Fault Detection and Diagnostics for Rooftop Air Conditioners

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3.2 Improved SRB Approach
Figure 3-2 depicts the overall structure of the SRB FDD method presented by
Rossi and Braun (1997). Data (including system driving conditions and state variables)
gathered from HVAC equipment are fed into the preprocessor, which includes a steadystate
model and a preprocessor for the steady-state detector. The steady-state detector
determines whether the system is considered to be at steady state, a necessary condition
for the fault detection and diagnosis steps, and provides a binary output to a switch (SW),
which ignores the output of the detection and diagnostic classifiers unless the system is in
steady-state. The steady-state model uses the measured driving conditions (ambient drybulb
temperature, mixed air temperature and wet-bulb) to predict state variables under
normal operation (evaporation temperature, suction superheat, condensing temperature,
condenser subcooling, compressor hot gas temperature, condenser and evaporator air
temperature differences). The residuals between current measured and predicted normal
operating states are used by fault detection and diagnostic classifiers.
Driving
Conditions
Plant
Air Conditioning Unit
Measurements
T amb
T ra
T wb
Preprocessor
Sensors
Steady
State
Model
- +
Sensors
Steady State
Preprocessor
T evap
T sh
T cond
T sc
T hg
∆T ca
∆T ea
Classifier
Residuals
Diagnostic
Classifier
Fault
Detection
Classifier
Steady
State
Classifier
SW
Figure 3-2 Structure of the SRB FDD method