A Louver Exhaust Fan Centralized Control System with Predictive Maintenance

Abstract

In order to reduce the failure rate of the louver exhaust fan on the farm and improve the economic benefit of the farm, a louver exhaust fan centralized control system with predictive maintenance characteristics was proposed. The system design is divided into three parts: the perception layer, the monitoring layer, and the analysis layer. The perception layer and the monitoring layer are based on the embedded system, which collects, transmits, and monitors the louver exhaust fan operation data in real time. The monitoring layer issues alarms and emergency control commands when necessary; the analysis layer is based on Gated Recurrent Unit (GRU) and The fusion network model of Convolutional Neural Networks (CNN) performs deep feature extraction on the operating state of the wind turbine. By calculating the similarity between the model output eigenvectors and the actual eigenvectors, it is possible to judge whether there is any abnormality in the louver exhaust fan operation and achieve the purpose of predictive maintenance. The actual test of the system shows that the collection and monitoring functions of the system operate normally, and the judgment accuracy of abnormal operation status is high, which is of practical significance for reducing the failure rate of louver exhaust fans.