comfort problems were investigated on a Chinese CFFL-850 crawler-type full-feed combine harvester based on ISO2631. Driver vibration and driving seat transmission characteristics were measured under the following conditions: no-load idling, driving on the road, driving in the field, and simulated harvesting. The root mean square values composite vibration under four conditions were 3.63 m/s 2 , 2.35 m/s 2 , 3.34 m/s 2 , and 2.67 m/s 2 , respectively. For the same condition, the maximum root mean square scores of vibration component on driver whole-body occurred in the seat support surface (test point 1) and vertical direction (Z direction), which were 3.56 m/s 2 , 2.05 m/s 2 , 3.15 m/s 2 , and 2.43 m/s 2 , respectively. The test point 2 to test point 1 vertical-transfer function curve trends were nearly identical. Nearly all of the transfer coefficients were greater than 1 in the range of 1-50 Hz, therefore, the seat vibration attenuation performance was poor. Based on the analysis results, the driver seat structure was altered and a verification test was performed. The test results indicated that after an X-damping mechanism was installed, vibration acceleration, on the surface of the seat support under the road-driving conditions, decreased from 2.35 m/s 2 to 1.68 m/s 2. Under the simulated harvesting condition, the vibration acceleration decreased from 2.56 m/s 2 to 1.46 m/s 2. Nearly all of the seat vertical transfer coefficients were less than 1 within the frequency range of 1-80 Hz, therefore the dynamic comfort of the seat was ameliorated after structural improvement.
One of the most important means of improving the mechanization of rapeseed harvests and increasing farmers’ income is to reduce the cleaning loss of rapeseed. In this study, a fuzzy grey control system was developed using an assembled cleaning loss sensor. Based on experimental data, the relationship between the cleaning loss and the opening of the louver sieve in the cleaning device was obtained. The fuzzy control scheme was established by combining grey prediction and the fuzzy control principle. Secondly, a microcontroller unit (MCU) was used as the controller, and the opening of the louver sieve was automatically regulated by detecting the signal of the cleaning loss. Finally, the performance and robustness of the control system was evaluated in field tests. Different experiments were conducted under different speed conditions to reflect the variable throughput. Results showed that using the grey prediction control system can realize the adjustment of the louver sieve opening in real time. The cleaning loss could be maintained within the ideal setpoint interval, compared with the operation with the control system switched off. These findings indicate that the application of the grey fuzzy control system reduces cleaning loss, and the nonlinear, time-variable and time delay problems in cleaning devices can be solved effectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.