Faults in heating, ventilation and air conditioning (HVAC) systems result in excessive energy waste and space comfort issues. In this study, the goal is to develop fault detection and diagnosis (FDD) approaches to identify fan belt slippage and pressure setpoint override faults. These faults can be easily detected based on the correlation of head and airflow and power for fans and pumps. Currently, these faults have to be detected by either model-based or rule-based fault detection and diagnosis (FDD) approaches. Model based approaches generally require high computational time, which makes them unsuitable for real time applications. The rule based approaches use other operating data rather than flow rate and cannot accurately detect these faults. On the other hand, a virtual flow meter technology, which determines flow rate based on the measured head and power of fans and pumps, makes the flow measurement accurately and economically without the need of physical flow meters. The purpose of this paper is to evaluate FDD methods for faults in air and water distribution systsms using the measured head and power along with a virtual flow meter. First, the correlation between fan power and head and the correlation between the pump head and flow rate are derived without and with faults based on fan and pump performance and system curves. Then experiment is conducted to validate developed FDD methods by comparing the actual correlations with the fault-free correlations. The results show the proposed FDD methods can effectively detect these faults.