Data mining and data-driven modelling for Air Handling Unit fault detection

Abstract

Data-driven automatic fault detection and diagnostics (AFDD) have gained a lot of research attention in recent years. Many existing solutions need to learn from the fault operation data to be able to diagnose the faults. However, these data are usually not available in buildings. In this study we present a data-driven AFDD solution for Air Handling Units (AHUs). The solution consists of three levels of fault detection that require different levels of data availability: the first level is daily energy benchmarking; the second level is control performance evaluation; and the third level is data-driven modelling of mechanical systems. The method is applied to two case studies: experimental data from ASHRAE project 1312-RP, and real-life operation data of an office building in France. These tests show that the solution is able to isolate control faults and mechanical faults of individual components, by learning from normal operation data only.