A FDD Model of VAV Systems Based on Neural-Networks and Residual Statistics
Components and sensors in VAV(Variable Air Volume)air distribution systems often suffer from failure easily,which result in energy waste,performance degradation or totally out of control.However,there is no applicable automatic commissioning tool for the VAV systems by now.Fault detection and diagnosis(FDD)models for VAV terminal units based on heat-mass balance of air conditioning areas are proposed in this study.Two BP neural network prediction models are built up for predicting the required air flow volume and the demand values of VAV damper opening,Fault detection can be implemented by means of statistics of the residuals between the measured values and the model predictions.
Fault detection and diagnosis VAV air conditioning BP neural network Residual statistical
Han Qi Wei Dong
Beijing University of Civil Engineering and Architecture,Beijing 100044
国际会议
长沙
英文
1555-1559
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）