Prognostics and health management : a practical approach to improving system reliability using conditioned-based data için kapak resmi
Başlık:
Prognostics and health management : a practical approach to improving system reliability using conditioned-based data
Yazar:
Goodman, Douglas (Industrial engineer), author.
ISBN:
9781119356707

9781119356691

9781119356677

9781119356653
Fiziksel Tanımlama:
1 online resource
İçerik:
Effects and Conditioning 34 CHAPTER 6: DESIGN: ROBUST PROTOTYPE OF AN EXEMPLARY PHM SYSTEM 1 6.1 PHM System: Review 1 6.1.1 Chapter 1: Introduction to Prognostics 1 6.1.2 Chapter 2: Prognostic Approaches for Prognosis and Health Management 4 6.1.3 Chapter 3: Failure Progression Signatures 7 6.1.4 Chapter 4: Heuristic-based Approach to Modeling CBD Signatures 10 6.1.5 Chapter 5: Non-ideal Data: Effects and Conditioning 10 6.1.6 Objectives for this Chapter 12 6.1.7 Chapter Organization 13 6.2 Design Approaches for a PHM System 15 6.2.1 Select and Evaluate Targets and Their Failure Modes 15 6.2.2 Offline Prognostic Approaches: Selection of Results 16 6.2.3 Select a Base Architecture for the Online Phase 17 6.3 Sampling and Polling 17 6.3.1 Continual - Periodic Sampling 18 6.3.2 Periodic-burst Sampling 18 6.3.3 Polling 21 6.4 Initial Design
Özet:
72 CHAPTER 2: APPROACHES FOR PROGNOSIS AND HEALTH MANAGEMENT (PHM) 1 2.1 Approaches for Prognosis and Health Management (PHM) 1 2.1.1 Model-based Prognostic Approaches 1 2.1.2 Data-driven Prognostic Approaches 2 2.1.3 Hybrid Prognostic Approaches 2 2.1.4 Objectives for this Chapter 3 2.1.5 Chapter Organization 3 2.2 Model-based Prognostics 2.2.1 Analytical Modeling 5 2.2.2 Distribution Modeling 10 2.2.3 Physics of Failure (PoF) and Reliability Modeling 12 2.2.4 Acceleration Factor (AF) 14 2.2.5 Complexity Related to Reliability Modeling 16 2.2.6 Failure Distribution 18 2.2.7 Multiple Modes of Failure: Failure Rate and FIT 19 2.2.8 Advantages and Disadvantages of Model-based Prognostics 19 2.3 Data-driven Prognostics 20 2.3.1 Statistical Methods 20 2.3.2 Machine Learning (ML): Classification and Clustering 26 2.4.
Notlar:
John Wiley and Sons
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