基于数据挖掘技术的谷物收获机割台可靠性分析

Abstract

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.