A Fault Detection and Diagnostics
(FDD) tool continuously identify faults and efficiency improvement
opportunities through a 1-way interface to the building automation system and
application of automated analytics. It
is estimated that 5-30% energy saving can be achieved by employing FDD tools
and implementing efficiency measures based on FDD findings. Although the potential of this technology is
high, currently, an action is always required to correct the faults to generate
energy savings. However, a subset of
faults can potentially be addressed automatically by the system without
operator intervention. Automating this
fault "correction" can increase the savings generated by FDD tools. This presentation describes innovative fault
auto-correction algorithms for HVAC systems. When the auto-correction routine is triggered,
it overwrites control setpoints or other variables (via BACnet protocol) to
implement intended changes. The
presentation also showcases the field study of fault auto-correction algorithms
implemented in two commercial FDD platforms and tested in four buildings.
Learning Objectives:
1. Identify the current state of the art in fault detection and diagnostics.
2. Describe the value proposition of fault detection and diagnostics.
3. Summarize the comprehensive set of fault auto-correction algorithms designed to beĀ integrated with commercial FDD tools.
4. Detail the performance and efficacy of automated fault correction.
Learning Objectives:
1. Identify the current state of the art in fault detection and diagnostics.
2. Describe the value proposition of fault detection and diagnostics.
3. Summarize the comprehensive set of fault auto-correction algorithms designed to beĀ integrated with commercial FDD tools.
4. Detail the performance and efficacy of automated fault correction.
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