Enhancing Life Cycle Reliability with Robust Engineering and Prognostic Health Management

Inbunden, Engelska, 2024

2 049 kr

Kommande

Complete process for ensuring product performance through robust concept design, robust optimization, selection, and verification in an uncontrollable user environment Life Cycle Reliability through Robustness Development, and Prognostic and 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, Life Cycle Reliability through Robustness Development, and Prognostic and Health Management includes information on: Effective reliability efforts in an integrated product development environment, failure mode avoidance, and reliability analysis using the physics-of-failure processEssential of robustness and robust design in reliability improvement, covering design-in reliability up front, eliminating failures prior to testing, and increasing fielded reliabilityRapid, cost-effective deployment of health and usage monitoring systems and improving diagnostic and prognostic techniques and processesROI analyses for PHM, selecting and deploying sensors, setting up data transmission channels, and developing data collection and data pre-processing functionsComprehensive in scope, Life Cycle Reliability through Robustness Development, and Prognostic and Health Management 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.

Produktinformation

Utgivningsdatum2024-10-17
FormatInbunden
SpråkEngelska
SerieQuality and Reliability Engineering Series
Antal sidor320
FörlagJohn Wiley & Sons Inc
ISBN9781394182381

Tillhör följande kategorier

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.

I. Scope 1.1. Purpose 1.2. Essential of Robustness and Robust Design in Reliability Improvement1.3. Effective Reliability efforts in an Integrated Product Development Environment1.4. Enhancing Reliability Integration into the Product Development Process1.5. Physics of Failures: Reliability Analysis Using the Physics-of-Failure Process1.6. Failure Mode Avoidance1.7. Design for Six Sigma1.8. Robust Design for Reliability1.9. Prognostic Health Management1.10. The importance of digital quality in Life Cycle Reliability through RobustnessDevelopment and Prognostics & Health Management II. Robustness Thinking and Strategies for Reliability Development2.1 Introduction2.2 What Is Robustness Thinking?2.3 Why Robust Design?2.4 The Concept of "Flow" in Robust Design2.5 Robustness Development Strategy2.6 Three Phases of Robust Design2.7 Robustness Measurement: Measurement of Function using S/N ratio.2.7.1 Static S/N Ratio2.7.2 Dynamic S/N Ratio III. Chapter 3: Robust Design Principles, Tactics and Primary Tools 3.1 Introduction3.2 Ideal Function: Ideal Transformation System Input and output relationship3.3 Ideal Function and Quality Problems3.4 Identification and Classification of Design Parameters: P-Diagram3.5 Robustness Measurement: Measurement of Function using S/N Ratio3.6 S/N Ratio Improvement and Variation Reduction3.7 S/N Ratio, the Additive Model and the Conservative Laws of Physics3.8 Opportunity for Robustness Development: Interactions between Control and Noise Factors3.9 Two-step Optimization3.10 Robust Parameter Design Strategy and Steps3.11 Quality Measurement: Loss Function IV. Chapter 4: Robust Design for Reliability (RDfR)A Comprehensive Approach to Product Excellence 4.1 Introduction4.2 A Comprehensive Approach to Product Excellence4.2.1 Building a Strong RDfR Team4.2.2 Concurrent Engineering in RDfR4.2.3 Implementation Readiness for RDfR4.2.4 Preventing Failure Modes Through Vigilance4.2.5 Supply Chain Integration in RDfR4.2.6 Regulatory Compliance in RDfR4.2.7 Digital Quality in RDfR4.3 Roadmap for Robust Design for Reliability Execution4.3.1 Identify Phase4.3.1.1 Identify Phase Purposes4.3.1.2 Identify Phase Activities4.3.1.3 Identify Phase Deliverables4.3.2 Design Phase 4.3.2.1 Design Phase Purposes4.3.2.2 Design Phase Activities4.3.2.3 Design Phase Deliverables4.3.3 Optimize Phase4.3.3.1 Optimize Phase Purpose4.3.3.2 Robustness "Rules of Engagement"4.3.3.3 Optimize Phase Activities4.3.3.4 Optimize Phase Deliverables4.3.4 Verify Phase4.3.4.1 Verify Phase Purpose4.3.4.2 Verify Phase Activities4.3.4.3 Verify Phase Deliverables4.4 Robust Design for Reliability Principles for Prognostics & Health Management (PHM)4.5 Scorecard for Robust Design for Reliability Implementation4.6 Digital quality through Robust Design for Reliability4.7 Critical Parameter Development and Management (CPD&M) Process andPhases V. Prognostics & Health Management CHAPTER 5:5.1.1. Justification for Prognostic Health Management (PHM) in Robust System Design5.2.1 PHM System Architecture5.3.1. System Components and their Functions5.3.2. Integration with Existing Maintenance Operations5.3.3. Scalability and Adaptability in PHM Design CHAPTER 6: Failure Signatures and Imperfect Data treatment6.1. Characterizing Failure Signatures6.1.1. Identifying Degradation Patterns6.1.2. Signature Analysis for Different System Components6.1.3. The Role of Signatures in Failure Prediction6.1.4. Data Collection for Signature Development6.1.5. Data Envelopment Analysis (DEA) for Signature Refinement6.2. Advanced Signature Analysis Techniques6.2.1. Machine Learning for Signature Recognition6.2.2. Multivariate Analysis of Complex Signatures6.2.3. Signature Synthesis for Enhanced Prognostic Accuracy6.2.4. Heuristic Principles in CBD Modeling6.3. Impact of Imperfect Data on PHM6.3.1. Sources and Types of Imperfect Data6.3.2. Effects of Data Quality on Prognostic Accuracy6.3.3. Data Preprocessing for PHM6.3.4. Advanced Data Conditioning Methods CHAPTER 7: Guidelines for PHM system implementation7.1. Enabling Technologies for PHM7.1.1. Sensor Technology Selection and Integration7.1.2. Developing Robust Sensor Technology and Integration Strategy for PHM7.1.2.1. Sensor Technology Development for PHM7.1.2.2. Conducting Robustness Assessment of Sensors7.1.3. Advanced Computing Platforms for PHM Analytics7.1.4. Evaluation Metrics for PHM Systems7.1.5. Economic Justification for PHM Implementation7.2. Identifying and Selecting Robust Sensors for PHM7.2.1. Modular Architecture for PHM Systems7.2.2. Redundancy and Fault Tolerance in System Design7.2.3. User-Centric Design for Ease of Integration7.2.4. Prototype/Test-Bench Development for PHM System Validation7.2.5. Verification Against Real-World Failure Data7.2.6. Continuous System Evaluation Post-Deployment7.3. Integration and Validation for PHM-Ready Systems7.4. Hardware-Level Integration7.5. Validation, Calibration, and Fault Tolerance7.6. Advanced Computing Platforms for PHM Analytics7.6.1. Edge Computing7.6.2. Cloud Computing7.6.3. Fog Computing7.6.4. Distributed Computing Frameworks7.6.5. High-Performance Computing (HPC)7.7. AI-Accelerated Hardware7.8. Evaluation Metrics for PHM Systems7.9. Robust PHM System7.9.1. Modular Architecture for PHM Systems7.9.2. Robustness, Redundancy, and Fault Tolerance in PHM System Design7.9.2.1. Redundancy in PHM Architecture7.9.2.2. Fault Tolerance Mechanisms7.9.2.3. Building for Long-Term Reliability and Cost Effectiveness7.9.3. User-Centric Design for Ease of Integration7.9.4. Implementation Measures of User-Centric Design in PHM7.10. Robust Prototype and Test-Bench Development for PHM System Validation7.10.1. System-Level Requirements with Robustness in Mind7.11. Modular, Robust PHM Prototype Architecture7.12. Test-Bench Design for Robustness Validation7.13. Embedding Robustness into PHM Prototyping7.13.1. Modular Prototype Architecture7.14. Verification Against Real-World Failure Data7.14.1 Why Real-World Data Validation Matters7.14.2. Types and Sources of Real-World Failure Data7.14.3. Public Benchmark Datasets7.14.4. Structured Methods for Real-World Verification7.14.5. Continuous System Evaluation Post-Deployment7.14.6. Rationale for Continuous Evaluation7.14.7. Key Components of a Post-Deployment Evaluation Framework7.15. Organizational Integration and Governance7.15.1. Strategic Implementation of PHM7.15.2. Future-Proofing PHM Systems for Technological Advancements7.16 Case Study of PHM System Development: CHAPTER 8: Case Study for Robust Design for Reliability (RDfR)8.1 RDfR Case Study: Detonator Power Supply Module (DPSM)8.1.1. Overview8.1.2. RDfR Team Building8.1.3. Why DPSM Was Chosen8.1.4. RDfR Scorecard as a Framework8.1.5. Key Elements of the RDfR Case Study8.1.6. RDfR Phases in DPSM Case Study8.1.7. Summary of Benefits for the Robust Design for Reliability (RDfR) Case Study onthe Detonator Power Supply Module (DPSM)