I. Introduction
A large amount of the energy used in a building is used for heating, ventilation, and air conditioning (HVAC), which is a sustainable use of total energy consumption. Besides, this share is steadily rising as a result of climate change. In the tropics, a major share of HVAC energy is used by chillers, with the compressor using a significant amount of power. The chiller failure will greatly increase the building’s energy consumption. Therefore, chiller fault diagnosis is an effective method to lower the utilization of energy in buildings. The classical fault diagnosis methods of chillers are divided into two main types: rule-based methods and model-based methods. These model-based and rule-based approaches heavily rely on the subjective prior knowledge of the chiller on the fault mechanism. With the increasing complexity of equipment, it is increasingly difficult for fault diagnosis methods based on mechanism analysis to achieve high performance. Recently, deep learning (DL), with its powerful data processing and feature extraction capabilities, has been widely put into the fault diagnosis of complex equipment and effectively enhanced the diagnostic performance [1], [2], [3], [4], [5], [6], [7], [8], [9], [10].