Enhancing Life Cycle Reliability with Robust Engineering and Predictive Health Management
Complete process for ensuring product performance through robust concept design, robust optimization, selection, and verification in an uncontrollable user environment
Enhancing Life Cycle Reliability with Robust Engineering and Predictive Health Management enables readers to build a robustness-thinking-based approach for robust design for reliability and prognostic health management (PHM), explaining best practices from early product design through the entire product lifecycle, leading to lower costs and shorter development cycles. The text integrates key tools and emerging reliability management systems into a comprehensive program for developing more robust and reliable technology-based products.
The text provides value-added strategies for robustness development in new products and health management with three main types of robustness development and reliability growth case studies: intrinsic, instrumental, and collective. Readers can harness multiple forms of engineering knowledge to inform decision-making within reliability contexts.
To ensure customer satisfaction, the text helps readers consciously consider noise factors (environmental variation during the product’s usage, manufacturing variation, and component deterioration) and cost of failure in the field for the Robust Design method.
Written by two highly qualified authors, this book includes information on:
- Effective reliability efforts in an integrated product development environment, failure mode avoidance, and reliability analysis using the physics-of-failure process
- Essentials of robustness and robust design in reliability improvement, covering design-in reliability up front, eliminating failures prior to testing, and increasing fielded reliability
- Rapid, cost-effective deployment of health and usage monitoring systems and improving diagnostic and prognostic techniques and processes
- ROI analyses for PHM, selecting and deploying sensors, setting up data transmission channels, and developing data collection and data pre-processing functions
Comprehensive in scope, this book is an essential resource on the subject for all individuals responsible for product development and design, increasing life-cycle product reliability, process quality, or reducing costs in a design, development, manufacturing, and maintenance.
Table of Contents:
Series Editor’s Foreword xv
Preface xvii
Acknowledgments xxiii
1 Enchaining Lifecycle Reliability with Robust Engineering and Prognostic Health Management 1
1.1 Introduction 2
1.2 Purpose 3
1.3 Essentials of Robustness and Robust Design in Reliability Improvement 4
1.4 Effective Reliability Efforts in an Integrated Product Development Environment 4
1.5 Enhancing Reliability Integration into the Product Development Process 6
1.6 Physics of Failure (PoF) 7
1.7 Failure-mode Avoidance 9
1.8 Design for Six Sigma 10
1.8.1 The Essence of Robustness Thinking 11
1.8.2 Robust Design as a Key Strategy 12
1.8.3 Paradigm Shift and Change 13
1.8.4 DFSS Roadmap: Emphasizing Robustness 13
1.9 Design for Reliability 16
1.10 Prognostics and Health Management 17
1.10.1 Health Indicators in Prognostic Health Management: Critical-to-Quality (CTQ) and Critical-to-Reliability (CTR) 21
1.10.2 Critical-to-Quality (CTQ) Parameters 21
1.10.3 Critical-to-Reliability (CTR) Parameters 21
1.10.4 Identification and Selection of CTQ and CTR Parameters 22
1.10.4.1 Robust Design for Reliability (RDfR) 22
1.10.5 Health Indicators in Prognostics and Health Management 22
1.11 The Importance of Digital Quality in Lifecycle Reliability Through Robustness Development and Predictive Health Management 23
1.12 Digital Quality in Lifecycle Reliability 23
1.12.1 Robustness Development 24
1.13 Predictive Health Management (PHM) 25
1.13.1 Integration of Digital Quality, Robustness Development, and PHM 26
1.14 Critical Parameter Development and Management (CPD&M): A Comprehensive Overview 27
1.14.1 The CPD&M Process 28
1.14.1.1 Initial Parameter Identification 28
1.14.1.2 The Seven Metrics 28
1.14.2 Continuous Improvement 28
References 30
Further Reading 31
2 Robustness Thinking and Strategies for Reliability Development 33
2.1 Introduction 34
2.1.1 Failure-Mode Avoidance: A Comprehensive Approach to Reliability 34
2.2 What Is Robustness Thinking? 40
2.3 The Challenge and Limitation of Conventional Reliability Approach 44
2.3.1 Uncertainty—Variation and Lack of Knowledge 44
2.3.1.1 Random Variation or Physical Uncertainty 46
2.3.1.2 Statistical Uncertainty 46
2.3.1.3 Model Uncertainty 46
2.3.1.4 Among These Three Types of Uncertainties 46
2.3.2 Traditional Reliability Challenges 47
2.3.3 Demand and Capacity—Statistical Modeling 53
2.3.4 Deterministic vs. Probabilistic Design 55
2.3.5 Understanding the Outer Array 59
2.3.6 Assessing Strength vs. Stress 59
2.3.7 P-Diagram 60
2.4 Why Robust Design? 61
2.5 The Importance and Principle of Flow in Robustness Thinking 62
2.5.1 Defining Flow 64
2.5.2 Transformation Systems, Flow, and Proactive Failure Creation 65
2.5.2.1 Load–Stress–Strength Thinking as a Proactive Reliability Framework 65
2.5.2.2 Margin, Limits, and Failure Distance 66
2.5.2.3 Noise Factors and Robust Design for Proactive Reliability 66
2.5.2.4 Architecture Robustness and Failure Propagation 67
2.5.2.5 Reliability Creation During Concept and Design 67
2.5.2.6 Summary: Robustness Thinking as Proactive Reliability 67
2.5.3 Importance of Flow in System Design and Optimization 67
2.5.4 Integrating Robustness Thinking and Robust Design Principles 67
2.5.5 Barriers to Flow Due to Lack of Robustness Thinking 68
2.5.6 Overcoming Barriers to Flow with Robustness Thinking 69
2.5.7 Examples of Barriers to Flow 69
2.5.8 Addressing Barriers with Robustness Thinking 70
2.6 Robustness Development Strategy 71
2.7 Three Phases of Robust Design 73
2.8 Understanding and Mitigating Mistakes in Design and Manufacturing 75
2.8.1 Improving Reliability by Reducing Mistakes 76
References 77
Further Reading 77
3 Robust Design Principles, Tactics, and Primary Tools 79
3.1 Introduction 79
3.2 Ideal Function: Ideal Transformation System Input and Output Relationship 80
3.3 Ideal Function and Quality Problems 81
3.4 Identification and Classification of Design Parameters: P-Diagram 82
3.5 Opportunity for Robustness Development 87
3.6 Two-Step Optimization 89
3.7 Robustness Measurement: S/N Ratio 90
3.8 S/N Ratio Improvement and Variation Reduction 92
3.9 S/N Ratio, the Additive Model, and the Conservative Laws of Physics 93
3.10 The Static Signal-to-Noise Ratios 94
3.10.1 Nominal-the-Best (NTB) Case 94
3.10.2 Smaller-the-Better (STB) 95
3.10.3 Larger-the-Better (LTB) 96
3.10.4 Operating Window (OW) Response 97
3.10.5 Classified Attribute Response 98
3.11 Dynamic Signal-to-Noise Ratios 98
3.11.1 Zero-Point Proportional Response 98
3.12 Robust Parameter Design Strategy and Steps 100
3.12.1 Steps in Robust Parameter Design for Nominal-the-Best Characteristics 106
3.13 Quality Measurement: Loss Function 108
3.14 Robust Technology Development 109
References 114
4 Robust Design for Reliability (RDfR) A Comprehensive Approach to Product Excellence 117
4.1 Introduction 117
4.2 Robust Design for Reliability: A Comprehensive Approach to Product Excellence 120
4.2.1 Preventing Failure Modes Through Vigilance 123
4.2.1.1 Understanding the Entropic Nature of Mistakes 123
4.2.1.2 Strengthening Organizational Vigilance 123
4.3 Roadmap for Robust Design for Reliability Execution 127
4.3.1 Identify Phase 127
4.3.1.1 Identify Phase Purposes 129
4.3.1.2 Identify Phase Activities 132
4.3.1.3 Identify Phase Deliverables 136
4.3.2 Design Phase 136
4.3.3 Design Phase Purposes 137
4.3.3.1 Design Phase Deliverables 141
4.3.4 Optimize Phase 142
4.3.4.1 Optimize Phase Purpose 142
4.3.4.2 Robustness “Rules of Engagement” 145
4.3.4.3 Optimize Phase Activities 146
4.3.4.4 Optimize Phase Deliverables 148
4.3.5 Verify Phase 148
4.3.5.1 Verify Phase Purpose in Robust Design for Reliability 148
4.3.5.2 Verify Phase Activities in Robust Design for Reliability 151
4.3.5.3 Verify Phase Deliverables 158
4.4 Robust Design Principles for Prognostic Health Management 159
4.5 Scorecard for Robust Design for Reliability Implementation 161
4.6 Digital Quality Through Robust Design for Reliability 165
4.7 Critical Parameter Development and Management (CPD&M) Process and Phases 170
References 171
Further Reading 172
5 Predictive & Health Management 173
5.1 Justification for PHM in Robust System Design 173
5.2 System Components and Their Functions 176
5.2.1 PHM System Architecture 176
5.2.2 Integration with Existing Maintenance Operations 179
5.2.2.1 Maintenance and Maintenance Strategies 179
5.2.2.2 Condition-based Maintenance (CBM) 179
5.2.3 Scalability and Adaptability in PHM Design 183
5.2.3.1 Activities of PHM and Reliability Over the Product Lifecycle 183
5.2.3.2 Integration of Robust Design and PHM for Enhanced System Reliability 184
5.2.3.3 The Power of Integrating Robust Engineering and PHM 184
5.2.3.4 Assignment of Reliability and PHM Activities Over the Product Lifecycle 185
5.2.3.5 The Role of PHM in the Product Lifecycle 186
5.2.3.6 PHM System Development Process and Associated Standards 186
References 189
6 Characterizing Failure Signatures 191
6.1 Characterizing Failure Signatures 191
6.1.1 Identifying Degradation Patterns 191
6.1.1.1 Synergistic Integration: Robust Design, Physics of Failure, and Degradation Pattern Identification 192
6.1.2 Signature Analysis for Different System Components 197
6.1.3 Signature Analysis Methods for Various System Components 200
6.1.4 The Role of Signatures in Failure Prediction 203
6.1.5 Data Collection for Signature Development 205
References 208
7 Guidelines for PHM System Implementation 209
7.1 Enabling Technologies for PHM 210
7.1.1 Sensor Technology Selection and Integration 210
7.1.2 Developing Robust Sensor Technology and Integration Strategy for PHM 210
7.1.2.1 Sensor Technology Development for PHM 210
7.1.2.2 Conducting Robustness Assessment of Sensors 211
7.2 Identifying and Selecting Robust Sensors for PHM 211
7.3 Integration and Validation for PHM-Ready Systems 211
7.4 Advanced Computing Platforms for PHM Analytics 213
7.4.1 Edge Computing 213
7.4.2 Cloud Computing 213
7.4.3 Fog Computing 214
7.4.4 Distributed Computing Frameworks 214
7.4.5 High-performance Computing (HPC) 214
7.5 AI-accelerated Hardware 214
7.6 Evaluation Metrics for PHM Systems 214
7.7 Robust PHM System 215
7.7.1 Modular Architecture for PHM Systems 215
7.7.2 Robustness, Redundancy, and Fault Tolerance in PHM System Design 216
7.7.2.1 Redundancy in PHM Architecture 217
7.7.2.2 Fault Tolerance Mechanisms 217
7.7.2.3 Building for Long-Term Reliability and Cost Effectiveness 218
7.7.3 User-centric Design for Ease of Integration 218
7.7.4 Implementation Measures of User-centric Design in PHM 219
7.8 Robust Prototype and Test-Bench Development for PHM System Validation 219
7.8.1 System-level Requirements with Robustness in Mind 219
7.9 Modular, Robust PHM Prototype Architecture 220
7.10 Test-Bench Design for Robustness Validation 220
7.11 Embedding Robustness into PHM Prototyping 222
7.12 Verification Against Real-World Failure Data 222
7.12.1 Why Real-World Data Validation Matters 222
7.12.2 Types and Sources of Real-World Failure Data 223
7.12.3 Public Benchmark Datasets 223
7.12.4 Structured Methods for Real-World Verification 223
7.12.5 Continuous System Evaluation Post-deployment 224
7.12.6 Rationale for Continuous Evaluation 224
7.12.7 Key Components of a Post-deployment Evaluation Framework 225
7.13 Organizational Integration and Governance 225
7.13.1 Strategic Implementation of PHM 226
7.13.1.1 PHM-triggered Actions and Data Feedback Loop 226
7.13.1.2 Enhancing PHM Models with Operational Data 226
7.13.2 Future-proofing PHM Systems for Technological Advancements 227
7.14 Case Study of PHM System Development 228
References 236
8 Case Study for Robust Design for Reliability (RDfR) 239
8.1 Introduction 239
8.2 RDfR Phases in DPSM Case Study 242
8.2.1 Identify Phase 242
8.2.2 Design Phase 244
8.2.3 Function Structures 246
8.2.4 Reviewing and Matching Functions to Devices 250
8.2.5 Summarizing Main Input and Output Flows 250
8.2.6 Creating a Robust, Efficient, and Reliable System 251
8.2.7 Supporting Effective Communication and Application of RDfR Principles 252
8.2.8 Understanding Control Factors in Robust Optimization 252
8.2.9 Type 1 Control Factor: Interaction with Noise Factor 253
8.2.10 Type 2 Control Factor: No Interaction with Noise Factor 253
8.2.11 Tailoring Optimization Strategies for Control Factors 253
8.3 Achieving System Robustness through Optimization 254
8.4 Optimize Phase 254
8.4.1 P-Diagram: Linking Robustness and Serving as an Input for DFMEA 255
8.5 Conclusion: Comprehensive Approach to Robust Optimization and Mistake Prevention 263
8.5.1 Verify Phase Purpose in Robust Design for Reliability 263
References 271
Index 273
About the Author :
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.
