For my project I am doing a research report on Automatic Fault Detection and Diagnosis (AFDD) of HVAC
systems. I created my outline in actual outline form rather than writing it out in paragraphs. I find this easier to put together and organize thoughts in preparation of writing a report. Depending on length/time availability I may expand a bit more on its adoption and influence on other "smart" building technologies.
1.
Buildings consume a large amount of energy in
the U.S. (get an estimate from reliable source)
a.
HVAC equipment use a lot of this energy consumed
in buildings
i.
These systems are usually not operating as they
are designed
ii.
There are usually faults in the system and/or
logic that are leading to a lot of wasted energy
iii.
Examples of faults/poor logic include:
1.
Simultaneous heating and cooling
2.
Dampers stuck open/shut/somewhere in between
3.
Heating/cooling coil valves leaking
2.
There have been large efforts to develop tools
to detect these faults – Automatic Fault Detection and Diagnosis (AFDD)
a.
Advantages
i.
Can save a lot of energy and thus money over
time
ii.
Can detect problems before “serious” damage
occurs
iii.
Saves money and time avoiding technicians having
to come out
iv.
Reduces complaints building operator has to deal
with
b.
There are different fault detection methods
i.
Physical modeling – detailed modeling of
the system
1.
Should in theory be the most accurate
2.
Impractical though because each system would
have to be custom modeled
ii.
Ruled-based – rules developed as to how
something should work by rules of thermodynamics and in relation to other
components in the system
1.
Most widely adopted/used commercially
2.
Not the most accurate
3.
A lot of false alarms are usually generated
4.
Rules have to be set up specific to the system
5.
Give an example of rule-based method
iii.
Data-driven – uses statistical approaches
using data already collected from system
1.
Plug-and-play technique
2.
Probably the most promising
3.
Fewer false alarms
4.
Give an example to contrast this and rule-based
methods
c.
Passive monitoring vs active diagnosis
3.
Commercialization and growth
a.
Buildings already collect vast amounts of data
i.
Most of this data is not being utilized
b.
Buildings are being equipped with more sensors
i.
Becoming cheaper and more worth the installation
c.
This data can be utilized in more ways than just
HVAC fault detection
i.
Smart grid
ii.
Demand response
Comments
Nunes and group - You guys are looking into an interesting
topic. I know that green and intelligent buildings are different but I'm
interested to see their similarities. I'm also curious about different
technologies used both currently and planned for the future for each
type.
Lee - I am in your shoes in that I have very limited Revit
experience. I'll be very interested to see how difficult you found this
project. At some point I'd like to familiarize myself a lot more with Revit so
hopefully you can provide some advice to us novices by the time you're
finished.
Sivertsen & Lavigne - Very good job in your initial
planning, it seems as though you have a great sight as to what you want to do
with this project. I've just recently begun to be exposed to databases, but I
don't know too much about them yet. I'll be very interested to hear what you
have to say.
very well planned outline, a good topic for research. By your outline we can see that you're covering topics that are really interesting and necessary for your research on Automatic Fault Detection and Diagnosis (AFDD) of HVAC systems. good job
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