Authors

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.

Abstract in Chinese

农业现代化的快速发展使得农业机械可靠性成为提高我国农机装备发展水平的关键。针对故障模式、影响及危害性分析在谷物收获机可靠性分析中存在的主观性、模糊性、试验成本高以及数据获取困难等问题，本文提出一种基于数据挖掘技术的FMECA分析方法。通过对谷物收获机割台实例数据分析，得出刀片部分故障模式危害性最大。研究结果表明了基于数据挖掘技术的FMECA方法在农业机械可靠性分析的可行性，该分析方法开拓了谷物收获机可靠性分析新思路，为低投入获取收获机可靠性水平提供了可能。