No abstract

Because of the low impurity removal rate and high damage rate of the potato combine harvester during the working process, a new type of impurity removal device for the potato combine harvester was designed. The device was mainly composed of retaining plates, impurity removal rollers, smooth rollers, and a control system. To determine the best working parameters of the device, the rotational speed of the rollers, the distance between two rollers, and the inclination angle of the device were used as the test factors. The impurity removal rate and damage rate were employed as the evaluation indexes. Then, the single factor test and the second rotation orthogonal regression test were performed. The optimum combination after parameter optimization was determined to be as follows: rotational speed of the rollers was 113.4 r/min, the distance between the two rollers was 97.5 mm and the inclination angle of the device was 10.4°. The corresponding impurity removal rate was 95.12%, and the damage rate was 0.54%. The device can better meet the requirements of impurity removal operations.

With the rapid development of agricultural modernization, the reliability of agricultural machinery had become the key to improving the development level of agricultural machinery and equipment in China. Aiming at the problems of subjectivity, fuzziness, high test cost and difficult data acquisition in the Failure Mode, Effects and Criticality Analysis of grain harvester, an FMECA analysis method based on Data Mining Technology was proposed in this paper. In this study, Python 3.7.8 is used to collect and process the fault data of the header of the grain harvester. According to the data analysis, it is concluded that the fault rate of the cutter component is the highest in the whole system. Then the agreed hierarchy of header was analyzed by Analytic Hierarchy Process, and it was concluded that the blade part failure mode was the most hazardous. The results show that the grain harvester should strengthen the inspection and maintenance of the blade of the cutter in the working process. The research results showed the feasibility of the FMECA method based on Data Mining Technology in agricultural machinery reliability analysis, which opens up a new idea of grain combine harvesters’ reliability analysis and provides the possibility to obtain the reliability level of harvesters with low input.