Diagnosis of single and multiple-source faults of chiller sensors using EWEEMD-ICKNN by time sequence denoising and non-Gaussian distribution feature extraction

Currently, fault detection of chiller sensors primarily focuses on single-source fault (SSF) sensors. These methods are typically ineffective in multiple source faults (MSF) cases. These methods assume a Gaussian distribution of the chiller data and ignore the coupling between the sensors. Therefore, we propose temporal feature denoising and non-Gaussian distribution-based methods in this study. First, the chiller sensor signals are denoised based on exponential weighted moving average ensemble empirical mode decomposition (EWEEMD). Subsequently, an independent component analysis k-nearest neighbour (ICKNN) classifier is proposed based on the non-Gaussian distribution features of the chiller sensor data. A residual vector combined with independent component analysis was used for feature extraction to propose the ICKNN classifier. Finally, according to their characteristics, different SSF and MSF were applied to the chiller sensors. Experimental comparisons with other methods were performed for four single-source faults and six multisource faults. The EWEEMD-ICKNN was experimentally confirmed to have excellent fault detection performance in the SSF and MSF chiller sensors. The experimental results demonstrate that the fault diagnosis rate of EWEEMD-ICKNN ranges from 99.90% to 86.80%. Additionally, the fault diagnosis efficiency of EWEEMD-ICKNN remains consistently stable across various fault states, exhibiting an average F1 score of 0.9589 with F1 score statistics of 0.038.