Research on a Novel Method Diagnosis and Maintenance for Key Produce Plant Based on MAS and NN

Abstract

As the development of the electrical power market, the maintenance automation has become an intrinsic need to increase the overall economic efficiency of hydropower plants. A Multi-Agent System (MAS) based model for the predictive maintenance system of hydropower plant within the framework of Intelligent Control-Maintenance-Management System (ICMMS) is proposed. All maintenance activities, form data collection through the recommendation of specific maintenance actions, are integrated into the system. In this model, the predictive maintenance system composed of four layers: Signal Collection, Data Processing, Diagnosis and Prognosis, and Maintenance Decision-Making. Using this model a prototype of predictive maintenance for hydropower plant is established. Artificial Neural-Network (NN) is successfully applied to monitor, identify and diagnosis the dynamic performance of the prototype system online.KeywordsMethod DiagnosisHydropower PlantSignal CollectionPrognosis ModelPredictive MaintenanceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.