Fault detection, diagnostics, and prognostics (FDD&P) is attracting an amount 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, particularly HVAC prognostics, 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 effects analysis (FMEA), which is a systematic method of identifying and preventing system, product and process problems. To address some of these challenges, the authors propose an FMEA method for common building HVAC equipment by exploring work-orders generated by building energy management systems (BEMS) using a data mining approach. With this FMEA approach, it is possible for building operators to isolate and prognose faults practically. The FMEA approach also helps us tackle high impact failures, for which operation data can be acquired and machine learning-based predictive models can be developed. This paper reports some preliminary results in conducting an HVAC FMEA from a large number of work-orders obtained from a BEMS in routine operations. The HVAC FMEA will be used as a guidance tool for data gathering and developing data-driven models for HVAC FDD&P and as a practical solution for HVAC prognostics in case that predictive models are difficult to develop.