| With the rapid development of high-speed railway and intelligent manufacturing technology,the production line of high speed train is constantly developing to be automated and specialized.So how to ensure the efficient operation of automatic production line has become the primary concern of the enterprise.Automation assembly line as a whole project,any equipment’s breakdown will affect its normal operation and cause serious economic losses.For the traditional maintenance,such as post maintenance and regular maintenance,it is time-consuming and difficult to prevent breakdown.Therefore,the concept of state based maintenance has begun to be paid attention to and put into application by the enterprise.The core issue of state based maintenance is how to carry out effective health state assessment and trend prediction for equipment,so as to implement effective maintenance measures.In this paper,the health state assessment and prediction of AGV trolley battery on assembly line are studied.After studying the health state assessment of the battery and life prediction of domestic and abroad,the Elman neural network technology is selected for the research.The paper has completed the following items:firstly,for the poor stability of the Elman neural network training results,the initial weights and thresholds are optimized by the genetic algorithm.The GA-Elman network model is established to reduce the randomness of the initial state of the network and improve the stability of the network training results.Secondly,the learning efficiency of BP algorithm is low,which is used to be the learning rule of the Elman neural network.On the basis of the GA-Elman network,the particle swarm optimization is combined to optimize the learning rule of the network.The GA-IPSO-Elman network model is established to improve the training speed and training effect of the network.Finally,combining the actual production environment,the research results of this paper are realized and put into the automatic assembly line monitoring system to predict the health status of the AGV trolley battery. |
