A predictive maintenance analysis model for power plant equipment based on the Internet of Things
Accurately predicting the health status of power plant equipment is of great significance for determining the reliability and service life of the equipment.In order to perform predictive maintenance on power plant equipment,this paper proposes an edge predictive maintenance analysis model based on short-term and short-term memory models.Predict the remaining service life of equipment by considering the status of different components,and evaluate equipment degradation based on real-time data collected from operating equipment.To label the dataset,fuzzy logic is used to generate maintenance priorities,which are used to calculate the actual remaining service life.Deploy the proposed model to the power plant equipment and conduct performance evaluation.The results indicate that the edge prediction maintenance analysis model is much easier to develop and deploy,and has good predictive maintenance performance.
predictive maintenancefault detectionlong-and short-term memory modelsedge prediction maintenance analysis model
万方数据
维普
