Fault Detection and Diagnostics for Rooftop Air Conditioners

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ABSTRACT
Although the statistical rule-based (SRB) fault detection and diagnosis (FDD)
method developed by Rossi and Braun (1997) has good performance for individual faults,
it requires measurements over a wide range of conditions for training reference models.
The development of these models can be time consuming and costly. Furthermore, SRB
FDD methods can only handle individual faults. This report presents new methods that
reduce engineering and installed costs for FDD and handle multiple-simultaneous faults.
The methods were evaluated using both laboratory and field data.
Inspired by a mathematical formulation for a general FDD methodology, a
decoupling-based FDD approach was developed to handle multiple-simultaneous faults.
To decouple different faults, all faults are categorized according to two criteria: scope of
the fault impact and fault cause. The fault impact scope separates faults into system-level
and component-level faults. The fault cause criterion classifies faults into service and
operational faults. After decoupling, features are identified that uniquely depend on each
fault. The other advantage of the mathematical formulation is that the previously
developed SRB FDD method can be cast within the general mathematical framework,
which guides the improvement and provides a better understanding of the SRB FDD.
In the proposed FDD approach, normal operation models and virtual sensors play
a very important role. So, various models and virtual sensors are proposed to generate
decoupled features. Wherever possible, physical or gray-box models are proposed that
exploit manufacturers’ performance rating data and only require very limited
experimental or field data to train model parameters.
Finally, three case studies are presented in this document. One case study
provides initial validation of the decoupling-based FDD approach using laboratory data
where single faults were artificially introduced into a 3-ton Trane rooftop unit (RTU)
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