Fault Detection and Diagnostics for Rooftop Air Conditioners

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1.2.2 Latest Progress
Since 1999, about 30 papers have been published on FDD for HVAC systems.
According to the IEA ANNEX 34 final report edited by Dexter and Pakanen (2001),
• Twenty-three prototype FDD performance monitoring tools and three validation tools
have been developed.
• Thirty demonstrations have been taken place in twenty buildings.
• Twenty-six FDD tools have been tested in real buildings.
• Four performance monitoring schemes have been jointly evaluated on three
documented data sets from real buildings.
• A test shell has been developed to simplify the comparative testing of FDD tools.
1.2.2.1 Packaged Air Conditioning Systems
Rossi and Braun (1996 and 1997) modified the general FDD supervision
methodology first described by Isermann (1984) for non-critical HVAC system as shown
in Figure 1-3 and developed a statistical rule-based (SRB) FDD technique for vapor
compression air conditioners. This technique uses only nine temperatures and one relative
humidity. Among the ten measurements, ambient air temperature
T amb
, return air
temperatureT ra , and return air relative humidity Φ
ra
(or wet-bulb temperature T wb ) are
considered to be driving conditions. The other seven measurements (evaporating
temperature T evap
, condensing temperature T cond
, suction line superheat T sh , liquid line
subcooling T sc , compressor discharge temperature T dis , air temperature rise across the
condenser ∆ Tca
, and air temperature drop across the evaporator ∆ Tea
) are used to
specify the system operating state. A steady-state model is used to describe the
relationship between the driving conditions and the expected output states in a normally
operating condition. By comparing the measurements of the output states with those
predicted by the steady-state model, residuals are generated. These residuals are
statistically evaluated to perform fault detection and compared with a set of rules based