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.
Highlights
Heating, ventilation, and air-conditioning (HVAC) equipment faults and operational errors result in comfort issues and waste of energy in buildings
They show similar patterns as in case study 1: The heating and cooling energy is linearly correlated to outdoor air temperature; the fan energy does not change much with outdoor air temperature
By checking the detailed operation data, we found out that it was caused by missing meter data or early shut down of the Air Handling Units (AHUs)
Summary
Ventilation, and air-conditioning (HVAC) equipment faults and operational errors result in comfort issues and waste of energy in buildings. Thanks to the rapid development of information technologies, sensors, and direct digital controllers, massive amounts of operation data are collected in buildings. These data are often not fully exploited to reveal the faults. Regression methods use normal operation data to train the model, and compare the prediction with real measurements to detect faults. Classification methods use normal operation and abnormal operation data to train the model, and to recognize patterns of different faults. A lot of data-driven AFDD solutions have been developed, most of them have limited capability in diagnostics, unless utilizing fault operation data which are usually not available in buildings. The third level uses supervised machine learning regression based on normal data to diagnose mechanical faults
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