Online Fault Diagnosis of Air-conditioning Units Using Recurrent Neural Network Trained with Simulation Data

抄録

Fault detection and diagnosis (FDD) of air-conditioning systems is very important for energy saving, safety and maintenance and in high demand. The air-conditioning system is a complex and dynamic system which makes the diagnosis difficult because the observed parameters do not directly express the state of the system. On the other hand, faults analysis and fault cases collected from the actual air-conditioning system are not practicable. With due consideration of the above points, the authors propose an effective FDD method for air-conditioning systems using model simulation and Recurrent Neural Network (RNN). An air-conditioning system of a certain building is used as a model in this research. The validity of the model is confirmed by comparing the simulation data with real observations. In this study, the authors concentrated on several typical faults in both chilled and hot water volume control valves. The authors obtained the data of the normal and some fault cases in time sequence using the model simulation. As a result of the current experiments, the recognition rate was over 90% and it was demonstrated that the proposed method is suitable for practical use.