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|Title: ||Demonstration of fault detection and diagnosis methods for air-handling units (ASHRAE 1020-RP)|
|Authors: ||Norford, Leslie K.|
Wright, Jonathan A.
Buswell, Richard A.
Klaassen, Curtis J.
|Issue Date: ||2002|
|Publisher: ||© American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.|
|Citation: ||NORFORD, L.K. ... et al, 2002. Demonstration of fault detection and diagnosis methods for air-handling units (ASHRAE 1020-RP). HVAC&R Research, 8 (1), pp. 41-71|
|Abstract: ||Results are presented from controlled field tests of two methods for detecting and diagnosing
faults in HVAC equipment. The tests were conducted in a unique research building that featured
two air-handling units serving matched sets of unoccupied rooms with adjustable internal loads.
Tests were also conducted in the same building on a third air handler serving areas used for
instruction and by building staff. One of the two fault detection and diagnosis (FDD) methods
used first-principles-based models of system components. The data used by this approach were
obtained from sensors typically installed for control purposes. The second method was based on
semiempirical correlations of submetered electrical power with flow rates or process control
Faults were introduced into the air-mixing, filter-coil, and fan sections of each of the three
air-handling units. In the matched air-handling units, faults were implemented over three blind
test periods (summer, winter, and spring operating conditions). In each test period, the precise
timing of the implementation of the fault conditions was unknown to the researchers. The faults
were, however, selected from an agreed set of conditions and magnitudes, established for each
season. This was necessary to ensure that at least some magnitudes of the faults could be
detected by the FDD methods during the limited test period. Six faults were used for a single
summer test period involving the third air-handling unit. These fault conditions were completely
unknown to the researchers and the test period was truly blind.
The two FDD methods were evaluated on the basis of their sensitivity, robustness, the number
of sensors required, and ease of implementation. Both methods detected nearly all of the faults
in the two matched air-handling units but fewer of the unknown faults in the third air-handling
unit. Fault diagnosis was more difficult than detection. The first-principles-based method misdiagnosed
several faults. The electrical power correlation method demonstrated greater success
in diagnosis, although the limited number of faults addressed in the tests contributed to this success.
The first-principles-based models require a larger number of sensors than the electrical
power correlation models, although the latter method requires power meters that are not typically
installed. The first-principles-based models require training data for each subsystem
model to tune the respective parameters so that the model predictions more precisely represent
the target system. This is obtained by an open-loop test procedure. The electrical power correlation
method uses polynomial models generated from data collected from “normal” system operation,
under closed-loop control.Both methods were found to require further work in three principal areas: to reduce the number
of parameters to be identified; to assess the impact of less expensive or fewer sensors; and
to further automate their implementation. The first-principles-based models also require further
work to improve the robustness of predictions.|
|Description: ||This is a journal article [© American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (www.ashrae.org)]. Reprinted by permission from HVAC&R Research, Vol. 8, Part 1. Additional reproduction, distribution, or transmission in either print or digital form is not permitted without ASHRAE’s prior written permission. It is also available at: www.ashrae.org/hvacr-research|
|Appears in Collections:||Published Articles (Architecture, Building and Civil Engineering)|
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