Corrosion Processes (eBook)

Sensing, Monitoring, Data Analytics, Prevention/Protection, Diagnosis/Prognosis and Maintenance Strategies

George Vachtsevanos, K. A. Natarajan, Ravi Rajamani, Peter Sandborn (Herausgeber)

eBook Download: PDF

2020
VII, 339 Seiten
Springer International Publishing (Verlag)
978-3-030-32831-3 (ISBN)

Infos

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.

Inhalt

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

Produkt-Details

• Erscheint lt. Verlag: 1.1.2020
• Reihe/Serie: Structural Integrity
• Reihe/Serie: Structural Integrity
• Zusatzinfo: VII, 339 p. 266 illus., 188 illus. in color.
• Sprache: englisch
• Themenwelt: Technik ► Maschinenbau
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management
• Schlagworte: Corrosion diagnosis and prognosis • Corrosion mitigation • Corrosion modeling • Corrosion processes • Corrosion ROI • Electro-chemistry of corrosion • Quality Control, Reliability, Safety and Risk
• ISBN-10: 3-030-32831-7 / 3030328317
• ISBN-13: 978-3-030-32831-3 / 9783030328313