Monte Carlo analysis of the effect of uncertainties on model-based HVAC fault detection and diagnostics

Abstract

Faults in HVAC systems can have a significant negative impact on energy consumption, indoor thermal comfort, and air quality. Automatic fault detection and diagnosis tools can help commissioning providers, operators, and facility managers efficiently detect and diagnose faults. They also can help satisfy the increasing demand for commissioning. A model-based fault detection and diagnosis (FDD) method was developed to detect faults by comparing model prediction and measurement, and to diagnose faults using a rule-based fuzzy inferencing system. The method includes Monte Carlo analysis to improve the robustness of the fault detection and diagnosis and reduce false alarms. The Monte Carlo analysis is employed not only to predict uncertainties in reference model outputs, based on estimates of uncertainty in each of the measured inputs, but also to determine the confidence levels of fault diagnosis by combining the effects of input uncertainties at different operating points. A simulated variable-air-volume (VAV) system, including detailed component models that can simulate different faults as well as correct operation, was used to test the diagnostic rules and the Monte Carlo analysis included in the method. The effect of uncertainties on fault diagnosis is illustrated for various types of faulty operation.