Abstract
The stable and efficient operation of the cold source system can significantly reduce building energy consumption and improve indoor comfort. Health monitoring of the cold source system is a necessary means to ensure such a requirement. Due to the variations of ambient temperature and cold load, the cold source system often running in multiple modes, which limits the application of conventional health monitoring methods with poor performance, e.g. high false alarm rate. Therefore, this paper uses our previously proposed method, just-in-time-learning aided canonical correlation analysis (JITL-CCA), to monitor the health condition of the real cold source system. The application steps are detailed, including how to select monitoring variables and how to use reconstruction-based contribution (RBC) to assist JITL-CCA in fault diagnosis. The on-site data experiments show the applicability of the JITL-CCA method and its superior performance when comparing with the conventional CCA method.
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