Vachtsevanos / Natarajan / Rajamani Corrosion Processes

Sensing, Monitoring, Data Analytics, Prevention/Protection, Diagnosis/Prognosis and Maintenance Strategies
1. Auflage 2020
ISBN: 978-3-030-32831-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 13, 341 Seiten

Reihe: Structural Integrity

This book discusses relevant topics in field of corrosion, from sensing strategies to modeling of control processes, corrosion prevention, detection of corrosion initiation, prediction of corrosion growth and evolution, to maintenance practices and return on investment.Written by leading international experts, it combines mathematical and scientific rigor with multiple case studies, examples, colorful images, case studies and numerous references exploring the essentials of corrosion in depth. It appeals to a wide readership, including corrosion engineers, managers, students and industrial and government staff, and can serve as a reference text for courses in materials, mechanical and aerospace engineering, as well as anyone working on corrosion processes.

Dr. George Vachtsevanos is currently serving as Professor Emeritus at the Georgia Institute of Technology, Atlanta, Georgia, USA. Dr. Vachtsevanos directs at Georgia Tech the Intelligent Control Systems laboratory where faculty and students are conducting interdisciplinary research in intelligent control, fault diagnosis and failure prognosis and resilient design and operation of complex systems and hierarchical/intelligent control of Unmanned Aerial Vehicles. His research has been funded over the years by government and industry. He has published over 350 technical papers and is the co-inventor of 13 U.S. patents.  He is the lead author of a book on Intelligent Fault Diagnosis and Prognosis for Engineering Systems published by Wiley in 2006. 
Dr. K.A. Natarajan is presently Emeritus Professor and NASI Honorary Scientist at the Department of Materials Engineering, Indian Institute of Science, Bangalore, India. He did his M.S. and Ph.D degrees specialising in Mineral beneficiation and Hydrometallurgy from the University of Minnesota, USA. The Indian Institute of Science, Bangalore conferred on him the degree of Doctor of Science in 1992 for his pioneering research contributions in Minerals bioprocessing. He is a Fellow of several academies such as the Indian Academy of Sciences, Indian National Academy of Engineering and the National Academy of Sciences. He has received several medals and awards such as the National Metallurgist Award by the Ministry of Mines, Govt. of India and National Mineral award by the Ministry of Mines. Govt. of India, Alumni Award of Excellence in Engineering Research by the Indian Institute of Science, Bangalore, Kamani Gold medal of the Indian Institute of Metals and the Hindustan Zinc Gold Medal. He has also been honored with the presentation of Biotech Product and Process Development & Commercialisation Award 2003, Dept. of Biotechnology, Govt. of India. He has also been conferred with the National Metallurgist Award for the year 2006 from the Ministry of Steel, Govt. of India for his outstanding contributions in the field of mineral processing and hydrometallurgy for enrichment of ores, extraction of valuable metals, deto xification of mine and metallurgical plant effluents. In  2016, he was awarded the NIGIS Life time Achievement Award For Teaching and Research in CORROSION ENGINEERING by the NACE.
He is on the Editorial board of several international journals in the area of Mineral processing. His areas of research include Mineral processing, Hydrometallurgy, Minerals bioprocessing, Corrosion engineering and Environmental control. He has published over 300 research papers in leading international journals in the above areas.  He was the Chairman of the Department of Metallurgy, Indian Institute of Science, and Bangalore during the period 1999-2004.Dr. Ravi Rajamani is an independent consultant, working in the aerospace and energy sectors, specifically on controls, diagnostics, and prognostics. He is the author of three books, many book chapters, journal and conference papers, and has several patents to his name. In the past, Ravi worked at Meggitt, United Technologies Corporation, and the General Electric Company. He has been elected a fellow of SAE International and of IMechE, in the UK and he currently serves as the Editor in Chief of the SAE International Aerospace Journal. In addition, he has research and visiting appointments at University of Connecticut and Cranfield University.

Peter Sandborn is a Professor in the CALCE Electronic Products and Systems Center and the Director of the Maryland Technology Enterprise Institute at the University of Maryland.  Dr. Sandborn's group develops life-cycle cost models and business case support for long field life systems.  This work includes: obsolescence forecasting algorithms, strategic design refresh planning, lifetime buy quantity optimization, and return on investment models for maintenance planning (including the application of PHM to systems).  Dr. Sandborn is the developer of the MOCA refresh planning tool.  He is the author of over 200 technical publications and several books on electronic packaging and electronic systems cost analysis and was the winner of the 2004 SOLE Proceedings, 2006 Eugene L. Grant, 2017 ASME Kos Ishii-Toshiba Award, and the 2018 Jacques S. Gansler Excellence in Sustainment Sciences awards.  He is a Fellow of the IEEE, ASME, and the PHM Society.

Autoren/Hrsg.

Vachtsevanos, George

Natarajan, K. A.

Rajamani, Ravi

Sandborn, Peter

Inhaltsverzeichnis

1;Contents;6
2;Contributors;7
3;1 Introduction;8
3.1;Abstract;8
3.2;1.1 Introduction;10
3.2.1;1.1.1 Impact of Corrosion on Engineering System Integrity;17
3.3;1.2 Fatigue Corrosion: Example Cases in Aerospace and Industrial Processes;26
3.4;1.3 Corrosion of Oil Platforms;28
3.5;1.4 Pipeline Fatigue Corrosion;29
3.6;1.5 Concrete Block Corrosion Sensing;29
3.7;1.6 GE Corrosion Sensing and Monitoring Technologies;29
3.8;1.7 Corrosion of Steel in Concrete Structures;30
3.9;1.8 Corrosion Assessment: From the Laboratory to On-Board the Aircraft;31
3.10;References;31
4;2 Principles of Corrosion Processes;33
4.1;Abstract;33
4.2;2.1 Silver–Silver Chloride Reference Electrode;37
4.3;2.2 Saturated Calomel Electrode (SCE);37
4.4;2.3 The Hydrogen Electrode (NHE);37
4.5;2.4 Copper–Copper Sulfate Electrode;38
4.6;2.5 Junction Potentials;40
4.7;2.6 Concentration Cells [1–5];40
4.8;2.7 EMF Series [1–5];43
4.9;2.8 Applications of EMF Series;45
4.10;2.9 Limitation of EMF Series;45
4.11;2.10 Galvanic Series [1–5];46
4.12;2.11 Electrochemical Aspects of Bimetallic (Galvanic) Corrosion [3, 6, 7];46
4.13;2.12 Potential-pH Diagrams [1–5];52
4.14;2.13 Electrochemical Kinetics [1–5, 9];59
4.15;2.14 Theory of Mixed Potentials [1–5, 10];63
4.16;2.15 Platinum-Iron Couple in Acid Solution [11];66
4.17;2.16 Iron-Zinc Couple [11];67
4.18;2.17 Determination of Corrosion Rates [2];68
4.19;2.18 Electrochemical Aspects of Passivity [1–5, 11, 12];70
4.20;2.19 Pitting Behavior of Passive Metals and Alloys;75
4.21;2.20 Anodic Protection [1, 2, 13];76
4.22;2.21 Cathodic Protection [1–5, 14, 15];78
4.23;2.22 Stray Current Corrosion [1–5];82
4.24;2.23 Biofouling and Microbially Influenced Corrosion [1, 16–22];84
4.25;2.24 Summary;87
4.26;Acknowledgements;87
4.27;References;87
5;3 Corrosion Sensing;89
5.1;Abstract;89
5.2;3.1 Introduction;89
5.3;3.2 Electrochemical/Electrical Techniques;90
5.3.1;3.2.1 Potentiostatic and Potentiodynamic Evaluation;91
5.3.2;3.2.2 Electrochemical Impedance Spectroscopy and Polarization Resistance;92
5.3.3;3.2.3 Galvanic Corrosion;94
5.3.4;3.2.4 Electrochemical Noise;95
5.3.5;3.2.5 Electrical Resistance;96
5.3.6;3.2.6 Inductive Shift;97
5.4;3.3 Environmental Sensing;97
5.4.1;3.3.1 Relative Humidity and Temperature;97
5.4.2;3.3.2 Environmental Contaminants;98
5.5;3.4 Mechanical Methods;100
5.5.1;3.4.1 Ultrasonic Probes;100
5.5.2;3.4.2 Radiography;101
5.5.3;3.4.3 Strain Measurements;102
5.5.4;3.4.4 Acoustic Sensing;102
5.5.5;3.4.5 Eddy Current;102
5.6;3.5 Sacrificial Sensors;103
5.7;3.6 Visual;104
5.8;3.7 Optical;106
5.8.1;3.7.1 Fiber Optic Methods;106
5.8.2;3.7.2 Laser Profilometry;107
5.8.3;3.7.3 White Light Interferometry;107
5.9;3.8 Other Sensing Modalities;108
5.10;3.9 Epilogue;109
5.11;References;109
6;4 Corrosion Prevention;111
6.1;Abstract;111
6.2;4.1 Introduction to Corrosion Prevention;111
6.3;4.2 Coatings for Corrosion Prevention;113
6.4;4.3 Organic Coatings;114
6.5;4.4 Inorganic Coatings;116
6.6;4.5 Metallic Coatings;118
6.7;4.6 Engineering Design Considerations and Coating Selection;120
6.8;4.7 Conclusion;123
6.9;References;123
7;5 Data Analytics for Corrosion Assessment;124
7.1;Abstract;124
7.2;5.1 Introduction;125
7.3;5.2 Corrosion Data Mining-Feature Extraction and Selection;127
7.4;5.3 Image Pre-processing;128
7.5;5.4 Data Mining/Image Processing;130
7.6;5.5 Feature Extraction and Selection;133
7.7;5.6 Baseline Profile Measuring Results;146
7.7.1;5.6.1 2D Profile Information;146
7.7.2;5.6.2 2D Profile Information;147
7.7.3;5.6.3 3D Profile Information;149
7.8;5.7 Cut-off Wavelength ?c Selection;149
7.9;5.8 Deep Learned Features (DLF);149
7.10;5.9 Methods;153
7.11;5.10 Codebase Validation;156
7.12;5.11 Conclusion;158
7.13;5.12 Feature Selection;158
7.14;5.13 Classification Techniques;159
7.15;5.14 Sensor Data Fusion;162
7.15.1;5.14.1 Fusion at the Feature Level;163
7.16;5.15 Epilogue;165
7.17;References;165
8;6 Corrosion Modeling;167
8.1;Abstract;167
8.2;6.1 The Need;168
8.3;6.2 The Objective;168
8.4;6.3 Corrosion Books;171
8.5;6.4 The Data Base;171
8.6;6.5 Imaging Data;172
8.7;6.6 Salt Fog Images;173
8.8;6.7 Fundamental Corrosion Processes;178
8.9;6.8 Corrosion Modeling: Background/State of the Art;179
8.10;6.9 Introduction to the Modeling Framework;181
8.11;6.10 Basic Modules of the Smart Sensing Modality and Corrosion Modeling;181
8.12;6.11 From Microscale to Mesoscale and Macroscale Models;185
8.13;6.12 Corrosion Modeling Methods;186
8.14;6.13 Data-Driven Models;188
8.15;6.14 Model-Based Approaches;188
8.16;6.15 Stochastic/Probabilistic Methods;189
8.17;6.16 Corrosion Modeling Approaches;191
8.18;6.17 Global Versus Local Corrosion Models;196
8.19;6.18 A Novel Modeling Approach;197
8.20;6.19 A General Framework to Corrosion Modeling;199
8.21;6.20 Other Failure Prediction Models;200
8.22;6.21 Stochastic Dynamical Model of Corrosion States from Pitting to Cracking Under Loading and Environmental Stress;201
8.23;6.22 Pitting Corrosion;202
8.24;6.23 Paris’ Law Revisited;203
8.25;6.24 Transition from Pitting to Cracking;204
8.26;6.25 Environmental Stressors;205
8.27;6.26 Symbolic Regression Modeling Framework;206
8.28;6.27 Discrete Form;208
8.29;6.28 Useful Tools;209
8.30;6.29 An Example;212
8.31;6.30 An Extended Corrosion Model;213
8.31.1;6.30.1 Sensor Modeling Parameters;213
8.32;6.31 Results;214
8.33;6.32 Model On-Line Update [24];215
8.34;6.33 Consideration of Operating Conditions;220
8.35;6.34 Other Failure Prediction Models;222
8.36;6.35 A Corrosion Modeling Framework for Steel Structures;224
8.37;6.36 Modeling of Nuclear Waste Storage Facilities;225
8.38;6.37 State of the Art in Corrosion Modeling for Nuclear Storage Facilities;226
8.39;6.38 Localized Corrosion;227
8.40;6.39 Localized Corrosion Due to Deliquescence;229
8.41;6.40 Epilogue;231
8.42;References;231
9;7 Corrosion Diagnostic and Prognostic Technologies;234
9.1;Abstract;234
9.2;7.1 The Corrosion Detection and Prediction Architecture;235
9.3;7.2 The Impact of Corrosion on the Integrity of Critical Assets;237
9.4;7.3 Corrosion Processes;238
9.5;7.4 Data and Corrosion Modeling Requirements;240
9.6;7.5 The Corrosion Diagnostic and Prognostic Algorithms;242
9.7;7.6 Corrosion Modeling Framework: Symbolic Regression;245
9.8;7.7 Objective Function;246
9.9;7.8 Discrete Form;246
9.10;7.9 Useful Tools;247
9.11;7.10 Symbolic Regression Result;251
9.12;7.11 Health Indexes;254
9.13;7.12 Development of Fault Diagnosis and Failure Prognosis Algorithms;256
9.14;7.13 Corrosion Degradation Detection—The Particle Filtering Approach to Degradation Detection;258
9.15;7.14 Diagnosis of Corrosion Degradation;260
9.16;7.15 Corrosion Diagnosis—Implementation Issues;262
9.17;7.16 Beyond Diagnosis Towards Prognosis;267
9.18;7.17 Degradation Prognosis;268
9.19;7.18 A Taxonomy of Prognostic Approaches;272
9.20;7.19 Data-Driven Prognostic Techniques;279
9.21;7.20 Model On-Line Update;281
9.22;7.21 Consideration of Operating Conditions;284
9.23;7.22 Statistical Techniques;286
9.24;7.23 Particle Filtering as an Uncertainty Representation and Management Technique for Failure Prognosis;286
9.25;7.24 Uncertainty Management in Long-Term Predictions;288
9.26;7.25 Measuring Prognostics Performance;289
9.27;7.26 Prognostic Horizon;289
9.28;7.27 ?-? Performance;289
9.29;7.28 Prognostic Dynamic Standard Deviation (DSTD);290
9.30;7.29 Critical-? Index;291
9.31;7.30 Corrosion Diagnostic and Prognostic Results;294
9.32;7.31 Testing of Data-Mining Techniques on µLPR Sensor Data;296
9.33;7.32 Performance Evaluation;300
9.34;7.33 Performance Metrics/Specifications/Constraints;300
9.35;7.34 Propagation from Corrosion to Structural Fatigue;301
9.36;7.35 Direct Tension Stress-Corrosion Testing;303
9.37;7.36 Considerations;303
9.38;7.37 Evaluation/Inspection;304
9.39;7.38 The Reasoning Paradigm: Dynamic Case Based Reasoning—The “Smart” Knowledge Base;305
9.40;7.39 Incremental Learning–The Reinforcement Learning Tool;309
9.41;7.40 The Association Strategy—Relating Sensor Outputs (Features) to Control Decisions;310
9.42;7.41 Performance Evaluation;311
9.43;7.42 Performance Metrics/Specifications/Constraints;311
9.44;7.43 Epilogue;311
9.45;References;312
10;8 Assessing the Value of Corrosion Mitigation in Electronic Systems Using Cost-Based FMEA—Tin Whisker Mitigation;315
10.1;Abstract;315
10.2;8.1 Introduction;315
10.3;8.2 Tin Whiskers;316
10.3.1;8.2.1 Tin Whisker Mitigation;317
10.3.2;8.2.2 Whisker Growth Modeling;318
10.4;8.3 Failure Severity Modelling;320
10.4.1;8.3.1 Cost of Reliability Models;320
10.4.2;8.3.2 Failure Severity Model;321
10.4.3;8.3.3 Determining the Initial PCFC;323
10.4.4;8.3.4 Activities Affecting the Number of Failures;325
10.4.5;8.3.5 Return on Investment;327
10.5;8.4 Case Study—The Cost Implications of Implementing Whisker Growth Mitigation Plans;327
10.5.1;8.4.1 Whisker Mitigation Activities—Conformal Coating;329
10.5.1.1;8.4.1.1 Silicone Coating;329
10.5.1.2;8.4.1.2 Parylene-C Coating;329
10.5.2;8.4.2 Board Applications;330
10.5.3;8.4.3 Desktop Computer Application Results;334
10.5.4;8.4.4 Commercial Aircraft Application Results;335
10.5.4.1;8.4.4.1 Silicone Conformal Coating;335
10.5.4.2;8.4.4.2 Parylene-C Conformal Coating;336
10.6;8.5 Epilogue;339
10.7;References;340