Reliability Analysis And Evaluation Method Research On Combine Harvester Chassis

In the process of small to large size and simple to complex model, the reliability of Modern Agricultural Equipment gradually becomes the focus of attention. However, in the domestic, the application of reliability technology in agricultural machinery products is still in a fledging period, and traditional design method is most adopted when agricultural machinery products is studied. In consequence, the reliability of agricultural machinery products is generally low in our country, and unable to meet the demands of users. In this paper, with the purpose of improving the reliability and prolonging the service life of agricultural machinery products, research is carried out from components reliability analysis to systems reliability analysis and from single index reliability evaluation to multi-index comprehensive evaluation. The main researches in this paper are presented as follows:(1) Aiming at the condition of limit state equation unknown, research on the structural reliability analysis method is carried out. From the perspective of forward reliability analysis, a response surface method based on all sample point interpolation is proposed, and the effectiveness of the sample points is analyzed. From the aspect of inverse reliability analysis, a response surface method based on inverse reliability principle sampling is proposed, and the proposed method is combined with Kriging approximate. These two proposed methods both can accelerate calculation efficiency and improve the precision of solution. Numerical examples are used to verify these two methods, respectively. The reliability of driving axle shaft of combine harvester is studied respectively adopting these two proposed methods.(2) The way of obtaining load of drive axle housing and the fatigue life of drive axle housing are analyzed. The vertical dynamic load mathematical model about discrete road roughness is constructed for the drive axle housing, and vertical load time history is obtained using the measured discrete road roughness. Fatigue life of drive axle housing is predicted under constant load, random load and hybrid load. Besides, under the hybrid load the change rule of fatigue life and dangerous part relative to design variable is studied. Strain time history of dangerous points of front axle housing of combine harvester is obtained by the experiment, and stress time history is acquired according to the stress-strain relationship. Then, load spectrum is formulated by appling rainflow counting method. The fatigue life of each dangerous point is calculated by using the nominal stress method and Miner damage rule. The most dangerous part is the location of the front axle housing supporting the body of combine harvester.(3) Reliability analysis of gearbox transmission system of combine harvester is conducted. Firstly, the components are classified on the basis of their importance to the reliability of gearbox transmission system. Secondly, according to the transmission principle, the failure of first gear, second gear, third gear and reverse gear are made detailed analysis, respectively. Finally, the fault tree model and the mathematical model of system failure are established. Based on the importance analysis, the solution models of structural importance, probability importance and relative probability importance are built for the gearbox transmission system of combine harvester.(4) A neural network reliability evaluation method based on interval sampling and extension theory is put forward to establish the reliability evaluation model of combine harvester chassis. Based on corresponded relationship between reliability evaluation index and evaluation result’s graded interval, reliability index sample is generated by means of interval sampling and permutation-combination, and the evaluation order of each combination sample is determined by applying extension theory and subjective correction, finally the reliability data sample used for network practice is created. The learning algorithm of BP neural network is established using entropy error function, and network structure is adjusted by increasing judge layer in output layer. The proposed method can improve the convergence rate of the neural network and the accuracy of reliability evaluation.