Karanki / Vinod / Ajit Advances in RAMS Engineering

In Honor of Professor Ajit Kumar Verma on His 60th Birthday
1. Auflage 2019
ISBN: 978-3-030-36518-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 484 Seiten

Reihe: Springer Series in Reliability Engineering

This book surveys reliability, availability, maintainability and safety (RAMS) analyses of various engineering systems. It highlights their role throughout the lifecycle of engineering systems and explains how RAMS activities contribute to their efficient and economic design and operation.  The book discusses a variety of examples and applications of RAMS analysis, including: •                     software products; •                     electrical and electronic engineering systems; •                     mechanical engineering systems; •                     nuclear power plants; •                     chemical and process plants and •                     railway systems.  The wide-ranging nature of the applications discussed highlights the multidisciplinary nature of complex engineering systems. The book provides a quick reference to the latest advances and terminology in various engineering fields, assisting students and researchers in the areas of reliability, availability, maintainability, and safety engineering.

Dr. Durga Rao Karanki is currently working as a RAMS Manager at Siemens Mobility AG (Switzerland) since 2017. He is responsible for planning, coordination, and performing RAMS activities for development and execution of European Train Control System (ETCS) projects. Previously, he worked as a Scientist at Paul Scherrer Institute (PSI) from 2009 to 2017. His research at PSI primarily focused on dynamic safety assessment and uncertainty management. Prior to joining PSI, he worked as a Scientific Officer (2002- 2009) at Bhabha Atomic Research Centre (India), where he conducted research on dynamic fault tree analysis, uncertainty analysis, and risk informed decision making of nuclear power plants. He is also a visiting faculty at several technical institutes. He has actively been involved in Reliability and Safety Assessment research and development for the last 17 years. His work resulted in more than 70 publications including 4 books, 15 first author journal papers, and several conference papers, with more than 900 citations. He received two awards for research Excellency from Society for Reliability Engineering, Quality and Operations Management (SREQOM). He is on the editorial board of three international journals in the area of reliability and risk analysis. He holds B.Tech in Electrical and Electronics Engineering from the Nagarjuna University (India), M.Tech in Reliability Engineering from the Indian Institute of Technology (IIT) Kharagpur and Ph.D. from the IIT Bombay.   Dr. Gopika Vinod received her doctoral degree in Reliability Engineering from Indian Institute of Technology, Bombay. She was the recipient of DAE Young Engineer Award 2007. She has been to visiting scientist at Brookhaven National Laboratory. Currently, she is heading the Probabilistic Safety Section of Reactor Safety Division of BARC. She also holds the position of Faculty with Homi Bhabha National Institute. She has been actively involved in Reliability, Safety and Risk analysis of Indian Nuclear Power Plants, nuclear and chemical facilities. She is in the editorial board of the International Journal of System Assurance Engineering and Management and the Journal of Life Cycle Reliability and Safety Engineering. Prof. A. Srividya has been a Guest Professor at Lulea Technical University, Sweden since Aug 2016 and was an Adjunct Professor of Maritime Studies at Western Norway University of Applied Sciences in autumn 2019. She has previously taught at the UiS Stavanger, Norway; at Stord/Haugesund University College, Norway; and at the IIT Bombay, India, where she was a Professor of Civil Engineering for over eight years with a research focus on Reliability and Safety Engineering & Quality Management. She has executed various research projects to promote industrial collaboration and has been course coordinator for industry CEPs like Reliability Engineering, Quality Management, and Six Sigma for IT. She has jointly edited books on Reliability Engineering, Quality Management and IT, and has co-authored five books on e.g. Fuzzy Reliability Engineering Concepts and Applications, Reliability and Safety Engineering, Dependability of Networked Computer Based Systems, and Risk Management for Non-Renewable Energy Systems. Prof. Srividya has over two hundred publications in various national/international journals and conference proceedings to her credit. She is an Editor of the International Journal of Systems Assurance Engineering and Management (IJSAEM), published by Springer, and serves on the editorial board of the journal Communications in DQM.

Autoren/Hrsg.

Karanki, Durga Rao

Vinod, Gopika

Ajit, Srividya

Inhaltsverzeichnis

1;Dedication;6
2;Foreword;8
3;Preface;9
3.1;Reliability and Availability;10
3.2;Maintainability;11
3.3;Safety;12
4;Acknowledgements;14
5;Contents;15
6;Contributors;17
7;Reliability and Availability Engineering;20
8;1 DC and AC Contingency Solvers in Composite Power System Adequacy Assessment;21
8.1;1 Composite System Adequacy;22
8.1.1;1.1 General Elements;22
8.1.1.1;1.1.1 Input Data;23
8.1.1.2;1.1.2 System State;23
8.1.1.3;1.1.3 Isolated Buses;25
8.1.1.4;1.1.4 Identification of Isolated Buses;26
8.1.2;1.2 DC—Contingency Solver;29
8.1.2.1;1.2.1 Network Model;29
8.1.2.2;1.2.2 Contingency Solver Description;32
8.1.2.3;1.2.3 Optimal Power Flow Formulation;33
8.1.2.4;1.2.4 Contingency Solver Verification;34
8.1.2.5;1.2.5 Illustrative Example;37
8.1.2.5.1;Step by Step Calculations;38
8.1.2.5.2;Composite System Adequacy Assessment;42
8.1.2.5.3;Calculation of the Reliability Indices;43
8.1.3;1.3 AC—Contingency Solver;44
8.1.3.1;1.3.1 Network Model;44
8.1.3.2;1.3.2 Contingency Solver Description;47
8.1.3.3;1.3.3 Optimal Power Flow Formulation;49
8.1.3.4;1.3.4 Initial Starting Point;50
8.1.3.5;1.3.5 Contingency Solver Verification;52
8.1.3.6;1.3.6 Illustrative Example;55
8.1.3.6.1;Step by Step Calculations;56
8.1.3.6.2;Composite System Adequacy Assessment;61
8.1.4;1.4 Reducing the Computation Time of HLII Assessment;64
8.1.4.1;1.4.1 Parallel Computation;64
8.2;2 Concluding Remarks;64
8.3;References;65
9;2 Reliability Analysis of Microgrid Systems Using Hybrid Approaches;67
9.1;Abstract;67
9.2;1 Introduction;68
9.3;2 System Descriptions;71
9.4;3 Hybrid Reliability Assessment Approaches;72
9.4.1;3.1 RBD Approach;72
9.4.2;3.2 FTA Approach;72
9.4.3;3.3 FTA-BDD and FTA-RBDD Approaches;73
9.5;4 Results and Assessment;74
9.5.1;4.1 Reliability Assessment of Microgrid System Employing RBD Approach;74
9.5.2;4.2 Reliability Assessment of Microgrid System Employing FTA-BDD and FTA-RBDD Approaches;77
9.5.3;4.3 Comparative Assessment;80
9.6;5 Conclusions;82
9.7;References;82
10;3 Reliability Prediction of Instrumentation and Control Cables for NPP Applications;85
10.1;1 Background;85
10.2;2 Ageing and Condition Monitoring;86
10.3;3 The Proposed Approach;87
10.4;4 Reliability Prediction by Stress Strength Interference Theory;87
10.4.1;4.1 Methodology;89
10.4.2;4.2 Analysis and Results;92
10.4.3;4.3 Summary;96
10.5;5 Reliability Prediction from Experimentally Determined Performance Indicators;96
10.5.1;5.1 Reliability Prediction from EAB;97
10.5.2;5.2 Results with Lognormal Distribution of Parameter A;98
10.5.3;5.3 Results with Gumbel (Min) Distribution of Parameter A;99
10.6;6 Reliability Prediction from OIT;99
10.7;7 Summary;101
10.8;References;101
11;4 Fatigue Life Model for PLCC Solder Joints Under Thermal Cycling Stress;103
11.1;Abstract;103
11.2;1 Introduction;103
11.3;2 Background;105
11.3.1;2.1 Solder Joints;105
11.3.2;2.2 Fatigue Failure;105
11.3.3;2.3 Fatigue Mechanism Due to Thermal Cycling;106
11.3.3.1;2.3.1 Fatigue Model;106
11.4;3 Description of Sample;107
11.5;4 Estimation of Stress/Strain Relationship Using Finite Element Model (FEM);107
11.5.1;4.1 Non-Linear FEA;108
11.6;5 Thermal Cycle Stress and Results;111
11.6.1;5.1 FEA Results;111
11.7;6 Experimentation;114
11.7.1;6.1 Pre-Experiment Defect Record;114
11.7.2;6.2 Planning of Test Profile;116
11.7.3;6.3 Acceleration Factor (AF) Estimation;117
11.7.4;6.4 Test Setup;117
11.7.5;6.5 Results;117
11.8;7 Estimation of Coffin-Manson Model Parameters;122
11.9;8 Summary and Discussion;123
11.10;Acknowledgements;124
11.11;References;124
12;5 An Integrated Approach of BOCR Modeling Framework for Decision Tool Evaluation;126
12.1;Abstract;126
12.2;1 Introduction;127
12.3;2 Related Background and Literature Review;128
12.3.1;2.1 Quantitative Framework in Decision Analysis;128
12.3.2;2.2 Definition and Prototyping Evaluation;130
12.3.3;2.3 Feasibility Analysis in Software Industry;130
12.3.4;2.4 Data Discrepancy from Multiple Inputs in Group Decision Making;131
12.3.4.1;2.4.1 Fuzzy Logic Set Theory;132
12.3.4.2;2.4.2 Arithmetic Interval Operations;134
12.3.5;2.5 BOCR Quantitative Approach;134
12.4;3 Customized Research Methodology and Modified Methods Used in the Proposed Framework;135
12.5;4 Case Studies Illustrations;136
12.5.1;4.1 Case Study 1;137
12.5.1.1;4.1.1 Introduction;137
12.5.1.2;4.1.2 Proposed Steps;138
12.5.1.3;4.1.3 Problem Formulation;139
12.5.1.4;4.1.4 BOCR-ANP: Control Network;140
12.5.1.4.1;Benefits Model;140
12.5.1.4.2;Opportunities Model;141
12.5.1.4.3;Costs Model;144
12.5.1.4.4;Risks Model;145
12.5.1.5;4.1.5 Strategic Network BOCR-AHP;146
12.5.1.6;4.1.6 Results Analysis;148
12.5.1.7;4.1.7 Final Results Evaluation: BOCR—Interval Arithmetic Operation Analysis;148
12.5.1.8;4.1.8 Results Interpretation/Discussion;149
12.5.2;4.2 Case Study 2;151
12.5.2.1;4.2.1 Introduction;151
12.5.2.2;4.2.2 Proposed Steps;152
12.5.2.3;4.2.3 Problem Statement;153
12.5.2.4;4.2.4 BOCR-FANP Control Network;154
12.5.2.5;4.2.5 BOCR-FAHP: Strategic Hierarchy;157
12.5.2.6;4.2.6 Final Analysis BOCR (FANP and FAHP);158
12.5.2.7;4.2.7 Results Interpretation/Discussion;160
12.6;5 Conclusions;162
12.7;6 Future Work;162
12.8;Acknowledgements;163
12.9;References;163
13;6 DevOps for IT Service Reliability and Availability;166
13.1;1 Introduction;166
13.1.1;1.1 Understanding Software Reliability and Availability;167
13.1.2;1.2 Understanding the Meaning of Service and Software;167
13.1.3;1.3 What Does Service Availability Mean?;168
13.1.4;1.4 What Is Service Reliability?;169
13.1.5;1.5 Agile’s Impact on Software Application Reliability and Availability;170
13.2;2 What Is DevOps?;171
13.2.1;2.1 How Does DevOps Differ from Other Methodologies?;172
13.2.2;2.2 What Are the Practices of DevOps?;174
13.2.2.1;2.2.1 Continuous Planning;175
13.2.2.2;2.2.2 Continuous Build and Integration;176
13.2.2.3;2.2.3 Continuous Testing;176
13.2.2.4;2.2.4 Continuous Deployment;177
13.2.2.5;2.2.5 Continuous Monitoring;178
13.2.2.6;2.2.6 Continuous Feedback;179
13.2.2.7;2.2.7 Collaboration;179
13.2.2.8;2.2.8 Metrics;182
13.2.2.9;2.2.9 Automation;182
13.2.2.10;2.2.10 Infrastructure and Environment Provisioning;183
13.3;3 Using DevOps Analytics to Improve Reliability and Availability;183
13.3.1;3.1 Implementation of DevOps Analytics;185
13.3.2;3.2 Architecture;186
13.3.3;3.3 Descriptive Analytics;188
13.3.3.1;3.3.1 Application Usage Analytics;188
13.3.3.2;3.3.2 Performance Analytics;189
13.3.3.3;3.3.3 Error Analytics;190
13.3.3.4;3.3.4 Code Quality Analytics;191
13.3.3.5;3.3.5 Application Security Analytics;191
13.3.3.6;3.3.6 Infrastructure Analytics;193
13.3.4;3.4 Correlation and Anomaly Detection;194
13.3.4.1;3.4.1 Correlation of Deployment Structure and Resource Utilization Metrics;194
13.3.4.2;3.4.2 Detection of Anomalies in Application Usage;194
13.3.4.3;3.4.3 Correlation of Errors with Methods and Programmer;194
13.3.4.4;3.4.4 Correlation of Performance and Application Complexity;197
13.3.4.5;3.4.5 Correlation of Errors Across Layers;197
13.4;4 Summary;199
13.5;References;199
14;7 The Unpopularity of the Software Tester Role Among Software Practitioners: A Case Study;201
14.1;Abstract;201
14.2;1 Introduction;202
14.3;2 Methodology;203
14.4;3 Results;204
14.4.1;3.1 PROs Related Results;204
14.4.2;3.2 CONs Related Results;206
14.5;4 Discussions;207
14.6;5 Limitations;209
14.7;6 Conclusions;209
14.8;Appendix: Survey Questions;210
14.9;References;211
15;8 A Study on Reliability of Rotors Using XLrotor;214
15.1;Abstract;214
15.2;1 Introduction;215
15.3;2 Applications of Rotordynamics Using Simulation Tools;219
15.4;3 Simulation Case Studies;219
15.4.1;3.1 Working Methodology;219
15.4.2;3.2 Analysis and Results of Case Study Using XLrotor;219
15.5;4 Conclusion;228
15.6;Acknowledgements;229
15.7;References;229
16;9 Time Variant Reliability Analysis of Passive Systems;232
16.1;1 Introduction;232
16.2;2 Passive Systems;233
16.3;3 Passive System Reliability;234
16.4;4 Static Reliability Analysis;235
16.4.1;4.1 A Case Study on PDHRS;235
16.5;5 Time Variant Reliability Analysis;240
16.5.1;5.1 Stochastic Fatigue Loading;243
16.5.2;5.2 A Case Study on PDHRS Piping System;245
16.6;6 Summary;248
16.7;References;249
17;10 Reliability Considerations in Analysis of Tunnels in Squeezing Rock;251
17.1;1 Methodology;252
17.2;2 Reliability Analysis of an Opening in Squeezing Ground;253
17.3;3 Application of RSM;253
17.4;4 Reliability Analysis of Tunnel Support System Under Squeezing Conditions;257
17.5;5 Target Reliability Indices;260
17.6;6 Results of Reliability Analysis of Lining;260
17.7;7 Effect of Tunnel Radius;261
17.8;8 Conclusion;263
17.9;References;269
18;Maintainability Engineering;272
19;11 Integrated RAMS, LCC and Risk Assessment for Maintenance Planning for Railways;273
19.1;Abstract;273
19.2;1 Introduction;274
19.3;2 Introduction to RAMS, LCC and Risk Assessment;276
19.3.1;2.1 Definition of RAMS;276
19.3.2;2.2 Definition of LCC;277
19.3.3;2.3 Definition of Risk Assessment;278
19.4;3 Maintenance Planning in Railways;280
19.4.1;3.1 Strategic Asset Management Plan (SAMP);281
19.4.2;3.2 Tactical and Route Asset Management Plan (RAMP);281
19.4.3;3.3 Implementation of the Asset Management Plan (IAMP);282
19.4.4;3.4 Interactions Among Planning Levels;282
19.5;4 Architecture of Failure and Cost Database;283
19.5.1;4.1 Guidelines for Future Data Standardization;285
19.6;5 Methodology for RAMS and LCC;287
19.6.1;5.1 Railway Asset Hierarchy;287
19.6.2;5.2 Asset Hierarchy Versus Planning Hierarchy;289
19.6.3;5.3 Methodologies from Previous Projects;291
19.6.3.1;5.3.1 INNOTRACK;291
19.6.3.2;5.3.2 D-Rail;291
19.6.3.3;5.3.3 SUSTRAIL;291
19.6.3.4;5.3.4 Automain;292
19.6.3.5;5.3.5 MAINLINE;292
19.6.3.6;5.3.6 ON-TIME;292
19.7;6 Maintenance Decisions Based on RAMS, LCC and Risk;293
19.7.1;6.1 Integrated RAMS, LCC and Risk Assessment;294
19.7.2;6.2 Generic Framework;295
19.7.3;6.3 Proposed Methodology;296
19.8;7 Conclusion;300
19.9;References;300
20;12 Implementation of Predictive Maintenance Systems in Remotely Located Process Plants under Industry 4.0 Scenario;305
20.1;Abstract;305
20.2;1 Introduction;306
20.3;2 Remotely Located Process Plants;307
20.3.1;2.1 Industry 4.0 and Changing Operational Philosophies of Remote Process Plants;308
20.3.2;2.2 Condition Monitoring of Remote Process Plants Under Industry 4.0;310
20.4;3 Maintenance Frame Work Under Industry 4.0 for Remote Process Plants;311
20.5;4 Transition to PdM 4.0 from Legacy Maintenance Systems;314
20.6;5 Customisation of PdM 4.0 Solutions to Remote Process Plants;315
20.6.1;5.1 Integration of PdM 4.0 Features During System Design;315
20.6.2;5.2 Optimisation of PdM 4.0 Solutions for Remote Process Plants;315
20.7;6 Case Study I: “a Process Critical Equipment of a Petroleum Refinery in North Eastern India Integrated with Industry 4.0 Based Predictive Maintenance System”;317
20.7.1;6.1 Introduction;317
20.7.2;6.2 Basic Information of Equipment Installed with Machine Monitoring System;318
20.7.3;6.3 Case Study I (Part 1): Compressor System with MPS Auto-Shutdown ‘OFF’;321
20.7.3.1;6.3.1 Problem Description and Prognostic System Logging System Alerts;321
20.7.3.2;6.3.2 Defect Analysis;322
20.7.3.3;6.3.3 Discussion;323
20.7.4;6.4 Case Study I (Part 2): Compressor System with MPS Auto-Shutdown ‘ON’;324
20.7.4.1;6.4.1 Problem Description;324
20.7.4.2;6.4.2 Discussion;325
20.7.4.3;6.4.3 Conclusion;325
20.8;7 Case Study II: Implementation Requirements for IIOT Based Predictive Maintenance System Under Industry 4.0 in a Remotely Located Petroleum Refinery;326
20.8.1;7.1 Plant Systems;327
20.8.2;7.2 Predictive Asset Health Monitoring;327
20.8.3;7.3 Suggested IIOT Architecture for a Refinery;333
20.8.4;7.4 Conclusion;333
20.8.5;7.5 Future Work for Implementation of IIOT Based Industry 4.0;333
20.9;8 Conclusion;335
20.10;References;335
21;13 Application of Fuzzy Sets in Reliability and in Optimal Condition Monitoring Technique Selection in Equipment Maintenance;339
21.1;Abstract;339
21.2;1 Introduction;339
21.3;2 Various Uncertainty Modeling Theories;340
21.4;3 Fuzzy Set Theory;342
21.5;4 Fuzzy Reliability;342
21.5.1;4.1 Example of PROBIST Fuzzy Reliability;343
21.5.2;4.2 Conceptual Example of PROFUST Fuzzy Reliability;345
21.5.3;4.3 Fuzzy Fault Tree;346
21.6;5 Fuzzy Multi Attribute Decision Making;348
21.6.1;5.1 Selection or Ranking of Condition Monitoring Techniques—Problem;349
21.6.2;5.2 Fuzzy Analytic Hierarchy Process (FAHP);349
21.6.3;5.3 Linguistic Scales and Input from the Experts;350
21.6.4;5.4 Rating and Ranking Method;352
21.6.5;5.5 Ranking Fuzzy Sets Using ‘Cardinal Utilities’;360
21.6.6;5.6 Ranking Fuzzy Sets by ‘Maximizing and Minimizing Sets’;362
21.6.7;5.7 Suitability Set and Dominance Relation;365
21.7;6 Scope for Hybrid Methods;368
21.8;References;369
22;14 Fuzzy Logic Based Analysis of Dissolved Decay Contents in Transformer Oil;372
22.1;1 Introduction;372
22.2;2 Assessment of Transformer Oil Using Dissolved Decay Contents;373
22.3;3 Transformer Oil Ageing Factors;374
22.3.1;3.1 Effect of Heat on Solid and Liquid Insulation;375
22.3.2;3.2 Reaction of Oxygen in the Oxidation Process;376
22.3.3;3.3 Effect of Oxygen on Oil and Paper Ageing;376
22.3.4;3.4 Furan Contents and Degree of Polymerization (DOP);377
22.4;4 Experimental Tests for Calculation of 2-FAL;378
22.4.1;4.1 Density;379
22.4.2;4.2 Overall Acidity Content;380
22.4.3;4.3 Interfacial Tension (IFT);380
22.4.4;4.4 Dissolved Decay Contents;382
22.4.5;4.5 Testing Details;382
22.5;5 Proposed Fuzzy Based Approach;383
22.5.1;5.1 Rules Defined;383
22.5.2;5.2 Physical Implementation of Model;385
22.5.3;5.3 Health Index of a Transformer;386
22.5.4;5.4 Design of the Membership Functions;386
22.6;6 Expert Rules;391
22.7;7 Result and Discussion;393
22.8;8 Conclusion;394
22.9;Acknowledgements;394
22.10;References;394
23;Safety Engineering;397
24;15 Probabilistic Safety Assessment in Nuclear and Non-nuclear Facilities: In a Glimpse;398
24.1;1 Probabilistic Safety Assessment of NPPs;398
24.1.1;1.1 Introduction to Nuclear Safety;398
24.1.2;1.2 Probabilistic Safety Assessment for NPP;399
24.1.2.1;1.2.1 Level 1 PSA;399
24.1.2.2;1.2.2 Level 2 PSA;400
24.1.2.3;1.2.3 Level 3 PSA;400
24.1.3;1.3 Level 1 PSA of NPPs;401
24.1.3.1;1.3.1 Identification of Initiating Events;402
24.1.3.2;1.3.2 Plant Response Modeling: Event Tree Analysis;402
24.1.3.3;1.3.3 System Reliability Modelling;403
24.1.3.4;1.3.4 Common Cause Failure Analysis;404
24.1.3.5;1.3.5 Human Reliability Analysis;405
24.1.3.6;1.3.6 Accident Sequence Analysis;408
24.1.3.7;1.3.7 Uncertainty Analysis;410
24.1.4;1.4 PSA Application in Safety Issues;410
24.1.4.1;1.4.1 Probabilistic Precursor Analysis;410
24.1.4.2;1.4.2 Probabilistic Vital Area Identification;412
24.1.5;1.5 Summary;413
24.2;2 Probabilistic Safety Assessment of Non-reactor Nuclear Facilities;413
24.2.1;2.1 Introduction;413
24.2.2;2.2 Steps in PSA for Nuclear Facility;414
24.2.2.1;2.2.1 System Description;414
24.2.2.2;2.2.2 Hazard Identification;414
24.2.2.3;2.2.3 Incident Enumeration;415
24.2.2.4;2.2.4 Accident Sequence Quantification;415
24.2.2.5;2.2.5 Risk Estimation;416
24.3;3 Probabilistic Safety Assessment of Non–nuclear Facilities;416
24.3.1;3.1 Introduction;416
24.3.2;3.2 Steps in PSA for Chemical Facility;420
24.3.2.1;3.2.1 Hazard Identification;420
24.3.2.2;3.2.2 Incident Enumeration;421
24.3.2.3;3.2.3 Consequence Estimation;421
24.3.2.4;3.2.4 Risk Estimation;422
24.3.3;3.3 Risk Based Inspection for Chemical Facility;422
24.4;4 Concluding Remarks;424
24.5;References;424
25;16 Passive System Reliability Assessment and Its Integration Into PSA;426
25.1;1 Introduction;426
25.2;2 Reliability Assessment for Passive Safety Systems;427
25.3;3 Reliability Evaluation of Passive Safety Systems (REPAS) Method;428
25.3.1;3.1 Step 1—Characterization of Passive Safety System;429
25.3.2;3.2 Step 2—Development of Analytical Model;429
25.3.3;3.3 Step 3—Assigning Probabilistic Distributions;429
25.3.4;3.4 Step 4—Performance Evaluation of Passive System;430
25.3.5;3.5 Step 5—Development of Response Surface;430
25.3.6;3.6 Step 6—Assessment of Passive Safety System Reliability;430
25.4;4 Assessment of Passive System ReliAbility (APSRA) Method;431
25.4.1;4.1 Generation of Failure Surface;431
25.4.2;4.2 Root Diagnosis;431
25.4.3;4.3 Reliability Assessment of Passive System;432
25.5;5 Use of Artificial Neural Networks (ANN) in Passive System Reliability Assessment;432
25.6;6 Integration of Passive System Reliability Into Probabilistic Safety Analysis (PSA);434
25.6.1;6.1 Description of the Isolation Condenser System (ICS);435
25.6.2;6.2 Estimation of ICS Reliability;436
25.6.3;6.3 Integration of Passive System Reliability Into PSA;437
25.7;7 Conclusion;441
25.8;References;441
26;17 Project Stage Considerations for an Inherently Safe and Reliable Chemical Plant;444
26.1;Abstract;444
26.2;1 Introduction;445
26.3;2 Engineering Projects and the Role in Reliability;445
26.4;3 Project Management Process;446
26.5;4 Safety, Operability and Reliability Consideration During Inception and Project Stage;447
26.6;5 Process Risk Assessment Techniques;450
26.6.1;5.1 Hazard and Operability Study;451
26.6.2;5.2 Process Risk Assessment Using Matrix;453
26.6.2.1;5.2.1 Risk Assessment Matrix;453
26.6.2.2;5.2.2 Layers of Protection Analysis (LOPA);454
26.6.2.3;5.2.3 Safety Integrity Level (SIL);455
26.6.2.4;5.2.4 Process Risk Assessment Form;456
26.7;6 Qualitative Analysis of General Reliability;458
26.7.1;6.1 Suggested Process;459
26.8;7 Conclusion;461
26.9;References;461
27;18 Integrated Deterministic and Probabilistic Safety Assessment;464
27.1;1 Introduction to Integrated Deterministic and Probabilistic Safety Assessment;464
27.1.1;1.1 Probabilistic and Deterministic Safety;465
27.1.2;1.2 Issues in Current Approach;465
27.1.3;1.3 IDPSA Using Dynamic Event Trees;467
27.2;2 A Simple Example: Water Leaks into a Ship/Vessel;468
27.3;3 IDPSA Methodologies;470
27.3.1;3.1 DET Informed PSA;471
27.3.2;3.2 Quantified DET Based IDPSA;472
27.4;4 Case Study—MLOCA Scenario;474
27.4.1;4.1 Description;474
27.4.2;4.2 Results—Comparison of IDPSA Approaches with PSA;479
27.5;5 Summary;482
27.6;Acknowledgements;482
27.7;References;482