Effects of K-value in the K-Nearest Neighbors Algorithm on Performace of Chiller Fault Detection and Diagnosis - Le, Hà Đông Quân; Le, Minh Nhựt (Journal of Technical Education Science, 2023)
Fault detection and diagnosis for water chiller systems help to extend the life of the system, prevent serious damage, and reduce energy consumption. For these reasons, this paper investigates the K value of the KNN algorithm and proposes a fault detection and diagnosis model for the water chiller system based on the K-nearest neighbors algorithm (FDD-KNN). The study results indicated that the FDD-KNN model has an accuracy rate of 99.15% or higher when the value of K is 1. When compared to previous studies for LV3 and LV4 severity faults, the proposed model exhibits high and uniform diagnostic accuracy across all faults and fault levels. In addition, the fault isolation and fluctuating trends of the variables in practice for Normal and fouling of the ConFoul condenser were also checked by the actual database of the water chiller systems, Center Saigon Building, Ho Chi Minh City, Vietnam. The results show that the changing trend of the two variables, TCO and TRC, is consistent with the actual operating conditions of the system. Therefore, the proposed model FDD-KNN with K=1 is completely reliable to apply to fault detection and diagnosis for water chiller systems.