Fault Detection And Diagnosis For Water-Cooled Chiller System

Chan, Kai Yang (2018) Fault Detection And Diagnosis For Water-Cooled Chiller System. Project Report. UTeM, Melaka, Malaysia. (Submitted)

Text (24 Pages) Fault Detection And Diagnosis For Water-Cooled Chiller System.pdf - Submitted Version Download (362kB)

Abstract

There are many types of air conditioner such as the split unit air conditioner, the package unit air conditioner and the chiller system which is widely used in modern commercial building. The demand of indoor air quality, heating, ventilation and air conditioning (HVAC) systems especially the chiller system of a HVAC system has been continuously increasing from years to years. The HVAC system has become more and more complex to provide a comfortable indoor environment as well as to provide energy management in modern building. However, an operating system with unidentified fault may lead to high energy consumption and system failure. As a result, a fault detection and diagnosis system which is able to identify fault as well as to monitor and diagnose faults within the HVAC system is required. Principal Component Analysis (PCA) is a Fault Detection Method based on data and parameters. It is used to reduce the dimensionality of data and detect faults based on data. On the other hand, a trained model with the implementation of the K-Nearest Neighbor (KNN) algorithm is used to diagnose faults based on input data. Fault detection and diagnosis system is essential in detecting and diagnosing faults to prevent system failure.

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