Research On Failure Prediction Method Of Industrial Equipment Based On Federated Learning

Modern industrial equipment and systems are more complex and difficult to manage.In recent years,fault prediction technology has been widely used in industry.This paper focuses on the failure prediction method of industrial equipment based on federated learning,analyzes the characteristics of the traditional algorithm,designs a privacy protection,security and efficient fault prediction algorithm,and verifies the performance of the algorithm in the experimental simulation.The work of this paper is divided into the following two parts:The first part includes the traditional method and the model of time series data classification and analysis the problem of the traditional timeseries data classification method.The time series classification algorithm based on convolution neural network has some problems,which ignores the samples of the temporal correlation.Moreover,there are few fault data samples in the actual scene,which affects the identification accuracy of the model.In this paper,a deep space transformation network algorithm is proposed.The spatial transformation unit is used to improve the problem of less fault data samples and improve the generalization ability of the model.The attentional mechanism is introduced to further improve the accuracy of fault prediction.In view of the characteristics of spatial movement of data,this method has good anti-interference to spatial movement,and the performance of this method is verified by designing experiments.The second part analyzes the data privacy protection and temporal correlation.In practical application scenarios,data collected by sensors often contain a lot of privacy,and directly used as training data will cause privacy disclosure and threaten production safety.Moreover,the research content of the first part still ignores the temporal correlation of data collected by sensors.Based on this,this paper introduces federated learning and short-time Fourier transform modules,and proposes a fault prediction method of industrial equipment based on federated learning,which can effectively avoid data privacy disclosure.Aiming at the problem of high communication cost of federated learning,this paper proposes a federated learning average algorithm based on communication efficiency,which can reduce the communication cost and improve the training efficiency of federated learning.