Feature Selection and Fault Detection Under Dynamic Conditions of Chiller Systems

Faults in chiller systems can significantly reduce energy efficiency and operational performance. To address this, fault detection and diagnosis (FDD) algorithms are increasingly integrated into building management systems (BMS). This study proposes a comprehensive FDD framework addressing two key aspects: (1) fault detection under dynamic operating conditions and (2) selection of key variables for unsupervised fault detection. Traditional approaches usually assume steady-state operation, limiting their ability to capture transient and nonlinear system behaviors. The proposed method integrates Variational Mode Decomposition (VMD) for noise reduction and signal denoising with Kernel Principal Component Analysis (KPCA) to capture nonlinear behavior in chiller systems. This combination enables accurate fault detection under both steady and transient conditions. Furthermore, a wrapper-based step-forward feature selection algorithm identifies the most informative variables for KPCA-based fault detection. Assuming at least one known fault type, the method minimizes the Missing Alarm Rate (MAR) and False Alarm Rate (FAR), enhancing adaptability to different sensor configurations. The proposed approach is validated on the ASHRAE RP-1043 dataset using first-level severity faults. Results show that the VMD-KPCA method detects 98% of faulty samples, significantly outperforming linear PCA (55%), and highlight the importance of vapor compression parameters and thermodynamic insights in improving fault detection reliability.