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Abnormal operation of HVAC systems can result in an increase in energy usage as well as poor indoor air quality, thermal discomfort, and low productivity. Building automated systems (BAS) collects a massive amount of data related to the operation of each component of HVAC systems. Although BAS has been implemented in many buildings over the past decade, the collected data have not been analyzed thoroughly. Some studies have relied on data-mining methods to predict, detect, and diagnose faults in HVAC systems. This paper critically reviews the existing literature and identifies the research gaps in data-driven data mining fault detection and diagnosis (FDD) methods studies on HVAC systems. In this review, data-driven based FDD methods are classified into three classes, namely supervised, unsupervised, and hybrid-learning methods. The hybrid approaches are introduced as the preferred methods …