Matthew Hu, Senior Vice President, Engineering and Quality, Haylion Technologies, and Adjunct Professor, University of Houston, USA. Dr. Hu is a Certified Robust Design Expert using Taguchi Method, a Certified LSS Master Black Belt, and a certified DFSS Master Black Belt. Yan-Fu Li, Professor, Tsinghua University, China. He is the Principal Investigator (PI) of several government projects including the key project funded by National Natural Science Foundation of China.

• Series Editor’s Foreword xvPreface xviiAcknowledgments xxiii1 Enchaining Lifecycle Reliability with Robust Engineering and Prognostic Health Management 11.1 Introduction 21.2 Purpose 31.3 Essentials of Robustness and Robust Design in Reliability Improvement 41.4 Effective Reliability Efforts in an Integrated Product Development Environment 41.5 Enhancing Reliability Integration into the Product Development Process 61.6 Physics of Failure (PoF) 71.7 Failure-mode Avoidance 91.8 Design for Six Sigma 101.8.1 The Essence of Robustness Thinking 111.8.2 Robust Design as a Key Strategy 121.8.3 Paradigm Shift and Change 131.8.4 DFSS Roadmap: Emphasizing Robustness 131.9 Design for Reliability 161.10 Prognostics and Health Management 171.10.1 Health Indicators in Prognostic Health Management: Critical-to-Quality (CTQ) and Critical-to-Reliability (CTR) 211.10.2 Critical-to-Quality (CTQ) Parameters 211.10.3 Critical-to-Reliability (CTR) Parameters 211.10.4 Identification and Selection of CTQ and CTR Parameters 221.10.4.1 Robust Design for Reliability (RDfR) 221.10.5 Health Indicators in Prognostics and Health Management 221.11 The Importance of Digital Quality in Lifecycle Reliability Through Robustness Development and Predictive Health Management 231.12 Digital Quality in Lifecycle Reliability 231.12.1 Robustness Development 241.13 Predictive Health Management (PHM) 251.13.1 Integration of Digital Quality, Robustness Development, and PHM 261.14 Critical Parameter Development and Management (CPD&M): A Comprehensive Overview 271.14.1 The CPD&M Process 281.14.1.1 Initial Parameter Identification 281.14.1.2 The Seven Metrics 281.14.2 Continuous Improvement 28References 30Further Reading 312 Robustness Thinking and Strategies for Reliability Development 332.1 Introduction 342.1.1 Failure-Mode Avoidance: A Comprehensive Approach to Reliability 342.2 What Is Robustness Thinking? 402.3 The Challenge and Limitation of Conventional Reliability Approach 442.3.1 Uncertainty—Variation and Lack of Knowledge 442.3.1.1 Random Variation or Physical Uncertainty 462.3.1.2 Statistical Uncertainty 462.3.1.3 Model Uncertainty 462.3.1.4 Among These Three Types of Uncertainties 462.3.2 Traditional Reliability Challenges 472.3.3 Demand and Capacity—Statistical Modeling 532.3.4 Deterministic vs. Probabilistic Design 552.3.5 Understanding the Outer Array 592.3.6 Assessing Strength vs. Stress 592.3.7 P-Diagram 602.4 Why Robust Design? 612.5 The Importance and Principle of Flow in Robustness Thinking 622.5.1 Defining Flow 642.5.2 Transformation Systems, Flow, and Proactive Failure Creation 652.5.2.1 Load–Stress–Strength Thinking as a Proactive Reliability Framework 652.5.2.2 Margin, Limits, and Failure Distance 662.5.2.3 Noise Factors and Robust Design for Proactive Reliability 662.5.2.4 Architecture Robustness and Failure Propagation 672.5.2.5 Reliability Creation During Concept and Design 672.5.2.6 Summary: Robustness Thinking as Proactive Reliability 672.5.3 Importance of Flow in System Design and Optimization 672.5.4 Integrating Robustness Thinking and Robust Design Principles 672.5.5 Barriers to Flow Due to Lack of Robustness Thinking 682.5.6 Overcoming Barriers to Flow with Robustness Thinking 692.5.7 Examples of Barriers to Flow 692.5.8 Addressing Barriers with Robustness Thinking 702.6 Robustness Development Strategy 712.7 Three Phases of Robust Design 732.8 Understanding and Mitigating Mistakes in Design and Manufacturing 752.8.1 Improving Reliability by Reducing Mistakes 76References 77Further Reading 773 Robust Design Principles, Tactics, and Primary Tools 793.1 Introduction 793.2 Ideal Function: Ideal Transformation System Input and Output Relationship 803.3 Ideal Function and Quality Problems 813.4 Identification and Classification of Design Parameters: P-Diagram 823.5 Opportunity for Robustness Development 873.6 Two-Step Optimization 893.7 Robustness Measurement: S/N Ratio 903.8 S/N Ratio Improvement and Variation Reduction 923.9 S/N Ratio, the Additive Model, and the Conservative Laws of Physics 933.10 The Static Signal-to-Noise Ratios 943.10.1 Nominal-the-Best (NTB) Case 943.10.2 Smaller-the-Better (STB) 953.10.3 Larger-the-Better (LTB) 963.10.4 Operating Window (OW) Response 973.10.5 Classified Attribute Response 983.11 Dynamic Signal-to-Noise Ratios 983.11.1 Zero-Point Proportional Response 983.12 Robust Parameter Design Strategy and Steps 1003.12.1 Steps in Robust Parameter Design for Nominal-the-Best Characteristics 1063.13 Quality Measurement: Loss Function 1083.14 Robust Technology Development 109References 1144 Robust Design for Reliability (RDfR) A Comprehensive Approach to Product Excellence 1174.1 Introduction 1174.2 Robust Design for Reliability: A Comprehensive Approach to Product Excellence 1204.2.1 Preventing Failure Modes Through Vigilance 1234.2.1.1 Understanding the Entropic Nature of Mistakes 1234.2.1.2 Strengthening Organizational Vigilance 1234.3 Roadmap for Robust Design for Reliability Execution 1274.3.1 Identify Phase 1274.3.1.1 Identify Phase Purposes 1294.3.1.2 Identify Phase Activities 1324.3.1.3 Identify Phase Deliverables 1364.3.2 Design Phase 1364.3.3 Design Phase Purposes 1374.3.3.1 Design Phase Deliverables 1414.3.4 Optimize Phase 1424.3.4.1 Optimize Phase Purpose 1424.3.4.2 Robustness “Rules of Engagement” 1454.3.4.3 Optimize Phase Activities 1464.3.4.4 Optimize Phase Deliverables 1484.3.5 Verify Phase 1484.3.5.1 Verify Phase Purpose in Robust Design for Reliability 1484.3.5.2 Verify Phase Activities in Robust Design for Reliability 1514.3.5.3 Verify Phase Deliverables 1584.4 Robust Design Principles for Prognostic Health Management 1594.5 Scorecard for Robust Design for Reliability Implementation 1614.6 Digital Quality Through Robust Design for Reliability 1654.7 Critical Parameter Development and Management (CPD&M) Process and Phases 170References 171Further Reading 1725 Predictive & Health Management 1735.1 Justification for PHM in Robust System Design 1735.2 System Components and Their Functions 1765.2.1 PHM System Architecture 1765.2.2 Integration with Existing Maintenance Operations 1795.2.2.1 Maintenance and Maintenance Strategies 1795.2.2.2 Condition-based Maintenance (CBM) 1795.2.3 Scalability and Adaptability in PHM Design 1835.2.3.1 Activities of PHM and Reliability Over the Product Lifecycle 1835.2.3.2 Integration of Robust Design and PHM for Enhanced System Reliability 1845.2.3.3 The Power of Integrating Robust Engineering and PHM 1845.2.3.4 Assignment of Reliability and PHM Activities Over the Product Lifecycle 1855.2.3.5 The Role of PHM in the Product Lifecycle 1865.2.3.6 PHM System Development Process and Associated Standards 186References 1896 Characterizing Failure Signatures 1916.1 Characterizing Failure Signatures 1916.1.1 Identifying Degradation Patterns 1916.1.1.1 Synergistic Integration: Robust Design, Physics of Failure, and Degradation Pattern Identification 1926.1.2 Signature Analysis for Different System Components 1976.1.3 Signature Analysis Methods for Various System Components 2006.1.4 The Role of Signatures in Failure Prediction 2036.1.5 Data Collection for Signature Development 205References 2087 Guidelines for PHM System Implementation 2097.1 Enabling Technologies for PHM 2107.1.1 Sensor Technology Selection and Integration 2107.1.2 Developing Robust Sensor Technology and Integration Strategy for PHM 2107.1.2.1 Sensor Technology Development for PHM 2107.1.2.2 Conducting Robustness Assessment of Sensors 2117.2 Identifying and Selecting Robust Sensors for PHM 2117.3 Integration and Validation for PHM-Ready Systems 2117.4 Advanced Computing Platforms for PHM Analytics 2137.4.1 Edge Computing 2137.4.2 Cloud Computing 2137.4.3 Fog Computing 2147.4.4 Distributed Computing Frameworks 2147.4.5 High-performance Computing (HPC) 2147.5 AI-accelerated Hardware 2147.6 Evaluation Metrics for PHM Systems 2147.7 Robust PHM System 2157.7.1 Modular Architecture for PHM Systems 2157.7.2 Robustness, Redundancy, and Fault Tolerance in PHM System Design 2167.7.2.1 Redundancy in PHM Architecture 2177.7.2.2 Fault Tolerance Mechanisms 2177.7.2.3 Building for Long-Term Reliability and Cost Effectiveness 2187.7.3 User-centric Design for Ease of Integration 2187.7.4 Implementation Measures of User-centric Design in PHM 2197.8 Robust Prototype and Test-Bench Development for PHM System Validation 2197.8.1 System-level Requirements with Robustness in Mind 2197.9 Modular, Robust PHM Prototype Architecture 2207.10 Test-Bench Design for Robustness Validation 2207.11 Embedding Robustness into PHM Prototyping 2227.12 Verification Against Real-World Failure Data 2227.12.1 Why Real-World Data Validation Matters 2227.12.2 Types and Sources of Real-World Failure Data 2237.12.3 Public Benchmark Datasets 2237.12.4 Structured Methods for Real-World Verification 2237.12.5 Continuous System Evaluation Post-deployment 2247.12.6 Rationale for Continuous Evaluation 2247.12.7 Key Components of a Post-deployment Evaluation Framework 2257.13 Organizational Integration and Governance 2257.13.1 Strategic Implementation of PHM 2267.13.1.1 PHM-triggered Actions and Data Feedback Loop 2267.13.1.2 Enhancing PHM Models with Operational Data 2267.13.2 Future-proofing PHM Systems for Technological Advancements 2277.14 Case Study of PHM System Development 228References 2368 Case Study for Robust Design for Reliability (RDfR) 2398.1 Introduction 2398.2 RDfR Phases in DPSM Case Study 2428.2.1 Identify Phase 2428.2.2 Design Phase 2448.2.3 Function Structures 2468.2.4 Reviewing and Matching Functions to Devices 2508.2.5 Summarizing Main Input and Output Flows 2508.2.6 Creating a Robust, Efficient, and Reliable System 2518.2.7 Supporting Effective Communication and Application of RDfR Principles 2528.2.8 Understanding Control Factors in Robust Optimization 2528.2.9 Type 1 Control Factor: Interaction with Noise Factor 2538.2.10 Type 2 Control Factor: No Interaction with Noise Factor 2538.2.11 Tailoring Optimization Strategies for Control Factors 2538.3 Achieving System Robustness through Optimization 2548.4 Optimize Phase 2548.4.1 P-Diagram: Linking Robustness and Serving as an Input for DFMEA 2558.5 Conclusion: Comprehensive Approach to Robust Optimization and Mistake Prevention 2638.5.1 Verify Phase Purpose in Robust Design for Reliability 263References 271Index 273

Matthew Hu, Senior Vice President, Engineering and Quality, Haylion Technologies, and Adjunct Professor, University of Houston, USA. Dr. Hu is a Certified Robust Design Expert using Taguchi Method, a Certified LSS Master Black Belt, and a certified DFSS Master Black Belt. Yan-Fu Li, Professor, Tsinghua University, China. He is the Principal Investigator (PI) of several government projects including the key project funded by National Natural Science Foundation of China.

• Series Editor’s Foreword xvPreface xviiAcknowledgments xxiii1 Enchaining Lifecycle Reliability with Robust Engineering and Prognostic Health Management 11.1 Introduction 21.2 Purpose 31.3 Essentials of Robustness and Robust Design in Reliability Improvement 41.4 Effective Reliability Efforts in an Integrated Product Development Environment 41.5 Enhancing Reliability Integration into the Product Development Process 61.6 Physics of Failure (PoF) 71.7 Failure-mode Avoidance 91.8 Design for Six Sigma 101.8.1 The Essence of Robustness Thinking 111.8.2 Robust Design as a Key Strategy 121.8.3 Paradigm Shift and Change 131.8.4 DFSS Roadmap: Emphasizing Robustness 131.9 Design for Reliability 161.10 Prognostics and Health Management 171.10.1 Health Indicators in Prognostic Health Management: Critical-to-Quality (CTQ) and Critical-to-Reliability (CTR) 211.10.2 Critical-to-Quality (CTQ) Parameters 211.10.3 Critical-to-Reliability (CTR) Parameters 211.10.4 Identification and Selection of CTQ and CTR Parameters 221.10.4.1 Robust Design for Reliability (RDfR) 221.10.5 Health Indicators in Prognostics and Health Management 221.11 The Importance of Digital Quality in Lifecycle Reliability Through Robustness Development and Predictive Health Management 231.12 Digital Quality in Lifecycle Reliability 231.12.1 Robustness Development 241.13 Predictive Health Management (PHM) 251.13.1 Integration of Digital Quality, Robustness Development, and PHM 261.14 Critical Parameter Development and Management (CPD&M): A Comprehensive Overview 271.14.1 The CPD&M Process 281.14.1.1 Initial Parameter Identification 281.14.1.2 The Seven Metrics 281.14.2 Continuous Improvement 28References 30Further Reading 312 Robustness Thinking and Strategies for Reliability Development 332.1 Introduction 342.1.1 Failure-Mode Avoidance: A Comprehensive Approach to Reliability 342.2 What Is Robustness Thinking? 402.3 The Challenge and Limitation of Conventional Reliability Approach 442.3.1 Uncertainty—Variation and Lack of Knowledge 442.3.1.1 Random Variation or Physical Uncertainty 462.3.1.2 Statistical Uncertainty 462.3.1.3 Model Uncertainty 462.3.1.4 Among These Three Types of Uncertainties 462.3.2 Traditional Reliability Challenges 472.3.3 Demand and Capacity—Statistical Modeling 532.3.4 Deterministic vs. Probabilistic Design 552.3.5 Understanding the Outer Array 592.3.6 Assessing Strength vs. Stress 592.3.7 P-Diagram 602.4 Why Robust Design? 612.5 The Importance and Principle of Flow in Robustness Thinking 622.5.1 Defining Flow 642.5.2 Transformation Systems, Flow, and Proactive Failure Creation 652.5.2.1 Load–Stress–Strength Thinking as a Proactive Reliability Framework 652.5.2.2 Margin, Limits, and Failure Distance 662.5.2.3 Noise Factors and Robust Design for Proactive Reliability 662.5.2.4 Architecture Robustness and Failure Propagation 672.5.2.5 Reliability Creation During Concept and Design 672.5.2.6 Summary: Robustness Thinking as Proactive Reliability 672.5.3 Importance of Flow in System Design and Optimization 672.5.4 Integrating Robustness Thinking and Robust Design Principles 672.5.5 Barriers to Flow Due to Lack of Robustness Thinking 682.5.6 Overcoming Barriers to Flow with Robustness Thinking 692.5.7 Examples of Barriers to Flow 692.5.8 Addressing Barriers with Robustness Thinking 702.6 Robustness Development Strategy 712.7 Three Phases of Robust Design 732.8 Understanding and Mitigating Mistakes in Design and Manufacturing 752.8.1 Improving Reliability by Reducing Mistakes 76References 77Further Reading 773 Robust Design Principles, Tactics, and Primary Tools 793.1 Introduction 793.2 Ideal Function: Ideal Transformation System Input and Output Relationship 803.3 Ideal Function and Quality Problems 813.4 Identification and Classification of Design Parameters: P-Diagram 823.5 Opportunity for Robustness Development 873.6 Two-Step Optimization 893.7 Robustness Measurement: S/N Ratio 903.8 S/N Ratio Improvement and Variation Reduction 923.9 S/N Ratio, the Additive Model, and the Conservative Laws of Physics 933.10 The Static Signal-to-Noise Ratios 943.10.1 Nominal-the-Best (NTB) Case 943.10.2 Smaller-the-Better (STB) 953.10.3 Larger-the-Better (LTB) 963.10.4 Operating Window (OW) Response 973.10.5 Classified Attribute Response 983.11 Dynamic Signal-to-Noise Ratios 983.11.1 Zero-Point Proportional Response 983.12 Robust Parameter Design Strategy and Steps 1003.12.1 Steps in Robust Parameter Design for Nominal-the-Best Characteristics 1063.13 Quality Measurement: Loss Function 1083.14 Robust Technology Development 109References 1144 Robust Design for Reliability (RDfR) A Comprehensive Approach to Product Excellence 1174.1 Introduction 1174.2 Robust Design for Reliability: A Comprehensive Approach to Product Excellence 1204.2.1 Preventing Failure Modes Through Vigilance 1234.2.1.1 Understanding the Entropic Nature of Mistakes 1234.2.1.2 Strengthening Organizational Vigilance 1234.3 Roadmap for Robust Design for Reliability Execution 1274.3.1 Identify Phase 1274.3.1.1 Identify Phase Purposes 1294.3.1.2 Identify Phase Activities 1324.3.1.3 Identify Phase Deliverables 1364.3.2 Design Phase 1364.3.3 Design Phase Purposes 1374.3.3.1 Design Phase Deliverables 1414.3.4 Optimize Phase 1424.3.4.1 Optimize Phase Purpose 1424.3.4.2 Robustness “Rules of Engagement” 1454.3.4.3 Optimize Phase Activities 1464.3.4.4 Optimize Phase Deliverables 1484.3.5 Verify Phase 1484.3.5.1 Verify Phase Purpose in Robust Design for Reliability 1484.3.5.2 Verify Phase Activities in Robust Design for Reliability 1514.3.5.3 Verify Phase Deliverables 1584.4 Robust Design Principles for Prognostic Health Management 1594.5 Scorecard for Robust Design for Reliability Implementation 1614.6 Digital Quality Through Robust Design for Reliability 1654.7 Critical Parameter Development and Management (CPD&M) Process and Phases 170References 171Further Reading 1725 Predictive & Health Management 1735.1 Justification for PHM in Robust System Design 1735.2 System Components and Their Functions 1765.2.1 PHM System Architecture 1765.2.2 Integration with Existing Maintenance Operations 1795.2.2.1 Maintenance and Maintenance Strategies 1795.2.2.2 Condition-based Maintenance (CBM) 1795.2.3 Scalability and Adaptability in PHM Design 1835.2.3.1 Activities of PHM and Reliability Over the Product Lifecycle 1835.2.3.2 Integration of Robust Design and PHM for Enhanced System Reliability 1845.2.3.3 The Power of Integrating Robust Engineering and PHM 1845.2.3.4 Assignment of Reliability and PHM Activities Over the Product Lifecycle 1855.2.3.5 The Role of PHM in the Product Lifecycle 1865.2.3.6 PHM System Development Process and Associated Standards 186References 1896 Characterizing Failure Signatures 1916.1 Characterizing Failure Signatures 1916.1.1 Identifying Degradation Patterns 1916.1.1.1 Synergistic Integration: Robust Design, Physics of Failure, and Degradation Pattern Identification 1926.1.2 Signature Analysis for Different System Components 1976.1.3 Signature Analysis Methods for Various System Components 2006.1.4 The Role of Signatures in Failure Prediction 2036.1.5 Data Collection for Signature Development 205References 2087 Guidelines for PHM System Implementation 2097.1 Enabling Technologies for PHM 2107.1.1 Sensor Technology Selection and Integration 2107.1.2 Developing Robust Sensor Technology and Integration Strategy for PHM 2107.1.2.1 Sensor Technology Development for PHM 2107.1.2.2 Conducting Robustness Assessment of Sensors 2117.2 Identifying and Selecting Robust Sensors for PHM 2117.3 Integration and Validation for PHM-Ready Systems 2117.4 Advanced Computing Platforms for PHM Analytics 2137.4.1 Edge Computing 2137.4.2 Cloud Computing 2137.4.3 Fog Computing 2147.4.4 Distributed Computing Frameworks 2147.4.5 High-performance Computing (HPC) 2147.5 AI-accelerated Hardware 2147.6 Evaluation Metrics for PHM Systems 2147.7 Robust PHM System 2157.7.1 Modular Architecture for PHM Systems 2157.7.2 Robustness, Redundancy, and Fault Tolerance in PHM System Design 2167.7.2.1 Redundancy in PHM Architecture 2177.7.2.2 Fault Tolerance Mechanisms 2177.7.2.3 Building for Long-Term Reliability and Cost Effectiveness 2187.7.3 User-centric Design for Ease of Integration 2187.7.4 Implementation Measures of User-centric Design in PHM 2197.8 Robust Prototype and Test-Bench Development for PHM System Validation 2197.8.1 System-level Requirements with Robustness in Mind 2197.9 Modular, Robust PHM Prototype Architecture 2207.10 Test-Bench Design for Robustness Validation 2207.11 Embedding Robustness into PHM Prototyping 2227.12 Verification Against Real-World Failure Data 2227.12.1 Why Real-World Data Validation Matters 2227.12.2 Types and Sources of Real-World Failure Data 2237.12.3 Public Benchmark Datasets 2237.12.4 Structured Methods for Real-World Verification 2237.12.5 Continuous System Evaluation Post-deployment 2247.12.6 Rationale for Continuous Evaluation 2247.12.7 Key Components of a Post-deployment Evaluation Framework 2257.13 Organizational Integration and Governance 2257.13.1 Strategic Implementation of PHM 2267.13.1.1 PHM-triggered Actions and Data Feedback Loop 2267.13.1.2 Enhancing PHM Models with Operational Data 2267.13.2 Future-proofing PHM Systems for Technological Advancements 2277.14 Case Study of PHM System Development 228References 2368 Case Study for Robust Design for Reliability (RDfR) 2398.1 Introduction 2398.2 RDfR Phases in DPSM Case Study 2428.2.1 Identify Phase 2428.2.2 Design Phase 2448.2.3 Function Structures 2468.2.4 Reviewing and Matching Functions to Devices 2508.2.5 Summarizing Main Input and Output Flows 2508.2.6 Creating a Robust, Efficient, and Reliable System 2518.2.7 Supporting Effective Communication and Application of RDfR Principles 2528.2.8 Understanding Control Factors in Robust Optimization 2528.2.9 Type 1 Control Factor: Interaction with Noise Factor 2538.2.10 Type 2 Control Factor: No Interaction with Noise Factor 2538.2.11 Tailoring Optimization Strategies for Control Factors 2538.3 Achieving System Robustness through Optimization 2548.4 Optimize Phase 2548.4.1 P-Diagram: Linking Robustness and Serving as an Input for DFMEA 2558.5 Conclusion: Comprehensive Approach to Robust Optimization and Mistake Prevention 2638.5.1 Verify Phase Purpose in Robust Design for Reliability 263References 271Index 273

Matthew Hu, Senior Vice President, Engineering and Quality, Haylion Technologies, and Adjunct Professor, University of Houston, USA. Dr. Hu is a Certified Robust Design Expert using Taguchi Method, a Certified LSS Master Black Belt, and a certified DFSS Master Black Belt. Yan-Fu Li, Professor, Tsinghua University, China. He is the Principal Investigator (PI) of several government projects including the key project funded by National Natural Science Foundation of China.

• Series Editor’s Foreword xvPreface xviiAcknowledgments xxiii1 Enchaining Lifecycle Reliability with Robust Engineering and Prognostic Health Management 11.1 Introduction 21.2 Purpose 31.3 Essentials of Robustness and Robust Design in Reliability Improvement 41.4 Effective Reliability Efforts in an Integrated Product Development Environment 41.5 Enhancing Reliability Integration into the Product Development Process 61.6 Physics of Failure (PoF) 71.7 Failure-mode Avoidance 91.8 Design for Six Sigma 101.8.1 The Essence of Robustness Thinking 111.8.2 Robust Design as a Key Strategy 121.8.3 Paradigm Shift and Change 131.8.4 DFSS Roadmap: Emphasizing Robustness 131.9 Design for Reliability 161.10 Prognostics and Health Management 171.10.1 Health Indicators in Prognostic Health Management: Critical-to-Quality (CTQ) and Critical-to-Reliability (CTR) 211.10.2 Critical-to-Quality (CTQ) Parameters 211.10.3 Critical-to-Reliability (CTR) Parameters 211.10.4 Identification and Selection of CTQ and CTR Parameters 221.10.4.1 Robust Design for Reliability (RDfR) 221.10.5 Health Indicators in Prognostics and Health Management 221.11 The Importance of Digital Quality in Lifecycle Reliability Through Robustness Development and Predictive Health Management 231.12 Digital Quality in Lifecycle Reliability 231.12.1 Robustness Development 241.13 Predictive Health Management (PHM) 251.13.1 Integration of Digital Quality, Robustness Development, and PHM 261.14 Critical Parameter Development and Management (CPD&M): A Comprehensive Overview 271.14.1 The CPD&M Process 281.14.1.1 Initial Parameter Identification 281.14.1.2 The Seven Metrics 281.14.2 Continuous Improvement 28References 30Further Reading 312 Robustness Thinking and Strategies for Reliability Development 332.1 Introduction 342.1.1 Failure-Mode Avoidance: A Comprehensive Approach to Reliability 342.2 What Is Robustness Thinking? 402.3 The Challenge and Limitation of Conventional Reliability Approach 442.3.1 Uncertainty—Variation and Lack of Knowledge 442.3.1.1 Random Variation or Physical Uncertainty 462.3.1.2 Statistical Uncertainty 462.3.1.3 Model Uncertainty 462.3.1.4 Among These Three Types of Uncertainties 462.3.2 Traditional Reliability Challenges 472.3.3 Demand and Capacity—Statistical Modeling 532.3.4 Deterministic vs. Probabilistic Design 552.3.5 Understanding the Outer Array 592.3.6 Assessing Strength vs. Stress 592.3.7 P-Diagram 602.4 Why Robust Design? 612.5 The Importance and Principle of Flow in Robustness Thinking 622.5.1 Defining Flow 642.5.2 Transformation Systems, Flow, and Proactive Failure Creation 652.5.2.1 Load–Stress–Strength Thinking as a Proactive Reliability Framework 652.5.2.2 Margin, Limits, and Failure Distance 662.5.2.3 Noise Factors and Robust Design for Proactive Reliability 662.5.2.4 Architecture Robustness and Failure Propagation 672.5.2.5 Reliability Creation During Concept and Design 672.5.2.6 Summary: Robustness Thinking as Proactive Reliability 672.5.3 Importance of Flow in System Design and Optimization 672.5.4 Integrating Robustness Thinking and Robust Design Principles 672.5.5 Barriers to Flow Due to Lack of Robustness Thinking 682.5.6 Overcoming Barriers to Flow with Robustness Thinking 692.5.7 Examples of Barriers to Flow 692.5.8 Addressing Barriers with Robustness Thinking 702.6 Robustness Development Strategy 712.7 Three Phases of Robust Design 732.8 Understanding and Mitigating Mistakes in Design and Manufacturing 752.8.1 Improving Reliability by Reducing Mistakes 76References 77Further Reading 773 Robust Design Principles, Tactics, and Primary Tools 793.1 Introduction 793.2 Ideal Function: Ideal Transformation System Input and Output Relationship 803.3 Ideal Function and Quality Problems 813.4 Identification and Classification of Design Parameters: P-Diagram 823.5 Opportunity for Robustness Development 873.6 Two-Step Optimization 893.7 Robustness Measurement: S/N Ratio 903.8 S/N Ratio Improvement and Variation Reduction 923.9 S/N Ratio, the Additive Model, and the Conservative Laws of Physics 933.10 The Static Signal-to-Noise Ratios 943.10.1 Nominal-the-Best (NTB) Case 943.10.2 Smaller-the-Better (STB) 953.10.3 Larger-the-Better (LTB) 963.10.4 Operating Window (OW) Response 973.10.5 Classified Attribute Response 983.11 Dynamic Signal-to-Noise Ratios 983.11.1 Zero-Point Proportional Response 983.12 Robust Parameter Design Strategy and Steps 1003.12.1 Steps in Robust Parameter Design for Nominal-the-Best Characteristics 1063.13 Quality Measurement: Loss Function 1083.14 Robust Technology Development 109References 1144 Robust Design for Reliability (RDfR) A Comprehensive Approach to Product Excellence 1174.1 Introduction 1174.2 Robust Design for Reliability: A Comprehensive Approach to Product Excellence 1204.2.1 Preventing Failure Modes Through Vigilance 1234.2.1.1 Understanding the Entropic Nature of Mistakes 1234.2.1.2 Strengthening Organizational Vigilance 1234.3 Roadmap for Robust Design for Reliability Execution 1274.3.1 Identify Phase 1274.3.1.1 Identify Phase Purposes 1294.3.1.2 Identify Phase Activities 1324.3.1.3 Identify Phase Deliverables 1364.3.2 Design Phase 1364.3.3 Design Phase Purposes 1374.3.3.1 Design Phase Deliverables 1414.3.4 Optimize Phase 1424.3.4.1 Optimize Phase Purpose 1424.3.4.2 Robustness “Rules of Engagement” 1454.3.4.3 Optimize Phase Activities 1464.3.4.4 Optimize Phase Deliverables 1484.3.5 Verify Phase 1484.3.5.1 Verify Phase Purpose in Robust Design for Reliability 1484.3.5.2 Verify Phase Activities in Robust Design for Reliability 1514.3.5.3 Verify Phase Deliverables 1584.4 Robust Design Principles for Prognostic Health Management 1594.5 Scorecard for Robust Design for Reliability Implementation 1614.6 Digital Quality Through Robust Design for Reliability 1654.7 Critical Parameter Development and Management (CPD&M) Process and Phases 170References 171Further Reading 1725 Predictive & Health Management 1735.1 Justification for PHM in Robust System Design 1735.2 System Components and Their Functions 1765.2.1 PHM System Architecture 1765.2.2 Integration with Existing Maintenance Operations 1795.2.2.1 Maintenance and Maintenance Strategies 1795.2.2.2 Condition-based Maintenance (CBM) 1795.2.3 Scalability and Adaptability in PHM Design 1835.2.3.1 Activities of PHM and Reliability Over the Product Lifecycle 1835.2.3.2 Integration of Robust Design and PHM for Enhanced System Reliability 1845.2.3.3 The Power of Integrating Robust Engineering and PHM 1845.2.3.4 Assignment of Reliability and PHM Activities Over the Product Lifecycle 1855.2.3.5 The Role of PHM in the Product Lifecycle 1865.2.3.6 PHM System Development Process and Associated Standards 186References 1896 Characterizing Failure Signatures 1916.1 Characterizing Failure Signatures 1916.1.1 Identifying Degradation Patterns 1916.1.1.1 Synergistic Integration: Robust Design, Physics of Failure, and Degradation Pattern Identification 1926.1.2 Signature Analysis for Different System Components 1976.1.3 Signature Analysis Methods for Various System Components 2006.1.4 The Role of Signatures in Failure Prediction 2036.1.5 Data Collection for Signature Development 205References 2087 Guidelines for PHM System Implementation 2097.1 Enabling Technologies for PHM 2107.1.1 Sensor Technology Selection and Integration 2107.1.2 Developing Robust Sensor Technology and Integration Strategy for PHM 2107.1.2.1 Sensor Technology Development for PHM 2107.1.2.2 Conducting Robustness Assessment of Sensors 2117.2 Identifying and Selecting Robust Sensors for PHM 2117.3 Integration and Validation for PHM-Ready Systems 2117.4 Advanced Computing Platforms for PHM Analytics 2137.4.1 Edge Computing 2137.4.2 Cloud Computing 2137.4.3 Fog Computing 2147.4.4 Distributed Computing Frameworks 2147.4.5 High-performance Computing (HPC) 2147.5 AI-accelerated Hardware 2147.6 Evaluation Metrics for PHM Systems 2147.7 Robust PHM System 2157.7.1 Modular Architecture for PHM Systems 2157.7.2 Robustness, Redundancy, and Fault Tolerance in PHM System Design 2167.7.2.1 Redundancy in PHM Architecture 2177.7.2.2 Fault Tolerance Mechanisms 2177.7.2.3 Building for Long-Term Reliability and Cost Effectiveness 2187.7.3 User-centric Design for Ease of Integration 2187.7.4 Implementation Measures of User-centric Design in PHM 2197.8 Robust Prototype and Test-Bench Development for PHM System Validation 2197.8.1 System-level Requirements with Robustness in Mind 2197.9 Modular, Robust PHM Prototype Architecture 2207.10 Test-Bench Design for Robustness Validation 2207.11 Embedding Robustness into PHM Prototyping 2227.12 Verification Against Real-World Failure Data 2227.12.1 Why Real-World Data Validation Matters 2227.12.2 Types and Sources of Real-World Failure Data 2237.12.3 Public Benchmark Datasets 2237.12.4 Structured Methods for Real-World Verification 2237.12.5 Continuous System Evaluation Post-deployment 2247.12.6 Rationale for Continuous Evaluation 2247.12.7 Key Components of a Post-deployment Evaluation Framework 2257.13 Organizational Integration and Governance 2257.13.1 Strategic Implementation of PHM 2267.13.1.1 PHM-triggered Actions and Data Feedback Loop 2267.13.1.2 Enhancing PHM Models with Operational Data 2267.13.2 Future-proofing PHM Systems for Technological Advancements 2277.14 Case Study of PHM System Development 228References 2368 Case Study for Robust Design for Reliability (RDfR) 2398.1 Introduction 2398.2 RDfR Phases in DPSM Case Study 2428.2.1 Identify Phase 2428.2.2 Design Phase 2448.2.3 Function Structures 2468.2.4 Reviewing and Matching Functions to Devices 2508.2.5 Summarizing Main Input and Output Flows 2508.2.6 Creating a Robust, Efficient, and Reliable System 2518.2.7 Supporting Effective Communication and Application of RDfR Principles 2528.2.8 Understanding Control Factors in Robust Optimization 2528.2.9 Type 1 Control Factor: Interaction with Noise Factor 2538.2.10 Type 2 Control Factor: No Interaction with Noise Factor 2538.2.11 Tailoring Optimization Strategies for Control Factors 2538.3 Achieving System Robustness through Optimization 2548.4 Optimize Phase 2548.4.1 P-Diagram: Linking Robustness and Serving as an Input for DFMEA 2558.5 Conclusion: Comprehensive Approach to Robust Optimization and Mistake Prevention 2638.5.1 Verify Phase Purpose in Robust Design for Reliability 263References 271Index 273