| Abstract | Fault Detection, Diagnostics and Prognostics (FDD&P) is attracting a lot of attention from building operators and researchers because it can help greatly improve the performance of building operations by reducing energy consumption for heating, ventilation and air-conditioning (HVAC) while improving occupant comfort at the same time. However, FDD&P for building operations remains with many challenges due to special operation environments of HVAC systems. These challenges include `tolerance or ignorance' of failures in long-haul operations, lack of operation regulations, and even lack of documents for HVAC failure mode and effect analysis (FMEA), which is a systematic method of identifying and preventing system, product and process problems. To address some of these challenges, we propose to develop a FMEA for HVAC by exploring work orders generated by building energy management systems (BEMS) using a data mining approach. With the developed HVAC FMEA, it is possible to conduct pre-FDD&P procedures to improve HVAC maintenance and to select the high impact failures in order to acquire the operation data for selected failures and develop machine learning-based predictive models to predict a failure before it occurs and isolate the root component of a given failure. In this paper we report some preliminary results in developing an HVAC FMEA tool from a large number of work orders obtained from a BEMS in routine operations. The developed HVAC FMEA will be used as a guidance tool for data gathering and developing data-driven models for building HVAC FDD&P. |
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