Research On Reliability Analysis Of Grain Harvesters Based On Data Mining

In the context of "Made in China 2025" and the Industry 4.0 era,improving the quality and reliability of complex mechanical equipment and intelligent devices has become the main development direction.Grain harvesters as an important category of agricultural machinery,and its development level to some extent represents the development level of the agricultural machinery industry.At present,the reliability research of agricultural machinery and equipment in China was affected by test funds,test conditions and test cycles,and there were problems such as difficulty in obtaining test data,failure severity level cannot be accurately identified,and failure interaction relationship between parts was not considered,etc.The reliability analysis results are low in accuracy and have strong randomness.Therefore,this paper collected historical failure data of grain harvesters with the help of data mining technology,explored new methods of reliability research based on big data,provided a basis for upgrading grain harvesters,and made it possible to obtain the reliability level of harvesters with low inputs.The main research contents and conclusions of this paper were as follows:(1)Based on the field research on agricultural machinery enterprises in Shandong Province,data mining technology was introduced to address the problems such as difficulties in obtaining data for reliable research,and the concept,characteristics,and development status of data mining technology were specifically introduced.On the basis of exploring the application of data mining technology in the field of agricultural machinery,a data mining framework was built for the characteristics of grain harvester reliability data,and a grain harvester reliability analysis database was established by combining research data to provide data support for grain harvester reliability analysis.(2)Build a data analysis model.The mining framework was built with Python program language,and the data analysis model was built with a clustering analysis algorithm,Apriori association rule algorithm and other data mining-related theories.According to the analysis results,the common failure characteristics and failure mechanism of grain harvester were summarized,and the highest failure rate of the grain harvester component,the cutting table,was briefly analyzed to provide a reference for the following grain harvester reliability research.(3)Based on the database of field investigation and data mining,the reliability analysis model of the whole machine was built to solve the problems of subjectivity,fuzziness,the same weight of each fault component and the same hazard of the fault components in the traditional reliability analysis methods.The reliability evaluation index of the grain harvester was determined by the Analytic Network Process,the fault matrix was established,and the ANP model was constructed to calculate the weight of each index.It was substituted into the data envelopment OCD model to calculate the efficiency value of each fault mode,and then the RPN value was calculated and sorted according to the severity of the fault.(4)Feed back the reliability analysis results of the grain harvester to the R&D and production stage of the grain harvester.According to the RPN value of each failure mode,started with the reliability technology and management,put forward the full-cycle reliability analysis scheme,and formed a research closed-loop.