Fault Detection and Diagnostics for Rooftop Air Conditioners

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was concluded that the method was quite robust against sensor error and could detect and
diagnose several types of faults.
Carling (2002) presented a comparison of three fault detection methods for AHUs.
The three methods were: a qualitative method that compares controller outputs and
model-based predictions, a rule-based method that examines measured temperatures and
controller outputs, and a model-based method that analyzes residuals based on steadystate
models. The author concluded that the first method was easy to set up and generated
few false alarms. However, it detected only a few faults of those introduced. The second
method is straightforward and detected more faults while requiring some analysis during
setup. The third method also detected more faults but it also generated more false alarms
and demanded considerably more time for setup. The third method may have generated
more false alarms because of poor steady-state detector performance and a bad detection
and diagnosis threshold. In this paper, an exponentially weighted variance steady-state
detector was used. Our investigation, which will be discussed in a later part of the report
shows that the variance method, either the exponentially weighted method or fixed
moving window method, is not robust enough and should be used together with a slope
method for steady-state detection.
Dexter and Ngo (2001) proposed a multi-step fuzzy model-based approach to
improve their earlier diagnosis results for AHUs. A computer simulation study
demonstrated that a more precise diagnosis can be obtained and experimental results also
showed that the proposed scheme does not generate false alarms. This method was based
on the use of two kinds of reference models, the fault-free reference model and one of the
reference models describing faulty behavior, to perform multiple-diagnosis. Although
this new technique overcame some weaknesses of the fuzzy method, the difficulty to
summarize or generate fuzzy rules when the number of fault types and levels and load
levels increased could not be eliminated.
In addition to fuzzy methods, several investigators (Lee & Park, 1996, Li & Vaezi
1997) attempted to use artificial neural network (ANN) directly to do FDD for AHUs.
The common feature of ANN FDD is to use an ANN to map the symptoms to the fault
indicators. The network must first be trained to recognize the symptoms of the possible