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Systems Dependability Assessment


Systems Dependability Assessment

Benefits of Petri Net Models
1. Aufl.

von: Jean-Francois Aubry, Nicolae Brinzei, Mohammed-Habib Mazouni

139,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 11.02.2016
ISBN/EAN: 9781119262107
Sprache: englisch
Anzahl Seiten: 288

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Beschreibungen

<p>Petri Nets were defined for the study of discrete events systems and later extended for many purposes including dependability assessment. In our knowledge, no book deals specifically with the use of different type of PN to dependability. We propose in addition to bring a focus on the adequacy of Petri net types to the study of various problems related to dependability such as risk analysis and probabilistic assessment.</p> <p>In the first part, the basic models of PN and some useful extensions are briefly recalled. In the second part, the PN are used as a formal model to describe the evolution process of critical system in the frame of an ontological approach. The third part focuses on the stochastic Petri Nets (SPN) and their use in dependability assessment. Different formal models of SPN are formally presented (semantics, evolution rules…) and their equivalence with the corresponding class of Markov processes to get an analytical assessment of dependability. Simplification methods are proposed in order to reduce the size of analytical model and to make it more calculable. The introduction of some concepts specific to high level PN allows too the consideration of complex systems. Few applications in the field of the instrumentation and control (l&C) systems, safety integrated systems (SIS) emphasize the benefits of SPN for dependability assessment.</p>
<p>Introduction xi</p> <p><b>Part 1 Short Review of Petri Net Modeling 1</b></p> <p>Introduction to Part 1 3</p> <p><b>Chapter 1 Autonomous Petri Nets 5</b></p> <p>1.1 Unmarked Petri nets 5</p> <p>1.1.1 Definitions 5</p> <p>1.1.2 Drawing 6</p> <p>1.1.3 Other definitions 7</p> <p>1.2 Marking of a PN 7</p> <p>1.2.1 Order relation on markings 8</p> <p>1.2.2 Enabled transition 9</p> <p>1.3 Dynamics of autonomous PNs 9</p> <p>1.3.1 Firing of a transition 9</p> <p>1.3.2 Transition matrix 11</p> <p>1.3.3 Firing sequence 11</p> <p>1.3.4 Reachable marking 12</p> <p>1.3.5 Fundamental equation 12</p> <p>1.3.6 Properties of PN 14</p> <p>1.3.7 Other properties 14</p> <p>1.3.8 Invariants in a PN 15</p> <p>1.3.9 Reachability graph 16</p> <p><b>Chapter 2 Petri Nets and Event Languages 19</b></p> <p>2.1 Labeled PNs 19</p> <p>2.1.1 Formal definition 19</p> <p>2.1.2 Generated and marked languages 20</p> <p>2.2 Example 21</p> <p><b>Chapter 3 Comparison Petri Nets –</b></p> <p>Finite State Automaton 25</p> <p>3.1 Language expression 26</p> <p>3.2 Building of the models 27</p> <p>3.2.1 Synchronization of submodels 28</p> <p>3.2.2 Resource sharing 29</p> <p>3.2.3 Construction by refinement 30</p> <p>3.3 Compactness of the model 32</p> <p><b>Chapter 4 Some Extensions of Petri Nets 35</b></p> <p>4.1 PN with inhibitor arcs 35</p> <p>4.2 Timed PN 36</p> <p>4.2.1 P-timed Petri nets 37</p> <p>4.2.2 T-timed Petri nets 37</p> <p>4.3 Synchronized PN 38</p> <p>4.4 Timed synchronized PN 40</p> <p>4.5 Interpreted PN 41</p> <p>4.6 Colored PN 42</p> <p>4.6.1 Introduction example 42</p> <p>4.6.2 Formal definition 45</p> <p>4.6.3 A dedicated software CPN Tools 46</p> <p>Conclusion to Part 1 51</p> <p><b>Part 2 A Formal Approach to Risk Assessment 53</b></p> <p>Introduction to Part 2 51</p> <p><b>Chapter 5 Ontology-based Accidental Process 61</b></p> <p>5.1 Preliminary definitions 61</p> <p>5.2 Elementary entities: HSE and VTE 63</p> <p>5.2.1 Hazard supplier entity (HSE) 63</p> <p>5.2.2 Vulnerable target entity (VTE) 63</p> <p>5.3 Elementary situations and elementary events 64</p> <p>5.3.1 State versus situation 64</p> <p>5.3.2 Initial situation (IS) 64</p> <p>5.3.3 Initiating event (IEv) 64</p> <p>5.3.4 Hazard situation (HS) 65</p> <p>5.3.5 Exposure event (EEv) 65</p> <p>5.3.6 Exposure situation (ES) 65</p> <p>5.3.7 Accident situation 65</p> <p>5.3.8 Hazardous (feared) event (HEv) 65</p> <p>5.4 Conclusion 66</p> <p><b>Chapter 6 Petri Net Modeling of the</b></p> <p>Accidental Process 67</p> <p>6.1 Elementary process 68</p> <p>6.2 Sequence of elementary processes 71</p> <p>6.3 Modeling the action of a safety barrier 71</p> <p>6.4 Modeling of a cumulative process 73</p> <p>6.5 PN as a support for risk assessment 75</p> <p>6.5.1 Modeling of the damage 75</p> <p>6.5.2 Modeling of the event frequencies 75</p> <p>6.5.3 CPN Tools implementation 77</p> <p>6.5.4 Evaluation rule of the risk 83</p> <p>6.6 Conclusion 86</p> <p><b>Chapter 7 Illustrative Example 87</b></p> <p>7.1 Functional description 87</p> <p>7.2 Building of an accidental process 88</p> <p>7.2.1 First elementary process 88</p> <p>7.2.2 Second elementary process 91</p> <p>7.2.3 Parallel process 92</p> <p>7.2.4 The whole model 92</p> <p>7.3 Conclusion 94</p> <p><b>Chapter 8 Design and Safety Assessment Cycle 95</b></p> <p>8.1 Five essential steps 95</p> <p>8.2 Ontological interest 98</p> <p>Conclusion to Part 2 101</p> <p><b>Part 3 Stochastic Petri Nets 103</b></p> <p>Introduction to Part 3 105</p> <p><b>Chapter 9 Basic Concept 107</b></p> <p>9.1 Introductory example 107</p> <p>9.2 Formal definition 108</p> <p><b>Chapter 10 Semantics, Properties and</b></p> <p>Evolution Rules of an SPN 111</p> <p>10.1 Conservatism properties 112</p> <p>10.1.1 Conservatism of the mean marking in steady state 112</p> <p>10.1.2 Conservatism of the flow in steady state 113</p> <p>10.2 Mean sojourn time in a place of a SPN 113</p> <p>10.3 Equivalent Markov process 114</p> <p>10.4 Example of SPN for systems dependability modelling and assessment 116</p> <p><b>Chapter 11 Simplification of Complex Models 121</b></p> <p>11.1 Introduction 121</p> <p>11.2 System modeling 122</p> <p>11.3 Presentation of the quantitative analysis method 124</p> <p>11.3.1 Steps to obtain an aggregated Markov graph 124</p> <p>11.3.2 Toward a direct establishment of a reduced Markov graph 137</p> <p>11.4 Example 137</p> <p>11.4.1 Failure modeling 138</p> <p>11.4.2 Study of the different functional and hardware solutions 139</p> <p>11.4.3 Evaluation of the weighting coefficients from the Petri nets 144</p> <p>11.4.4 Conclusion 147</p> <p><b>Chapter 12 Extensions of SPN 149</b></p> <p>12.1 Introduction 149</p> <p>12.2 Relationship between stochastic Petri nets and stochastic processes 150</p> <p>12.3 The transition firing policy 151</p> <p>12.4 Associated stochastic processes 151</p> <p>12.4.1 Temporal memory based on resampling 152</p> <p>12.4.2 Temporal memory based on age memory or on enabling memory 153</p> <p>12.4.3 Stochastic process underlying a stochastic PN 154</p> <p>12.4.4 Embedded Markov chain of the stochastic process 157</p> <p>12.4.5 Application to a case study 159</p> <p>12.5 Synchronization problem in generalized stochastic Petri nets 162</p> <p>12.5.1 GSPN with internal synchronization 162</p> <p>12.5.2 SPN with predicates and assertions 164</p> <p>12.6 Conclusion 168</p> <p><b>Part 4 Applications of Stochastic Petri Nets to Assessment Problems in Industrial Systems 169</b></p> <p>Introduction to Part 4 171</p> <p><b>Chapter 13 Application in Dynamic Reliability 175</b></p> <p>13.1 Presentation of the system and hypothesis 175</p> <p>13.2 System modeling with Petri net 177</p> <p>13.3 Methodology application 179</p> <p>13.4 Construction of an aggregated Markov graph 180</p> <p>13.5 Conclusion 185</p> <p><b>Chapter 14 Classical Dependability Assessment 187</b></p> <p>14.1 Availability study of a nuclear power plant subsystem 187</p> <p>14.1.1 CPN modeling 188</p> <p>14.1.2 Reliability and dependability assessment 192</p> <p>14.1.3 Conclusion 196</p> <p>14.2 Common causes failures in nuclear plants (safety oriented) 197</p> <p>14.2.1 The Atwood model 197</p> <p>14.2.2 Case study 199</p> <p>14.2.3 Probabilistic dependability assessment 208</p> <p>14.2.4 Conclusion 212</p> <p><b>Chapter 15 Impact of Failures on System Performances 213</b></p> <p>15.1 Reliability evaluation of networked control system 213</p> <p>15.1.1 Statement of the problem 213</p> <p>15.1.2 Reliability criteria of an NCS 215</p> <p>15.1.3 Elements of modeling 216</p> <p>15.1.4 Simulation and results 225</p> <p>15.1.5 Evaluation of reliability 230</p> <p>15.1.6 Conclusion 230</p> <p>15.2 Railway signaling 231</p> <p>15.2.1 Introduction 231</p> <p>15.2.2 Interest 233</p> <p>15.2.3 Signaling system specifications 234</p> <p>15.2.4 Elements to be modeled 235</p> <p>15.2.5 Architecture of the model 236</p> <p>15.2.6 Example of an elementary model 237</p> <p>15.2.7 Incident generation 239</p> <p>15.2.8 Results 239</p> <p>15.2.9 Conclusion 242</p> <p>Conclusion 245</p> <p>Appendix 247</p> <p>Bibliography 251</p> <p>Index 261</p>
<strong>Pr. Dr. Jean-Francois AUBRY</strong>, Professor Emeritus, University of Lorraine, France. <p><strong>Dr. Nicolae BRINZEI</strong>, Associate University of Lorraine. <p><strong>Dr. Mohammed-Habib MAZOUNI</strong>, Engineer.

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