Despite improved commissioning practices, malfunctions or degradation of building systems still contribute to increase up to 20% the energy consumption. During operation and maintenance stage, project and building technical managers need appropriate methods for the detection and diagnosis of faults and drifts of energy performances in order to establish effective preventive maintenance strategies. This paper proposes a hybrid and multilevel fault detections and diagnosis (FDD) tool dedicated to the identification and prioritization of corrective maintenance actions helping to ensure the energy performance of buildings. For this purpose, we use dynamic Bayesian networks (DBN) to monitor the energy consumption and detect malfunctions of building equipment and systems by considering both measured occupancy and the weather conditions (number of persons on site, temperature, relative humidity (RH), etc.). The hybrid FDD approach developed makes possible the use of both measured and simulated data. The training of the Bayesian network for functional operating mode relies on on-site measurements. As far as dysfunctional operating modes are concerned, they rely mainly on knowledge extracted from dynamic thermal analysis simulating various operational faults and drifts. The methodology is applied to a real building and demonstrates the way in which the prioritization of most probable causes can be set for a fault affecting energy performance. The results have been obtained for a variety of simulated situations with faults deliberately injected, such as increase in heating preset temperature and deterioration of the transmission coefficient of the building's glazing. The limitations of the methodology are discussed and are translated in terms of the ability to optimize the experiment design, control period, or threshold adjustment on the control charts used.

References

References
1.
de Wilde
,
P.
,
2014
, “
The Gap Between Predicted and Measured Energy Performance of Buildings: A Framework for Investigation
,”
Autom. Constr.
,
41
, pp.
40
49
.
2.
van Dronkelaar
,
C.
,
Dowson
,
M.
,
Spataru
,
C.
, and
Mumovic
,
D.
,
2016
, “
A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-Domestic Buildings
,”
Front. Mech. Eng.
,
1
, p.
17
.https://www.researchgate.net/publication/290478576_A_Review_of_the_Regulatory_Energy_Performance_Gap_and_Its_Underlying_Causes_in_Non-domestic_Buildings
3.
Titikpina
,
F.
,
Caucheteux
,
A.
,
Charki
,
A.
, and
Bigaud
,
A.
,
2015
, “
Uncertainty Assessment in Building Energy Performance With a Simplified Model
,”
Int. J. Metrol. Qual. Eng.
,
6
(
3
), p.
308
.
4.
Katipamula
,
S.
, and
Brambley
,
M. R.
,
2005
, “
Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—Part I: A Review
,”
HVACR Res.
,
11
(
1
), pp.
3
25
.
5.
Jing
,
R.
,
Wang
,
M.
,
Zhang
,
R.
,
Li
,
N.
, and
Zhao
,
Y.
,
2017
, “
A Study on Energy Performance of 30 Commercial Office Buildings in Hong Kong
,”
Energy Build.
,
144
, pp.
117
128
.
6.
Bynum
,
J. D.
,
Claridge
,
D. E.
, and
Curtin
,
J. M.
,
2012
, “
Development and Testing of an Automated Building Commissioning Analysis Tool (ABCAT)
,”
Energy Build.
,
55
, pp.
607
617
.
7.
Wang
,
L.
,
2012
, “
Modeling and Simulation of HVAC Faulty Operations and Performance Degradation Due to Maintenance Issues
,”
Asia Conference of International Building Performance Simulation Association
(
ASIM'2012
), Shangai, China, Nov. 27–29, p.
8
.https://www.researchgate.net/publication/258246690_Modeling_and_Simulation_of_HVAC_Faulty_Operations_and_Performance_Degradation_due_to_Maintenance_Issues
8.
Verhelst
,
J.
,
van Ham
,
G.
,
Saelens
,
D.
, and
Hensen
,
L.
,
2017
, “
Model Selection for Continuous Commissioning of HVAC-Systems in Office Buildings: A Review
,”
Renewable Sustainable Energy Rev.
,
76
, pp.
673
686
.
9.
Yu
,
Y. B.
,
Woradechjumroen
,
D.
, and
Yu
,
D. H.
,
2014
, “
A Review of Fault Detection and Diagnosis Methodologies on Air-Handling Units
,”
Energy Build.
,
82
, pp.
550
562
.
10.
Zhao
,
Y.
,
Wang
,
S. W.
, and
Xiao
,
F.
,
2013
, “
A Statistical Fault Detection and Diagnosis Method for Centrifugal Chillers Based on Exponentially-Weighted Moving Average Control Charts and Support Vector Regression
,”
Appl. Therm. Eng.
,
51
(
1–2
), pp.
560
572
.
11.
Verbert
,
K.
,
Babuška
,
R.
, and
De Schutter
,
B.
,
2017
, “
Combining Knowledge and Historical Data for System-Level Fault Diagnosis of HVAC Systems
,”
Eng. Appl. Artif. Intell.
,
59
, pp.
260
273
.
12.
Dong
,
B.
,
O'Neill
,
Z.
, and
Li
,
Z.
,
2014
, “
A BIM-Enabled Information Infrastructure for Building Energy Fault Detection and Diagnostics
,”
Autom. Constr.
,
44
, pp.
197
211
.
13.
Wall
,
J.
, and
Guo
,
Y.
,
2018
, “
RP1026: Evaluation of Next-Generation Automated Fault Detection & Diagnosis (FDD) Tools for Commercial Building Energy Efficiency—Part I: FDD Case Studies in Australia
,”
Low Carbon Living
,
CRC Press
,
Boca Raton, FL
, p.
66
.
14.
Abdollahi
,
A.
,
Pattipati
,
K. R.
,
Kodali
,
A.
,
Singh
,
S.
,
Zhang
,
S.
, and
Luh
,
P. B.
,
2016
, “
Probabilistic Graphical Models for Fault Diagnosis in Complex Systems
,” Principles of Performance Reliability Modeling and Evaluation—Essays in Honor of Kishor Trivedi on His 70th Birthday (
Springer Series in Reliability Engineering
),
L.
Fiondella
and
A.
Puliafito
eds., Springer, Berlin, pp.
109
139.
15.
Hao
,
J.
,
Kang
,
J.
,
Li
,
J.
, and
Zhao
,
Z.
,
2012
, “
A Physical Model Based Research for Fault Diagnosis of Gear Crack
,”
International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
, Chengdu, China, June 15–18, pp.
572
575
.
16.
Schein
,
J.
,
Bushby
,
S. T.
,
Castro
,
N. S.
, and
House
,
J. M.
,
2006
, “
A Rule-Based Fault Detection Method for Air Handling Units
,”
Energy Build.
,
38
(
12
), pp.
1485
1492
.
17.
Cai
,
B.
,
Huang
,
L.
, and
Xie
,
M.
,
2017
, “
Bayesian Networks in Fault Diagnosis
,”
IEEE Trans. Ind. Inf.
,
13
(
5
), pp.
2227
2240
.
18.
Afram
,
A.
,
Janabi-Sharifi
,
F.
,
Fung
,
A. S.
, and
Raahemifar
,
K.
,
2017
, “
Artificial Neural Network (ANN) Based Model Predictive Control (MPC) and Optimization of HVAC Systems: A State of the Art Review and Case Study of a Residential HVAC System
,”
Energy Build.
,
141
, pp.
96
113
.
19.
Li
,
G.
, and
Hu
,
Y.
,
2019
, “
An Enhanced PCA-Based Chiller Sensor Fault Detection Method Using Ensemble Empirical Mode Decomposition Based Denoising
,”
Energy Build.
,
183
, pp.
311
324
.
20.
Beghi
,
A.
,
Cecchinato
,
L.
,
Corazzol
,
C.
,
Rampazzo
,
M.
,
Simmini
,
F.
, and
Susto
,
G. A.
,
2014
, “
A One-Class SVM Based Tool for Machine Learning Novelty Detection in HVAC Chiller Systems
,”
IFAC Proc. Vol.
,
47
(
3
), pp.
1953
1958
.
21.
van Every
,
P. M.
,
Rodriguez
,
M.
,
Jones
,
C. B.
,
Mammoli
,
A. A.
, and
Martínez-Ramón
,
M.
,
2017
, “
Advanced Detection of HVAC Faults Using Unsupervised SVM Novelty Detection and Gaussian Process Models
,”
Energy Build.
,
149
, pp.
216
224
.
22.
He
,
S.
,
Zhiwei
,
W.
,
Zhanwei
,
W.
,
Xiaowei
,
G.
, and
Zengfeng
,
Y.
,
2016
, “
Fault Detection and Diagnosis of Chiller Using Bayesian Network Classifier With Probabilistic Boundary
,”
Appl. Therm. Eng.
,
107
, pp.
37
47
.
23.
Du
,
Z.
, and
Jin
,
X.
,
2008
, “
Multiple Faults Diagnosis for Sensors in Air Handling Unit Using Fisher Discriminant Analysis
,”
Energy Convers. Manage.
,
49
(
12
), pp.
3654
3665
.
24.
Kim
,
W.
, and
Katipamula
,
S.
,
2018
, “
A Review of Fault Detection and Diagnostics Methods for Building Systems
,”
Sci. Technol. Built Environ.
,
24
(
1
), pp.
3
21
.
25.
Uusitalo
,
L.
,
2007
, “
Advantages and Challenges of Bayesian Networks in Environmental Modelling
,”
Ecol. Modell.
,
203
(
3–4
), pp.
312
318
.
26.
Taal
,
A.
,
Itard
,
L.
, and
Zeiler
,
W.
,
2018
, “
A Reference Architecture for the Integration of Automated Energy Performance Fault Diagnosis Into HVAC Systems
,”
Energy Build.
,
179
(
15
), pp.
144
155
.
27.
Wang
,
Z.
,
Wang
,
Z.
,
Gu
,
X.
,
He
,
S.
, and
Yan
,
Z.
,
2018
, “
Feature Selection Based on Bayesian Network for Chiller Fault Diagnosis From the Perspective of Field Applications
,”
Appl. Therm. Eng.
,
129
(
25
), pp.
674
683
.
28.
Zhao
,
Y.
,
Wen
,
J.
, and
Wang
,
S.-W.
,
2015
, “
Diagnostic Bayesian Networks for Diagnosing Air Handling Units Faults—Part II: Faults in Coils and Sensors
,”
Appl. Therm. Eng.
,
90
(
5
), pp.
145
157
.
29.
Zhao
,
Y.
,
Wen
,
J.
,
Xiao
,
F.
,
Yang
,
X.
, and
Wang
,
S.-W.
,
2017
, “
Diagnostic Bayesian Networks for Diagnosing Air Handling Units Faults—Part I: Faults in Dampers, Fans, Filters and Sensors
,”
Appl. Therm. Eng.
,
111
(
25
), pp.
1272
1286
.
30.
Cai
,
B.
,
Liu
,
Y.
,
Fan
,
Q.
,
Zhang
,
Y.
,
Liu
,
Z.
,
Yu
,
S.
, and
Ji
,
R.
,
2014
, “
Multi-Source Information Fusion Based Fault Diagnosis of Ground-Source Heat Pump Using Bayesian Network
,”
Appl. Energy
,
114
, pp.
1
9
.
31.
Marvin
,
H. J.
,
Bouzembrak
,
Y.
,
Janssen
,
E. M.
,
van der Zande
,
M.
,
Murphy
,
F.
,
Sheehan
,
B.
,
Mullins
,
M.
, and
Bouwmeester
,
H.
,
2017
, “
Application of Bayesian Networks for Hazard Ranking of Nanomaterials to Support Human Health Risk Assessment
,”
Nanotoxicology
,
11
(
1
), pp.
123
133
.
32.
Millán
,
E.
,
Descalço
,
L.
,
Castillo
,
G.
,
Oliveira
,
P.
, and
Diogo
,
S.
,
2013
, “
Using Bayesian Networks to Improve Knowledge Assessment
,”
Comput. Educ.
,
60
(
1
), pp.
436
447
.
33.
Shute
,
V.
, and
Wang
,
L.
,
2016
, “
Assessing and Supporting Hard-to-Measure Constructs in Video Games
,”
The Wiley Handbook of Cognition and Assessment: Frameworks, Methodologies, and Applications
,
Wiley
,
Hoboken, NJ
, pp.
535
562
.
34.
Lyons
,
D. M.
,
Arkin
,
R. C.
,
Jiang
,
S.
,
O'Brien
,
M.
,
Tang
,
F.
, and
Tang
,
P.
,
2017
, “
Performance Verification for Robot Missions in Uncertain Environments
,”
Rob. Auton. Syst.
,
98
, pp.
89
104
.
35.
Ju
,
Z.
,
Ji
,
X.
,
Li
,
J.
, and
Liu
,
H.
,
2017
, “
An Integrative Framework of Human Hand Gesture Segmentation for Human-Robot Interaction
,”
IEEE Syst. J.
,
11
(
3
), pp.
1326
1336
.
36.
Slanzi
,
D.
, and
Poli
,
I.
,
2014
, “
Evolutionary Bayesian Network Design for High Dimensional Experiments
,”
Chemom. Intell. Lab. Syst.
,
135
(
15
), pp.
172
182
.
37.
Taylor
,
D.
,
Biedermann
,
A.
,
Hicks
,
T.
, and
Champod
,
C.
,
2018
, “
A Template for Constructing Bayesian Networks in Forensic Biology Cases When Considering Activity Level Propositions
,”
Forensic Sci. Int.: Genet.
,
3
, pp.
136
146
.
38.
Szkuta
,
B.
,
Ballantyne
,
K. N.
,
Kokshoorn
,
B.
, and
van Oorscho
,
R. A. H.
,
2018
, “
Transfer and Persistence of Non-Self DNA on Hands Over Time: Using Empirical Data to Evaluate DNA Evidence Given Activity Level Propositions
,”
Forensic Sci. Int.: Genet.
,
33
, pp.
84
97
.
39.
Bae
,
S.-C.
, and
Lee
,
Y. H.
,
2018
, “
Comparative Efficacy and Tolerability of Monotherapy With Leflunomide or Tacrolimus for the Treatment of Rheumatoid Arthritis: A Bayesian Network Meta-Analysis of Randomized Controlled Trials
,”
Clin. Rheumatol.
,
37
(
2
), pp.
323
330
.
40.
Chiremsel
,
Z.
,
Nait Said
,
R.
, and
Chiremsel
,
R.
,
2016
, “
Probabilistic Fault Diagnosis of Safety Instrumented Systems Based on Fault Tree Analysis and Bayesian Network
,”
J. Failure Anal. Prev.
,
16
(
5
), pp.
747
760
.
41.
Sousa
,
H. S.
,
Prieto-Castrillo
,
F.
,
Matos
,
J. C.
,
Branco
,
J. M.
, and
Lourenço
,
P. B.
,
2018
, “
Combination of Expert Decision and Learned Based Bayesian Networks for Multi-Scale Mechanical Analysis of Timber Elements
,”
Expert Syst. Appl.
,
93
(
1
), pp.
156
168
.
42.
Bishop
,
C. M.
,
2006
,
Pattern Recognition and Machine Learning
,
Springer
,
Berlin
, p.
738
.
43.
Dondelinger
,
F.
,
Lèbre
,
S.
, and
Husmeier
,
D.
,
2013
, “
Non-Homogeneous Dynamic Bayesian Networks With Bayesian Regularization for Inferring Gene Regulatory Networks With Gradually Time-Varying Structure
,”
Mach. Learn.
,
90
(
2
), pp.
191
230
.
44.
Kwisthout
,
J.
,
2018
, “
Approximate Inference in Bayesian Networks: Parameterized Complexity Results
,”
Int. J. Approximate Reasoning
,
93
, pp.
119
131
.
45.
Wu
,
P. P.-Y.
,
Julian Caley
,
M.
,
Kendrick
,
G. A.
,
McMahon
,
K.
, and
Mengersen
,
K.
,
2018
, “
Dynamic Bayesian Network Inferencing for Non-Homogeneous Complex Systems
,”
J. R. Stat. Soc. Ser. C: Appl. Stat.
,
67
(
2
), pp.
417
434
.
46.
Black
,
A.
,
Korb
,
K. B.
, and
Nicholson
,
A. E.
,
2014
, “
Intrinsic Learning of Dynamic Bayesian Networks
,”
PRICAI 2014: Trends in Artificial Intelligence
(
Lecture Notes in Computer Science
, Vol.
8862)
,
Springer, Berli
n, pp.
256
269
.
47.
Hu
,
M.
,
Chen
,
H.
,
Shen
,
L.
,
Li
,
G.
,
Guo
,
Y.
,
Li
,
H.
,
Li
,
J.
, and
Hu
,
W.
,
2018
, “
A Machine Learning Bayesian Network for Refrigerant Charge Faults of Variable Refrigerant Flow Air Conditioning System
,”
Energy Build.
,
158
, pp.
668
676
.
48.
Lin
,
S.
,
Chen
,
X.
, and
Wang
,
Q.
,
2018
, “
Fault Diagnosis Model Based on Bayesian Network Considering Information Uncertainty and Its Application in Traction Power Supply System
,”
IEEJ Trans. Electr. Electron. Eng.
,
13
(
5
), pp.
671
680
.
49.
Verron
,
S.
,
2007
, “
Diagnosis and Monitoring of Complex Processes Via Bayesian Networks
,” Ph.D. thesis, University of Angers, Angers, France.
50.
Pillet
,
M.
,
Boukar
,
A.
,
Pairel
,
E.
,
Rizzon
,
B.
,
Boudaoud
,
N.
, and
Cherfi
,
Z.
,
2013
, “
Multivariate SPC for Total Inertial Tolerancing
,”
Int. J. Metrol. Qual. Eng.
,
4
(
3
), pp.
169
175
.
51.
Caucheteux
,
A.
,
Sabar
,
A. E.
, and
Boucher
,
V.
,
2013
, “
Occupancy Measurement in Building: A Literature Review, Application on an Energy Efficiency Research Demonstration Building
,”
Int. J. Metrol. Qual. Eng.
,
4
(
2
), pp.
135
144
.
52.
ASHRAE
,
2014
, “
ASHRAE Guideline 14 for Measurement of Energy and Demand Savings
,” American Society of Heating, Refrigeration and Air Conditioning Engineers, Atlanta, GA.
53.
EVO
,
2012
, “
International Performance Measurement and Verification Protocol: Concepts and Options for Determining Energy and Water Savings
,”
Efficiency Valuation Organization
,
Toronto, ON, Canada
, Report No. EVO 10000 − 1:2012.
54.
WMO
,
2008
,
Guide to Meteorological Instruments and Methods of Observation
,
7th ed.
,
World Meteorological Organization
,
Geneva, Switzerland
.
55.
ASHRAE
,
2017
, “
Thermal Environmental Conditions for Human Occupancy
,” American Society of Heating, Refrigeration and Air Conditioning Engineers, Atlanta, GA, Standard No. 55.
56.
Caucheteux
,
A.
,
Gautier
,
A.
, and
Lahrech
,
R.
,
2016
, “
A Metamodel-Based Methodology for an Energy Savings Uncertainty Assessment of Building Retrofitting
,”
Int. J. Metrol. Qual. Eng.
,
7
(
4
), p.
402
.
57.
Calì
,
D.
,
Matthes
,
P.
,
Huchtemann
,
K.
,
Streblow
,
R.
, and
Müller
,
D.
,
2015
, “
CO2 Based Occupancy Detection Algorithm: Experimental Analysis and Validation for Office and Residential Buildings
,”
Building Environ.
,
86
, pp.
39
49
.
58.
Ansanay-Alex
,
G.
,
Abdelouadoud
,
Y.
, and
Schetelat
,
P.
,
2016
, “
Statistical and Stochastic Modelling of French Households and Their Energy Consuming Activities
,”
12th REHVA World Congress-CLIMA
,
Aalborg, Denmark
,
May 22–25
, Paper No.
385
.https://www.researchgate.net/publication/301684851_Statistical_and_Stochastic_Modelling_of_French_Households_and_Their_Energy_Consuming_Activities
59.
Yan
,
D.
,
O'Brien
,
W.
,
Hong
,
T.
,
Feng
,
X.
,
Gunay
,
H. B.
,
Tahmasebi
,
F.
, and
Mahdavi
,
A.
,
2015
, “
Occupant Behavior Modeling for Building Performance Simulation: Current State and Future Challenges
,”
Energy Build.
,
107
, pp.
264
278
.
60.
Murphy
,
K.
,
2001
, “
The Bayesian Network Toolbox for Matlab
,” University of California, Berkeley, CA, accessed Dec. 18, 2018, https://www.cs.ubc.ca/~murphyk/Papers/bnt.pdf
61.
Lauritzen
,
S. L.
,
1992
, “
Propagation of Probabilities, Means and Variances in Mixed Graphical Association Models
,”
J. Am. Stat. Assoc.
,
87
(
420
), pp.
1098
1108
.
62.
Lauritzen
,
S. L.
, and
Jensen
,
F.
,
2001
, “
Stable Local Computation With Conditional Gaussian Distributions
,”
Stat. Comput.
,
11
(
2
), pp.
191
203
.
63.
Sachs
,
K.
,
Perez
,
O.
,
Pe'er
,
D.
,
Lauffenburger
,
D. A.
, and
Nolan
,
G. P.
,
2005
, “
Causal Protein-Signaling Networks Derived From Multiparameter Single-Cell Data
,”
Science
,
308
(
5721
), pp.
523
529
.
64.
Claeskens
,
G.
, and
Hjort
,
N. L.
,
2008
,
Model Selection and Model Averaging
(Part of Cambridge Series in Statistical and Probabilistic Mathematics),
Cambridge Press
,
Cambridge, UK
, p.
332
.
65.
Ghahramani
,
Z.
,
2001
, “
An Introduction to Hidden Markov Models and Bayesian Networks
,”
Int. J. Pattern Recognit. Artif. Intell.
,
15
(
1
), pp.
9
42
.
66.
Vlachopoulou
,
M.
,
Chin
,
G.
,
Fulle
,
J.
, and
Lu
,
S.
,
2014
, “
Aggregated Residential Load Modeling Using Dynamic Bayesian Networks
,”
IEEE International Conference on Smart Grid Communications
(
SmartGridComm
), Venice, Italy, Nov. 3–6, pp.
818
823
.
67.
Ghahramani
,
A.
,
Tang
,
C.
,
Yang
,
Z.
, and
Becerik-Gerber
,
B.
,
2015
, “
A Study of Time-Dependent Variations in Personal Thermal Comfort Via a Dynamic Bayesian Network
,”
First International Symposium on Sustainable Human-Building Ecosystems
, Pittsburgh, PA, Oct. 5–6, pp.
99
107
.https://www.researchgate.net/publication/283083710_A_Study_of_Time-Dependent_Variations_in_Personal_Thermal_Comfort_via_a_Dynamic_Bayesian_Network
68.
Markovic
,
R.
,
Wolf
,
S.
,
Cao
,
J.
,
Spinnräker
,
E.
,
Wölki
,
D.
,
Frisch
,
J.
, and
van Treeck
,
C.
,
2017
, “
Comparison of Different Classification Algorithms for the Detection of User's Interaction With Windows in Office Buildings
,”
International Conference on Future Buildings and Districts—Energy Efficiency From Nano to Urban Scale
(
CISBAT
), Lausanne, Switzerland, Sept. 6–8, pp.
337
342
.http://orbit.dtu.dk/ws/files/139843510/1_s2.0_S1876610217329375_main.pdf
69.
Cai
,
B.
,
Liu
,
Y.
,
Ma
,
Y.
,
Huang
,
L.
, and
Liu
,
Z.
,
2015
, “
A Framework for the Reliability Evaluation of Grid-Connected Photovoltaic Systems in the Presence of Intermittent Faults
,”
Energy
,
93
, pp.
1308
1320
.
70.
Hastie
,
T.
,
Efron
,
B.
,
2012
, “
Lars: Least Angle Regression, Lasso and Forward Stage Wise. R Package Version 1.1
,” Stanford, CA, accessed June 8, 2019, https://cran.r-project.org/web/packages/lars/index.html
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