Home Page Icon
Home Page
Table of Contents for
Table of Contents
Close
Table of Contents
by Christophe Simon, Philippe Weber
Benefits of Bayesian Network Models
Cover
Title
Copyright
Foreword by J.-F. Aubry
Foreword by L. Portinale
Acknowledgments
Introduction
I.1. Problem statement
I.2. Book structure
PART 1: Bayesian Networks
1 Bayesian Networks: a Modeling Formalism for System Dependability
1.1. Probabilistic graphical models: BN
1.2. Reliability and joint probability distributions
1.3. Discussion and conclusion
2 Bayesian Network: Modeling Formalism of the Structure Function of Boolean Systems
2.1. Introduction
2.2. BN models in the Boolean case
2.3. Standard Boolean gates CPT
2.4. Non-deterministic CPT
2.5. Industrial applications
2.6. Conclusion
3 Bayesian Network: Modeling Formalism of the Structure Function of Multi-State Systems
3.1. Introduction
3.2. BN models in the multi-state case
3.3. Non-deterministic CPT
3.4. Industrial applications
3.5. Conclusion
PART 2: Dynamic Bayesian Networks
4 Dynamic Bayesian Networks: Integrating Environmental and Operating Constraints in Reliability Computation
4.1. Introduction
4.2. Component modeled by a DBN
4.3. Model of a dynamic multi-state system
4.4. Discussion on dependent processes
4.5. Conclusion
5 Dynamic Bayesian Networks: Integrating Reliability Computation in the Control System
5.1. Introduction
5.2. Integrating reliability information into the control
5.3. Control integrating reliability modeled by DBN
5.4. Application to a drinking water network
5.5. Conclusion
5.6. Acknowledgments
Conclusion
Bibliography
Index
End User License Agreement
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Prev
Previous Chapter
Cover
Next
Next Chapter
Title
Table of Contents
Cover
Title
Copyright
Foreword by J.-F. Aubry
Foreword by L. Portinale
Acknowledgments
Introduction
I.1. Problem statement
I.2. Book structure
PART 1: Bayesian Networks
1 Bayesian Networks: a Modeling Formalism for System Dependability
1.1. Probabilistic graphical models: BN
1.2. Reliability and joint probability distributions
1.3. Discussion and conclusion
2 Bayesian Network: Modeling Formalism of the Structure Function of Boolean Systems
2.1. Introduction
2.2. BN models in the Boolean case
2.3. Standard Boolean gates CPT
2.4. Non-deterministic CPT
2.5. Industrial applications
2.6. Conclusion
3 Bayesian Network: Modeling Formalism of the Structure Function of Multi-State Systems
3.1. Introduction
3.2. BN models in the multi-state case
3.3. Non-deterministic CPT
3.4. Industrial applications
3.5. Conclusion
PART 2: Dynamic Bayesian Networks
4 Dynamic Bayesian Networks: Integrating Environmental and Operating Constraints in Reliability Computation
4.1. Introduction
4.2. Component modeled by a DBN
4.3. Model of a dynamic multi-state system
4.4. Discussion on dependent processes
4.5. Conclusion
5 Dynamic Bayesian Networks: Integrating Reliability Computation in the Control System
5.1. Introduction
5.2. Integrating reliability information into the control
5.3. Control integrating reliability modeled by DBN
5.4. Application to a drinking water network
5.5. Conclusion
5.6. Acknowledgments
Conclusion
Bibliography
Index
End User License Agreement
List of Tables
1 Bayesian Networks: a Modeling Formalism for System Dependability
Table 1.1.
Generic definition of a conditional probability table
Table 1.2.
Probability distributions of component states
Table 1.3.
Joint probability distributions modeling the three-valve system, part 1
Table 1.4.
Joint probability distributions modeling the three-valve system, part 2
Table 1.5.
Probability distributions of E
1
states
Table 1.6.
Probability distributions of E
2
states
Table 1.7.
Probability distributions of y states
2 Bayesian Network: Modeling Formalism of the Structure Function of Boolean Systems
Table 2.1.
Probability distribution of valves’ states
Table 2.2.
CPT of cut-set C
2
Table 2.3.
CPT of y based on cut-sets
Table 2.4.
CPT of L
1
Table 2.5.
CPT of L
2
Table 2.6.
CPT of y|L
1
, L
2
Table 2.7.
Probability distribution on y state
Table 2.8.
CPT of a Boolean AND
Table 2.9.
CPT of a Boolean OR
Table 2.10.
CPT of the inhibitor of E
2
= x
2
∧ x
3
by B
1
in a bowtie model
Table 2.11.
CPT of the inhibitor of I
p1
by B
2
in a bowtie model
Table 2.12.
CPT of a Boolean 2-out-of-3:G system
Table 2.13.
CPT of the C
i
variables
Table 2.14.
CPT of y in a linear consecutive-2-out-of-5:G
Table 2.15.
CPT of y in a linear consecutive-2-out-of-5:G
3 Bayesian Network: Modeling Formalism of the Structure Function of Multi-State Systems
Table 3.1.
Multi-state L
13
tie-set
Table 3.2.
Multi-state L
24
tie-set
Table 3.3.
Multi-state L
567
tie-set
Table 3.4.
Results of the computation based on multi-state and tie-sets
Table 3.5.
Multi-state C
1
tie-set
Table 3.6.
Multi-state C
2
tie-set
Table 3.7.
Multi-state C
3
tie-set
Table 3.8.
Multi-state C
4
tie-set
Table 3.9.
Results of the computation based on multi-state and cut-sets
Table 3.10.
Variables in the IDEF0 model representing the flow F(i)
Table 3.11.
Results of the computation based on the IDEF0 model
Table 3.12.
Results of the computation based on the IDEF0 model L
i
variables
Table 3.13.
Results of the computation based on the IDEF0 model I
i
variables
Table 3.14.
CPT of HSB efficiency
4 Dynamic Bayesian Networks: Integrating Environmental and Operating Constraints in Reliability Computation
Table 4.1.
CPT defining the transition probability matrix of a MC
5 Dynamic Bayesian Networks: Integrating Reliability Computation in the Control System
Table 5.1.
Failure rates of the actuators
Table 5.2.
Paths linking the sources to the demand point
Table 5.3.
Variables Computing the actuator Weighting
List of Illustrations
1 Bayesian Networks: a Modeling Formalism for System Dependability
Figure 1.1.
Bayesian network model
Figure 1.2.
Multi-state system with three valves
Figure 1.3.
Multi-state three-valve system with two stages
2 Bayesian Network: Modeling Formalism of the Structure Function of Boolean Systems
Figure 2.1.
RBD of the flow distribution system
Figure 2.2.
BN model for three cut-sets
Figure 2.3.
BN model of two minimal cut-sets
Figure 2.4.
BN modeling the two minimal tie-sets
Figure 2.5.
FT of the flow distribution system
Figure 2.6.
BN model of the FT of Figure 2.5
Figure 2.7.
BN model of a bowtie and its barriers
Figure 2.8.
BN model of the 2-out-of-3:G system
Figure 2.9.
BN model of the linear consecutive-2-out-of-5:G system
Figure 2.10.
BN model of the circular consecutive-2-out-of-5:G system
Figure 2.11.
Noisy-OR structures
Figure 2.12.
Leaky Noisy-OR structures
Figure 2.13.
Structuration in organizational level and action phases relating to a bowtie model
Figure 2.14.
RB unified model of the power plant risk
3 Bayesian Network: Modeling Formalism of the Structure Function of Multi-State Systems
Figure 3.1.
BN structured by the minimal multi-state tie-sets
Figure 3.2.
Compact BN structured by minimal tie-sets for a multi-state system
Figure 3.3.
BN based on the minimal cut-sets of a multi-state system
Figure 3.4.
Generic definition of a function and its flows
Figure 3.5.
Generic BN pattern of a function
Figure 3.6.
Functional model of the system
Figure 3.7.
Model of the function (transfer the fluid)
Figure 3.8.
Model of the function (circulate the fluid)
Figure 3.9.
Model of the function (stop the fluid)
Figure 3.10.
BN model mapped from the functional model of the system
Figure 3.11.
BN model of a human safety barrier (HSB)
4 Dynamic Bayesian Networks: Integrating Environmental and Operating Constraints in Reliability Computation
Figure 4.1.
DBN model developed over eight time slices
Figure 4.2.
DBN of a MC
Figure 4.3.
DBN of a non-homogeneous MC
Figure 4.4.
Inference in the DBN of a non-homogeneous MC
Figure 4.5.
DBN model of a MSM
Figure 4.6.
DBN model of an IOHMM
Figure 4.7.
Inference in the DBN model of the IOHMM
Figure 4.8.
Unroll up the DBN model without conditional dependence between components
Figure 4.9.
Unroll up the DBN model with conditional dependence between components
Figure 4.10.
2TBN of a multi-state system
Figure 4.11.
Inference of a 2TBN multi-state model and state probability distribution
Figure 4.12.
Multi-state system and components’ reliability
Figure 4.13.
2TBN model of a multi-state system with largely dependent processes
5 Dynamic Bayesian Networks: Integrating Reliability Computation in the Control System
Figure 5.1.
Control structure of an over-actuated system integrating a reliability model
Figure 5.2.
Control framework of an over-actuated system integrating a DBN reliability model
Figure 5.3.
Part of the Barcelona DWN studied
Figure 5.4.
DBN model of the Barcelona DWN
Figure 5.5.
Actuators and DWN reliability
Figure 5.6.
Simulation of control inputs and weights of the Barcelona DWN
Guide
Cover
Table of Contents
Begin Reading
Pages
C1
iii
iv
v
ix
x
xi
xiii
xiv
xv
xvii
xviii
xix
xx
xxi
xxii
xxiii
1
3
4
5
6
7
8
9
10
11
12
13
14
15
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
65
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
101
102
103
104
105
106
107
108
109
110
111
112
113
114
G1
G2
G3
G4
G5
G6
G7
G8
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset