Research Of Device Life Prediction Method Based On BP Neural Network

In the era of Industry 4.0,there are many equipment in the industrial production field.How to improve efficiency and safety is a major problem in the field of equipment operation assurance.Therefore,it is necessary to carry out the research of RUL prediction technology method.BP neural network has obvious advantages in dealing with nonlinear relationships and is often applied to various prediction and classification problems.Therefore,this article will study the RUL prediction method based on BP neural network.First introduced the technical and theoretical basis of equipment life prediction based on BP neural network,and then carried out research with a certain factory equipment as the research object,proposed a equipment life prediction model based on BP neural network,and verified the proposed method on the equipment telemetry data set.In order to further improve the predictive ability of the model,First,the advantages of the genetic algorithm in the global search ability are used to optimize the weights and thresholds of the BP neural network.The experimental results show that the genetic algorithm does improve the prediction rate of RUL,but due to the iteration of the genetic algorithm,the training time is prolonged.Therefore,the particle swarm algorithm is used to replace the genetic algorithm to optimize the BP neural network and perform experimental verification.The results show that the particle swarm algorithm is used instead of the genetic algorithm to optimize the BP neural network.The neural network optimized by the group algorithm has a better prediction effect.Compared with traditional RUL prediction technologies,the prediction model in this paper is simple to implement and has significant prediction effects.