Reliability Prediction Of Lithium Battery Production Line Based On Weighted Support Vector Machine

With the development of modern technology,notebook computers,new energy vehicles and other industries,the market demand for lithium batteries has increased significantly.There are many links in the production of lithium batteries and a large number of equipment;With the improvement of the quality,output and quality of lithium battery,higher requirements are put forward for the reliability of lithium battery production line.Therefore,how to effectively improve the quality and reliability of lithium battery production line has become an important proposition in enterprise production.In this paper,taking lithium battery production line as the research object,considering the influence of equipment on lithium battery production,the reliability prediction of lithium battery production line and equipment is studied.This paper has completed the following research work.Firstly,the lithium battery production line model of superimposed Weibull distribution based on pivot quantity method is established.In this paper,a parameter estimation method of pivot quantity based on superposition Weibull distribution is proposed by using pivot quantity and graphic method;The shape parameter estimation of superimposed Weibull distribution is obtained by graphic analysis.Based on this,the pivot quantity is constructed to estimate the interval of scale parameter,and then the point estimation is carried out by genetic algorithm to improve the parameter accuracy.In order to verify the correctness of the model assumptions and the accuracy of parameter estimation,the parameters of Weibull distribution,three parameter Weibull distribution and superimposed log linear process are estimated by pivot quantity method,Through the comparison,we can find that the AIC value of superimposed Weibull distribution model based on pivot quantity method is the largest and the decidable coefficient is the smallest,The results show that the proposed method is effective and accurate.Secondly,the importance of lithium battery production line equipment is analyzed based on Binary Decision Diagrams(BDD).Establish system reliability block diagram for lithium battery production line;The importance of lithium battery production line is analyzed by using binary decision diagram;Through the mapping relationship between reliability diagram and BDD of lithium battery production line,the condition probability diagram of each equipment is drawn and the probability value of each equipment condition is calculated,and the importance of each equipment is obtained,Through comparative analysis,it can be determined that roller press,No.1 coater and No.2 coater are important equipment,which have a great impact on lithium battery production line.Finally,the reliability of lithium battery production line is predicted based on weighted support vector machine.Traditional support vector machine(SVM)has low prediction accuracy and large error,The weighted support vector machine(WSVM)method is introduced to predict the reliability of lithium battery production line.Through theoretical analysis,it can be seen that the single kernel function can not have generalization ability and learning ability at the same time.Therefore,we choose polynomial kernel function with strong generalization ability and multilayer perceptron kernel function with strong learning ability to construct new kernel function.Particle swarm optimization algorithm is used to optimize the parameters of the new hybrid kernel function,and hybrid kernel weighted support vector machine is used to predict the reliability of lithium battery production line.Compared with single kernel function weighted support vector machine,support vector machine and BP neural network,the effectiveness of hybrid kernel function weighted support vector machine is verified by analyzing root mean square error.Effective reliability prediction is very important for the safety of production line,the completion of established tasks,the maintenance and replacement according to the situation,and the reduction of economic costs.It plays an exemplary role in the research of the reliability of lithium battery production line.