Chiller gradual fault detection based on Independent Component Analysis

Aiming at the chiller process variables cannot be strictly obey the Gauss distribution, and the large number of variables between the serious correlation, this paper describes a fault detection method to detect the faults of chiller. Independent Component Analysis(ICA) approach is used to extract the correlation of variables of chiller and reduce the dimension of measured data. A ICA-based method model is built to determine the thresholds of statistics and calculate statistics I² and SPE, which are used to check if a fault occurs in chiller. The method is validated using the laboratory data from ASHRAE RP-1043 and compared with Principle Component Analysis (PCA). Results show that the ICA-based method has better fault detection performance of chiller. It has very good sensitivity for early fault and can effectively reduce the false alarm rate.