Application of Residual-Based EWMA Control Charts for Detecting Faults in Variable-Air-Volume Air Handling Unit System

Abstract

An online robust fault detection method is presented in this paper for VAV air handling unit and its implementation. Residual-based EWMA control chart is used to monitor the control processes of air handling unit and detect faults of air handling unit. In order to provide a level of robustness with respect to modeling errors, control limits are determined by incorporating time series model uncertainty in EWMA control chart. The fault detection method proposed was tested and validated using real time data collected from real VAV air-conditioning systems involving multiple artificial faults. The results of validation show residual-based EWMA control chart with designing control limits can improve the accuracy of fault detection through eliminating the negative effects of dynamic characteristics, serial correlation, normal transient changes of system, and time series modeling errors. The robust fault detection method proposed can provide an effective tool for detecting the faults of air handling units.

Intelligent air conditioning control system based on human infrared thermal imaging

“…Among the existing technologies, there is an air conditioning controller that uses the camera to perform face recognition to determine the distribution of the crowd and then automatically adjust the temperature, and the literature [1] introduces the relevant application analysis of the PMV model in the rail transit car, and the feasibility of the PMV model for the temperature field is obtained. The literature [2] introduces the research of infrared human parameter recognition based on deep learning, and uses convolutional neural network to analyze the image of human physiological parameters on environmental thermal comfort.…”