Data-Driven Fault Detection and Diagnosis: Research and Applications for HVAC Systems in Buildings.

In particular, three main topics are analyzed: (i) FDD classification and taxonomy, (ii) approaches to data-driven FDD in HVAC systems, (iii) deployment of FDD strategies in buildings and related impact assessment. The main goal of Fault Detection and Diagnosis (FDD) processes is to identify faults, determine their sources, and recognize solutions before the system is further harmed or service is lost. In the case where model-based methodologies are adopted, professionals can develop the FDD tools by using only the building or system's metadata, while data-based methods require calibrated measurement training data. Kim I. and Kim W. [[6]] presented a data-driven FDD approach that uses ML classification methods to detect and diagnose faults in a 90 ton (approximately 316 kW) centrifugal chiller system. [Extracted from the article]