Abstract

In a heating, ventilation, and air conditioning (HVAC) system, a whole building fault (WBF) refers to a fault that occurs in one component but may trigger additional faults/abnormalities on different components or subsystems resulting in significant impacts on the energy consumption or indoor air quality in buildings. At the whole building level, interval data collected from various components/subsystems can be used to detect WBFs. In the Part I of this study, a novel data-driven method which includes weather and schedule-based pattern matching (WPM) procedure and a feature based principal component analysis (FPCA) procedure was developed to detect the WBF. This article is the second of a two-part study of the development of the whole building fault detection method. In the Part II of the study (this paper), various WBFs were designed and imposed in the HVAC system of a campus building. Data from both imposed fault and naturally occurred faults were collected through the building automation system (BAS) to evaluate the developed fault detection method. Evaluation results show that the developed WPM-FPCA method reaches a satisfactory detection rate (85% and 100% under two principal component retention rates) and a 0% false alarm rate (under two principal component retention rates).

References

1.
Brambley
,
M. R.
,
Haves
,
P.
,
McDonald
,
S. C.
,
Torcellini
,
P.
,
Hansen
,
D. G.
,
Holmberg
,
D.
, and
Roth
,
K.
,
2005
,
Advanced Sensors and Controls for Building Applications: Market Assessment and Potential R&D Pathways
,
Pacific Northwest National Laboratory
,
Richland, WA
.
2.
Sechilariu
,
M.
,
Wang
,
B.
, and
Locment
,
F.
,
2013
, “
Building-Integrated Microgrid: Advanced Local Energy Management for Forthcoming Smart Power Grid Communication
,”
Energy Build.
,
59
, pp.
236
243
.
3.
Kintner-Meyer
,
M.
,
Molburg
,
J.
,
Subbarao
,
K.
,
Kumar
,
N. P.
,
Bandyopadhyay
,
G.
,
Finley
,
C.
,
Koritarov
,
V.
,
Wang
,
J.
,
Zhao
,
F.
, and
Brackney
,
L.
,
2010
,
The Role of Energy Storage in Commercial Building: A Preliminary Report
,
Pacific Northwest National Laboratory
,
Richland, WA
.
4.
O'Neill
,
Z.
,
Pang
,
X.
,
Shashanka
,
M.
,
Haves
,
P.
, and
Bailey
,
T.
,
2014
, “
Model-Based Real-Time Whole Building Energy Performance Monitoring and Diagnostics
,”
J. Build. Perform. Simul.
,
7
(
2
), pp.
83
99
.
5.
Schein
,
J.
, and
Bushby
,
S. T.
,
2006
, “
A Hierarchical Rule-Based Fault Detection and Diagnostic Method for HVAC Systems
,”
HVACR Res.
,
12
(
1
), pp.
111
125
.
6.
Lin
,
G.
, and
Claridge
,
D. E.
,
2015
, “
A Temperature-Based Approach to Detect Abnormal Building Energy Consumption
,”
Energy Build.
,
93
, pp.
110
118
.
7.
Haves
,
P.
,
Salsbury
,
T.
,
Claridge
,
D.
, and
Liu
,
M.
,
2001
, Use of Whole Building Simulation in On-Line Performance Assessment: Modeling and Implementation Issues.
8.
Li
,
S.
, and
Wen
,
J.
,
2014
, “
A Model-Based Fault Detection and Diagnostic Methodology Based on PCA Method and Wavelet Transform
,”
Energy Build.
,
68
, pp.
63
71
.
9.
Chen
,
Y.
,
Wen
,
J.
, and
James
,
L.
,
2021
, “
Using Weather and Schedule Based Pattern Matching and Feature based Principal Component Analysis for Whole Building Fault Detection — Part I Development of the Method
,”
ASME J. Eng. Sustain. Bldgs. Cities.
, pp.
1
13
. doi.org/10.1115/1.4052729.
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