Fault Detection and Diagnostics for Rooftop Air Conditioners

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motor speed. This technique was developed to detect and diagnose a limited number of
air handler faults and was shown to work well with data taken from a test building. The
other method relies on physical models of the electromechanical dynamics that occur
immediately after a motor is turned on. This technique has been demonstrated with submetered
data for a pump and for a fan. Tests showed that several faults could be
successfully detected from motor startup data alone. While the method relies solely on
generally stable and accurate voltage and current sensors, thereby avoiding problems with
flow and temperature sensors used in other fault detection methods, it requires electrical
data taken directly at the motor, down-stream of variable-speed drives, where current
sensors would not normally be installed for control or load-monitoring purposes. Later
Norford and Wright (2002) presented some results from controlled field tests and
concluded that: the first-principles-based method misdiagnosed several faults and
required a larger number of sensors than the electrical power correlation models, while
the latter method demonstrated greater success in diagnosis (limited number of faults
addressed in the tests may have contributed to this success) but required power meters
that were not typically installed.
Yoshida and Kumar (1999) presented a model-based methodology for online fault
detection for VAV HVAC systems. Two models, Auto Regressive Exogenous (ARX)
and Adaptive Forgetting through Multiple Models (AFMM), were trained and validated
on data obtained from a real building. Based on the results, it was concluded that the
variation of parameters rather than the difference between the predicted and actual output
is more prominent and reflective of a sudden fault in the system. The AFMM could detect
any change in the system but required a long window length and therefore may not detect
faults of low magnitude. The ARX model, on the other hand, could be used with very
short window length and was more robust. Yoshida and Kumar (2001a) further put forth
an off-line analysis based on ARX method. It was concluded that off-line analysis of data
by this model was likely to detect most of the faults. To evaluate the robustness of this
technique, Yoshida and Kunmar (2001b) developed a recursive autoregressive exogenous
algorithm (RARX) to build the frequency response dynamic model for VAV AHUs. It