Ontology Modeling in Physical Asset Integrity Management (eBook)

Drs. Vahid Ebrahimipour and Soumaya Yacout are Professors in the Department of Industrial Engineering and Math at the École Polytechnique de Montréal.

Dedication 6
Preface 8
Introduction 8
Readers 9
Organization of Topics 9
Special Thanks 12
Biography 14
Contents 16
Contributors 18
Chapter 1: ISO 15926 20
1.1 Introduction 20
1.2 The ISO15926 21
1.2.1 Why ISO15926 21
1.2.2 The History 22
1.3 Part 2: The Data Model 23
1.4 Part 4: The Initial Reference Data 23
1.5 Part 7: The Template Methodology 25
1.6 Reference Data 27
1.7 Object Information Models (OIM) 29
1.8 Part 8: The Implementation 29
1.8.1 RDF and OWL 29
1.8.2 Data Flow 29
1.8.3 Storage 30
1.8.4 Part 9: The Façade and SPARQL 30
1.8.5 Peer-to-Peer Interfacing 30
Appendix A: Life-Cycle Activities 31
General 31
Requirements 31
Front-End Engineering and Process Design 31
Detailed Engineering and Plant Design 32
Procurement and Subcontracting 32
Manufacturing and Assembling 32
Prefabrication 33
Construction and Precommissioning 33
Handover and Commissioning 33
Operations 33
Feedstock Acquisition 34
Product Sales 34
Maintenance 34
Decommissioning and Disassembling 34
Performance Analysis 34
Definition of Modification Requirements 34
Demolishment and Disposal 35
References 35
Chapter 2: Ontological Analysis and Engineering Standards: An Initial Study of IFC 36
2.1 Introduction 36
2.2 Ontology and Ontological Analysis 38
2.2.1 The Dolce Foundational Ontology 39
2.3 Industry Foundation Classes 41
2.4 Ontologizing IFC 45
2.4.1 State of the Art 45
2.4.2 From EXPRESS to OWL 46
2.5 Types and Occurrences in IFC: An Ontological Analysis 51
2.6 Properties in IFC Ontologies 54
2.7 Conclusion and Further Discussion 58
References 59
Chapter 3: FMEA, HAZID, and Ontologies 63
3.1 What Is FMEA 63
3.1.1 Why FMEA 63
3.1.2 FMEA: What Is the Problem 64
3.1.3 How to Do the FMEA 64
3.1.3.1 The FMEA Process 64
3.1.3.2 Component-Based FMEA 65
3.1.3.3 Functional FMEA 68
3.1.3.4 The FMEDA 68
3.1.4 Prioritizing 69
3.1.5 How Can We Use the FMEA Results 69
3.1.6 Failure Modes 71
3.1.6.1 Why Generic Failure Modes 71
3.1.6.2 The CESAR Results 71
3.1.6.3 The NRC Results 72
3.1.6.4 Operator Failure Modes 73
3.1.7 FMEA for the System 75
3.2 What Is HazId 75
3.2.1 HazId as an Alternative Approach 75
3.2.2 HazId Based on Functions 76
3.2.3 HazId Based on Components 76
3.3 The System’s Environment 78
3.3.1 Generic Fault Trees 78
3.3.2 Environmental Hazard Lists 79
3.3.3 Combining FMEAs 80
3.3.4 Cause–Consequence Diagrams 80
3.4 What Is an Ontology 84
3.4.1 On Ontologies 84
3.4.2 How to Build an Ontology 84
3.4.3 Why Do We Need Ontologies in FMEA 86
3.4.4 The Component Ontology 88
3.4.5 System Ontologies 89
3.4.6 Ontologies and Failure Propagation 90
3.4.7 A Simple Control Loop Example 91
3.4.7.1 System Description 91
3.4.7.2 Using FMEA and Ontology Information 92
3.4.7.3 Using the Can–Cause Construct 94
3.4.8 The Steam Boiler Ontology Example 96
3.4.8.1 The System 96
3.4.8.2 Component-Based FMEA: From Scratch 97
3.4.8.3 Component-Based FMEA: With Reuse 97
3.4.8.4 Detailed Analysis of the Water Level Control 99
3.5 What Is the Ontology Good at and Where Are Humans Better 101
3.5.1 The Need for a Tool 101
3.5.2 Fitts’ List 102
References 103
Chapter 4: Ontology Development and Optimization for Data Integration and Decision-Making in Product Design and Obsolescence Management 104
4.1 Introduction 104
4.1.1 Product Design and Obsolescence Problem 104
4.1.2 Current Status of Product Design and Obsolescence Management 105
4.1.3 Ontology and Its Utilization 106
4.2 Framework and Work Process 108
4.3 Ontology Development, Optimization, and Utilization for Data Integration 109
4.3.1 Ontology-Based Data Integration 109
4.3.2 Ontology Development 111
4.3.3 Ontology Optimization 113
4.3.3.1 Structures of Ontologies 113
4.3.3.2 Relations Between Ontologies 115
Semantic Relation 115
Path Relation 117
4.3.3.3 Ontology Clustering 119
4.3.4 Ontology Utilization 119
4.3.4.1 Ontology-Based Data Integration 119
Framework of Ontology-Based Data Integration 119
Ontologies and Work Process for Ontology-Enabled Data Inquiry 121
4.3.4.2 Ontology-Based Product Design 123
4.4 Case Studies 125
4.4.1 Ontology-Based Decision-Making Support in Product Design 125
4.4.1.1 Ontology-Based Product Parameter Adjustment 126
4.4.1.2 Ontology-Based Material and Machine Selection 128
4.4.2 Ontology-Based Knowledge Representation and Decision Support for Managing Product Obsolescence 132
4.4.2.1 Ontology Representation for Obsolescence Knowledge 132
4.4.2.2 Knowledge Base for Obsolescence Management 134
4.4.2.3 Framework of Obsolescence Management Information System 140
4.4.2.4 Obsolescence Management Cost Analysis 140
4.5 Conclusions and Future Work 146
References 147
Chapter 5: Fault Diagnosis System Based on Ontology for Fleet Case Reused 150
5.1 Introduction 150
5.2 Context of Diagnosis of Complex System 151
5.3 PHM Vs. Fleet-Wide Approach 153
5.3.1 Fleet Integrated PHM Review 153
5.3.2 Predictive Diagnosis Using Fleet-Wide Knowledge 154
5.3.3 Sub-fleet Characterization 156
5.4 Ontology for Fleet-Wide Semantic Knowledge Modeling 158
5.4.1 Providing Semantic Through Ontology 158
5.4.2 Ontology-Based PHM Knowledge Modeling Rules 160
5.4.2.1 To Define the Key Concepts of the Domain 161
5.4.2.2 To Define a Class Hierarchy (Subsumption) 161
5.4.2.3 To Describe and Define Classes 162
5.4.2.4 To Define Properties of Classes 164
5.4.2.5 To Define the Value Type, the Cardinality and the Allowed Values of Classes 164
5.4.2.6 To Create Instances 165
5.4.3 Ontology for PHM and Marine Domains 166
5.4.3.1 Technical Context 167
5.4.3.2 Dysfunctional Context 168
5.4.3.3 Operational Context 169
5.4.3.4 Service Context 170
5.4.3.5 Application Context 171
5.4.3.6 Relations Between the Contexts 171
5.5 Application 172
5.5.1 Fleet-Wide Diagnosis Software 173
5.5.2 Case Study 175
5.6 Conclusions and Perspectives 183
References 184
Chapter 6: Integrating Cultural and Regulatory Factors in the Bowtie: Moving from Hand-Waving to Rigor 187
6.1 Introduction: Organizational and Cultural Influences in Incidents 187
6.2 Risk Analysis 190
6.2.1 Risk Analysis and Risk Assessment 191
6.2.2 Risk Management and Safety Management Systems 193
6.2.3 FJORDS: Formal, Justified, Organized, Rigorous, Disciplined, and Structured 193
6.3 Bowties 194
6.3.1 Analysis of a Bowtie 197
6.3.2 Escalation Factors: The Second Level of Analysis 198
6.3.3 Management Controls at Level 2 200
6.3.4 Distinguishing Cultural and Organizational Factors 201
6.3.5 Contracting Out and Partnering as Level 3 Phenomena 202
6.3.6 Individual Accountabilities 203
6.4 Using the Bowtie to Extend Our Understanding of Safety Management 204
6.4.1 Criticality of Barriers 204
6.4.2 Common Mode Failure 205
6.5 Integration with Incident Analysis and Reporting Systems 207
6.5.1 Incident Investigation 207
6.5.2 Reporting Systems 208
6.5.3 Integration with Audit Programs 209
6.6 Note for Practitioners 210
6.6.1 Correct Top Events Are Crucial 210
6.6.2 Sparseness 211
6.6.3 Completeness 211
6.6.4 Level 1 Simple Bowties for Frontline Staff 212
6.7 Conclusion 212
References 213
Chapter 7: Addressing Uncertainty in Estimating the Cost for a Product-Service-System Delivering Availability: Epistemology and Ontology 215
7.1 Introduction 215
7.2 Why Is Uncertainty Important in Modelling a PSS? 217
7.3 Systems-Based Approach to Cost Modelling for PSS 218
7.4 Uncertainty in Classic Service Cost Estimation 221
7.5 Uncertainty in a System-Based Model 224
7.6 Addressing Uncertainty in PSS 227
7.6.1 Are We Measuring the Right Things? 229
7.6.2 Towards an Ontology for PSS 229
7.7 Conclusions 230
References 231
Chapter 8: Ontology-Based Knowledge Platform to Support Equipment Health in Plant Operations 236
8.1 Introduction 237
8.1.1 Knowledge Building: Plant Topology and Semantic Knowledge Acquisition Representation 238
8.1.2 Equipment Failure Reasoning and Modeling 239
8.1.3 Adaptive Intelligent Fault Diagnosis and Prognosis 239
8.2 Literature Review 240
8.2.1 Knowledge Representation and Ontological Schema 241
8.2.2 Fault Diagnosis Orientation 242
8.3 The Intelligent Ontology-Based Framework (IOBF) 245
8.3.1 Ontology 245
8.3.2 OWL 248
8.3.3 ISO 15926 249
8.3.3.1 ISO 15926 Parts 2–4 249
8.3.3.2 ISO 15926 Parts 7–10 250
8.3.4 OREDA 250
8.4 Integrated Platform 250
8.4.1 Plant Topology and Semantic Knowledge Acquisition and Representation 253
8.4.2 Equipment Failure Reasoning and Modeling (An Intelligent Inference Engine) 255
8.4.3 An Adaptive Intelligent Fault Diagnosis and Prognosis Framework 258
8.5 Case Study 258
8.6 Conclusion 266
References 267
Index 271