{"product_id":"prognostics-and-health-management-9781119356653","title":"Prognostics and Health Management","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eA comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life.\u003c\/b\u003e\u003ci\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003ePrognostics and Health Management\u003c\/i\u003e provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improving system reliability using conditioned-based data (CBD) to the monitoring and management of health of systems. This proven approach uses electronic signatures extracted from conditioned-based electrical signals, including those representing physical components, and employs processing methods that include data fusion and transformation, domain transformation, and normalization, canonicalization and signal-level translation to support the determination of predictive diagnostics and prognostics.\u003c\/p\u003e \u003cp\u003eWritten by noted experts in the field, \u003ci\u003ePrognostics and Health Management\u003c\/i\u003e clearly describes how to extract signatures from conditioned-based data using condi\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eList of Figures xi\u003c\/p\u003e \u003cp\u003eSeries Editor’s Foreword xxi\u003c\/p\u003e \u003cp\u003ePreface xxiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Prognostics \u003c\/b\u003e\u003cb\u003e\u003ci\u003e1\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 What Is Prognostics? 1\u003c\/p\u003e \u003cp\u003e1.1.1 Chapter Objectives 1\u003c\/p\u003e \u003cp\u003e1.1.2 Chapter Organization 3\u003c\/p\u003e \u003cp\u003e1.2 Foundation of Reliability Theory 3\u003c\/p\u003e \u003cp\u003e1.2.1 Time-to-Failure Distributions 4\u003c\/p\u003e \u003cp\u003e1.2.2 Probability and Reliability 6\u003c\/p\u003e \u003cp\u003e1.2.3 Probability Density Function 7\u003c\/p\u003e \u003cp\u003e1.2.4 Relationships of Distributions 10\u003c\/p\u003e \u003cp\u003e1.2.5 Failure Rate 10\u003c\/p\u003e \u003cp\u003e1.2.6 Expected Value and Variance 16\u003c\/p\u003e \u003cp\u003e1.3 Failure Distributions Under Extreme Stress Levels 18\u003c\/p\u003e \u003cp\u003e1.3.1 Basic Models 18\u003c\/p\u003e \u003cp\u003e1.3.2 Cumulative Damage Models 21\u003c\/p\u003e \u003cp\u003e1.3.3 General Exponential Models 21\u003c\/p\u003e \u003cp\u003e1.4 Uncertainty Measures in Parameter Estimation 23\u003c\/p\u003e \u003cp\u003e1.5 Expected Number of Failures 26\u003c\/p\u003e \u003cp\u003e1.5.1 Minimal Repair 26\u003c\/p\u003e \u003cp\u003e1.5.2 Failure Replacement 28\u003c\/p\u003e \u003cp\u003e1.5.3 Decreased Number of Failures Due to Partial Repairs 30\u003c\/p\u003e \u003cp\u003e1.5.4 Decreased Age Due to Partial Repairs 30\u003c\/p\u003e \u003cp\u003e1.6 System Reliability and Prognosis and Health Management 31\u003c\/p\u003e \u003cp\u003e1.6.1 General Framework for a CBM-Based PHM System 32\u003c\/p\u003e \u003cp\u003e1.6.2 Relationship of PHM to System Reliability 34\u003c\/p\u003e \u003cp\u003e1.6.3 Degradation Progression Signature (DPS) and Prognostics 35\u003c\/p\u003e \u003cp\u003e1.6.4 Ideal Functional Failure Signature (FFS) and Prognostics 37\u003c\/p\u003e \u003cp\u003e1.6.5 Non-ideal FFS and Prognostics 41\u003c\/p\u003e \u003cp\u003e1.7 Prognostic Information 41\u003c\/p\u003e \u003cp\u003e1.7.1 Non-ideality: Initial-Estimate Error and Remaining Useful Life (RUL) 42\u003c\/p\u003e \u003cp\u003e1.7.2 Convergence of RUL Estimates Given an Initial Estimate Error 44\u003c\/p\u003e \u003cp\u003e1.7.3 Prognostic Distance (PD) and Convergence 45\u003c\/p\u003e \u003cp\u003e1.7.4 Convergence: Figure of Merit (𝜒\u003csub\u003e𝛼\u003c\/sub\u003e) 45\u003c\/p\u003e \u003cp\u003e1.7.5 Other Sources of Non-ideality in FFS Data 46\u003c\/p\u003e \u003cp\u003e1.8 Decisions on Cost and Benefits 47\u003c\/p\u003e \u003cp\u003e1.8.1 Product Selection 47\u003c\/p\u003e \u003cp\u003e1.8.2 Optimal Maintenance Scheduling 49\u003c\/p\u003e \u003cp\u003e1.8.3 Condition-Based Maintenance or Replacement 54\u003c\/p\u003e \u003cp\u003e1.8.4 Preventive Replacement Scheduling 55\u003c\/p\u003e \u003cp\u003e1.8.5 Model Variants and Extensions 58\u003c\/p\u003e \u003cp\u003e1.9 Introduction to PHM: Summary 60\u003c\/p\u003e \u003cp\u003eReferences 60\u003c\/p\u003e \u003cp\u003eFurther Reading 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Approaches for Prognosis and Health Management\/Monitoring (PHM) \u003c\/b\u003e\u003cb\u003e63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction to Approaches for Prognosis and Health Management\/Monitoring (PHM) 63\u003c\/p\u003e \u003cp\u003e2.1.1 Model-Based Prognostic Approaches 63\u003c\/p\u003e \u003cp\u003e2.1.2 Data-Driven Prognostic Approaches 63\u003c\/p\u003e \u003cp\u003e2.1.3 Hybrid Prognostic Approaches 64\u003c\/p\u003e \u003cp\u003e2.1.4 Chapter Objectives 64\u003c\/p\u003e \u003cp\u003e2.1.5 Chapter Organization 64\u003c\/p\u003e \u003cp\u003e2.2 Model-Based Prognostics 65\u003c\/p\u003e \u003cp\u003e2.2.1 Analytical Modeling 66\u003c\/p\u003e \u003cp\u003e2.2.2 Distribution Modeling 71\u003c\/p\u003e \u003cp\u003e2.2.3 Physics of Failure (PoF) and Reliability Modeling 72\u003c\/p\u003e \u003cp\u003e2.2.4 Acceleration Factor (AF) 74\u003c\/p\u003e \u003cp\u003e2.2.5 Complexity Related to Reliability Modeling 76\u003c\/p\u003e \u003cp\u003e2.2.6 Failure Distribution 78\u003c\/p\u003e \u003cp\u003e2.2.7 Multiple Modes of Failure: Failure Rate and FIT 79\u003c\/p\u003e \u003cp\u003e2.2.8 Advantages and Disadvantages of Model-Based Prognostics 79\u003c\/p\u003e \u003cp\u003e2.3 Data-Driven Prognostics 80\u003c\/p\u003e \u003cp\u003e2.3.1 Statistical Methods 80\u003c\/p\u003e \u003cp\u003e2.3.2 Machine Learning (ML): Classification and Clustering 85\u003c\/p\u003e \u003cp\u003e2.4 Hybrid-Driven Prognostics 90\u003c\/p\u003e \u003cp\u003e2.5 An Approach to Condition-Based Maintenance (CBM) 92\u003c\/p\u003e \u003cp\u003e2.5.1 Modeling of Condition-Based Data (CBD) Signatures 92\u003c\/p\u003e \u003cp\u003e2.5.2 Comparison of Methodologies: Life Consumption and CBD Signature 92\u003c\/p\u003e \u003cp\u003e2.5.3 CBD-Signature Modeling: An Illustration 93\u003c\/p\u003e \u003cp\u003e2.6 Approaches to PHM: Summary 103\u003c\/p\u003e \u003cp\u003eReferences 103\u003c\/p\u003e \u003cp\u003eFurther Reading 106\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Failure Progression Signatures \u003c\/b\u003e\u003cb\u003e107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction to Failure Signatures 107\u003c\/p\u003e \u003cp\u003e3.1.1 Chapter Objectives 107\u003c\/p\u003e \u003cp\u003e3.1.2 Chapter Organization 108\u003c\/p\u003e \u003cp\u003e3.2 Basic Types of Signatures 108\u003c\/p\u003e \u003cp\u003e3.2.1 CBD Signature 109\u003c\/p\u003e \u003cp\u003e3.2.2 FFP Signature 114\u003c\/p\u003e \u003cp\u003e3.2.3 Transforming FFP into FFS 118\u003c\/p\u003e \u003cp\u003e3.2.4 Transforming FFP into a Degradation Progression Signature (DPS) 120\u003c\/p\u003e \u003cp\u003e3.2.5 Transforming DPS into DPS-Based FFS 122\u003c\/p\u003e \u003cp\u003e3.3 Model Verification 124\u003c\/p\u003e \u003cp\u003e3.3.1 Signature Classification 124\u003c\/p\u003e \u003cp\u003e3.3.2 Verifying CBD Modeling 125\u003c\/p\u003e \u003cp\u003e3.3.3 Verifying FFP Modeling 127\u003c\/p\u003e \u003cp\u003e3.3.4 Verifying DPS Modeling 128\u003c\/p\u003e \u003cp\u003e3.3.5 Verifying DPS-Based FFS Modeling 129\u003c\/p\u003e \u003cp\u003e3.4 Evaluation of FFS Curves: Nonlinearity 130\u003c\/p\u003e \u003cp\u003e3.4.1 Sensing System 132\u003c\/p\u003e \u003cp\u003e3.4.2 FFS Nonlinearity 132\u003c\/p\u003e \u003cp\u003e3.5 Summary of Data Transforms 134\u003c\/p\u003e \u003cp\u003e3.6 Degradation Rate 140\u003c\/p\u003e \u003cp\u003e3.6.1 Constant Degradation Rate: Linear DPS-Based FFS 140\u003c\/p\u003e \u003cp\u003e3.6.2 Nonlinear Degradation Rate 141\u003c\/p\u003e \u003cp\u003e3.7 Failure Progression Signatures and System Nodes 142\u003c\/p\u003e \u003cp\u003e3.8 Failure Progression Signatures: Summary 144\u003c\/p\u003e \u003cp\u003eReferences 145\u003c\/p\u003e \u003cp\u003eFurther Reading 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Heuristic-Based Approach to Modeling CBD Signatures \u003c\/b\u003e\u003cb\u003e147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction to Heuristic-Based Modeling of Signatures 147\u003c\/p\u003e \u003cp\u003e4.1.1 Review of Chapter 3 147\u003c\/p\u003e \u003cp\u003e4.1.2 Theory: Heuristic Modeling of CBD Signatures 149\u003c\/p\u003e \u003cp\u003e4.1.3 Chapter Objectives 150\u003c\/p\u003e \u003cp\u003e4.1.4 Chapter Organization 151\u003c\/p\u003e \u003cp\u003e4.2 General Modeling Considerations: CBD Signatures 151\u003c\/p\u003e \u003cp\u003e4.2.1 Noise Margin 152\u003c\/p\u003e \u003cp\u003e4.2.2 Definition of a Degradation-Signature Model 152\u003c\/p\u003e \u003cp\u003e4.2.3 Feature Data: Nominal Value 152\u003c\/p\u003e \u003cp\u003e4.2.4 Feature Data, Fault-to-Failure Progression Signature, and Degradation-Signature Model 153\u003c\/p\u003e \u003cp\u003e4.2.5 Approach to Transforming CBD Signatures into FFS Data 153\u003c\/p\u003e \u003cp\u003e4.3 CBD Modeling: Degradation-Signature Models 154\u003c\/p\u003e \u003cp\u003e4.3.1 Representative Examples: Degradation-Signature Models 155\u003c\/p\u003e \u003cp\u003e4.3.2 Example Plots of Representative FFP Degradation Signatures 167\u003c\/p\u003e \u003cp\u003e4.3.3 Converting Decreasing Signatures to Increasing Signatures 167\u003c\/p\u003e \u003cp\u003e4.4 DPS Modeling: FFP to DPS Transform Models 168\u003c\/p\u003e \u003cp\u003e4.4.1 Developing Transform Models: FFP to DPS 168\u003c\/p\u003e \u003cp\u003e4.4.2 Example Plots of FFP Signatures and DPS Signatures 177\u003c\/p\u003e \u003cp\u003e4.5 FFS Modeling: Failure Level and Signature Modeling 177\u003c\/p\u003e \u003cp\u003e4.5.1 Developing DPS-Based Failure Level (FL) Models Using FFP Defined Failure Levels 177\u003c\/p\u003e \u003cp\u003e4.5.2 Modeling Results for Failure Levels: FFP-Based and DPS-Based 182\u003c\/p\u003e \u003cp\u003e4.5.3 Transforming DPS Data into FFS Data 183\u003c\/p\u003e \u003cp\u003e4.6 Heuristic-Based Approach to Modeling of Signatures: Summary 183\u003c\/p\u003e \u003cp\u003eReferences 186\u003c\/p\u003e \u003cp\u003eFurther Reading 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Non-Ideal Data: Effects and Conditioning \u003c\/b\u003e\u003cb\u003e189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction to Non-Ideal Data: Effects and Conditioning 189\u003c\/p\u003e \u003cp\u003e5.1.1 Review of Chapter 4 189\u003c\/p\u003e \u003cp\u003e5.1.2 Data Acquisition, Manipulation, and Transformation 189\u003c\/p\u003e \u003cp\u003e5.1.3 Chapter Objectives 191\u003c\/p\u003e \u003cp\u003e5.1.4 Chapter Organization 194\u003c\/p\u003e \u003cp\u003e5.2 Heuristic-Based Approach Applied to Non-Ideal CBD Signatures 194\u003c\/p\u003e \u003cp\u003e5.2.1 Summary of a Heuristic-Based Approach Applied to Non-Ideal CBD Signatures 195\u003c\/p\u003e \u003cp\u003e5.2.2 Example Target for Prognostic Enabling 196\u003c\/p\u003e \u003cp\u003e5.2.3 Noise is an Issue in Achieving High Accuracy in Prognostic Information 200\u003c\/p\u003e \u003cp\u003e5.3 Errors and Non-Ideality in FFS Data 202\u003c\/p\u003e \u003cp\u003e5.3.1 Noise Margin and Offset Errors 202\u003c\/p\u003e \u003cp\u003e5.3.2 Measurement Error, Uncertainty, and Sampling 203\u003c\/p\u003e \u003cp\u003e5.3.3 Other Sources of Noise 214\u003c\/p\u003e \u003cp\u003e5.3.4 Data Smoothing and Non-Ideality in FFS Data 218\u003c\/p\u003e \u003cp\u003e5.4 Heuristic Method for Adjusting FFS Data 223\u003c\/p\u003e \u003cp\u003e5.4.1 Description of a Method for Adjusting FFS Data 223\u003c\/p\u003e \u003cp\u003e5.4.2 Adjusted FFS Data 224\u003c\/p\u003e \u003cp\u003e5.4.3 Data Conditioning: Another Example Data Set 225\u003c\/p\u003e \u003cp\u003e5.5 Summary: Non-Ideal Data, Effects, and Conditioning 227\u003c\/p\u003e \u003cp\u003eReferences 229\u003c\/p\u003e \u003cp\u003eFurther Reading 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Design: Robust Prototype of an Exemplary PHM System \u003c\/b\u003e\u003cb\u003e233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 PHM System: Review 233\u003c\/p\u003e \u003cp\u003e6.1.1 Chapter 1: Introduction to Prognostics 233\u003c\/p\u003e \u003cp\u003e6.1.2 Chapter 2: Prognostic Approaches for Prognosis and Health Management 234\u003c\/p\u003e \u003cp\u003e6.1.3 Chapter 3: Failure Progression Signatures 237\u003c\/p\u003e \u003cp\u003e6.1.4 Chapter 4: Heuristic-Based Approach to Modeling CBD Signatures 239\u003c\/p\u003e \u003cp\u003e6.1.5 Chapter 5: Non-Ideal Data: Effects and Conditioning 239\u003c\/p\u003e \u003cp\u003e6.1.6 Chapter Objectives 243\u003c\/p\u003e \u003cp\u003e6.1.7 Chapter Organization 245\u003c\/p\u003e \u003cp\u003e6.2 Design Approaches for a PHM System 246\u003c\/p\u003e \u003cp\u003e6.2.1 Selecting and Evaluating Targets and Their Failure Modes 247\u003c\/p\u003e \u003cp\u003e6.2.2 Offline Prognostic Approaches: Selecting Results 248\u003c\/p\u003e \u003cp\u003e6.2.3 Selecting a Base Architecture for the Online Phase 248\u003c\/p\u003e \u003cp\u003e6.3 Sampling and Polling 249\u003c\/p\u003e \u003cp\u003e6.3.1 Continual – Periodic Sampling 249\u003c\/p\u003e \u003cp\u003e6.3.2 Periodic-Burst Sampling 250\u003c\/p\u003e \u003cp\u003e6.3.3 Polling 252\u003c\/p\u003e \u003cp\u003e6.4 Initial Design Specifications 253\u003c\/p\u003e \u003cp\u003e6.4.1 Operation: Test\/Demonstration vs. Real 253\u003c\/p\u003e \u003cp\u003e6.4.2 Test Bed 255\u003c\/p\u003e \u003cp\u003e6.4.3 Test Bed: Results 260\u003c\/p\u003e \u003cp\u003e6.5 Special RMS Method for AC Phase Currents 261\u003c\/p\u003e \u003cp\u003e6.5.1 Peak-RMS Method 263\u003c\/p\u003e \u003cp\u003e6.5.2 Special Peak-RMS Method: Base Computational Routine 263\u003c\/p\u003e \u003cp\u003e6.5.3 Special Peak-RMS Method: FFP Computational Routine 264\u003c\/p\u003e \u003cp\u003e6.5.4 Peak-RMS Method: EMA 265\u003c\/p\u003e \u003cp\u003e6.6 Diagnostic and Prognostic Procedure 274\u003c\/p\u003e \u003cp\u003e6.6.1 SMPS Power Supply 274\u003c\/p\u003e \u003cp\u003e6.6.2 EMA 275\u003c\/p\u003e \u003cp\u003e6.7 Specifications: Robustness and Capability 275\u003c\/p\u003e \u003cp\u003e6.7.1 Node-Based Architecture 276\u003c\/p\u003e \u003cp\u003e6.7.2 Example Design 277\u003c\/p\u003e \u003cp\u003e6.8 Node Specifications 279\u003c\/p\u003e \u003cp\u003e6.8.1 System Node Definition 279\u003c\/p\u003e \u003cp\u003e6.8.2 Node Definition 279\u003c\/p\u003e \u003cp\u003e6.8.3 Other Node Definitions for the Prototype PHM System 287\u003c\/p\u003e \u003cp\u003e6.9 System Verification and Performance Metrics 288\u003c\/p\u003e \u003cp\u003e6.9.1 Offset Types of Errors 288\u003c\/p\u003e \u003cp\u003e6.9.2 Uncertainty in Determining Prognostic Distance 292\u003c\/p\u003e \u003cp\u003e6.9.3 Estimating Convergence to Within PHα 296\u003c\/p\u003e \u003cp\u003e6.9.4 Performance Metrics 297\u003c\/p\u003e \u003cp\u003e6.9.5 Prognostic Information: RUL, SoH, PH, and Degradation 299\u003c\/p\u003e \u003cp\u003e6.10 System Verification: Advanced Prognostics 300\u003c\/p\u003e \u003cp\u003e6.10.1 SMPS: FFP Signature Directly to FFS 300\u003c\/p\u003e \u003cp\u003e6.10.2 SMPS: FFP Signature to DPS to FFS 301\u003c\/p\u003e \u003cp\u003e6.11 PHM System Verification: EMA Faults 303\u003c\/p\u003e \u003cp\u003e6.11.1 EMA: Load (Friction) Type of Fault 304\u003c\/p\u003e \u003cp\u003e6.11.2 EMA: Winding Type of Fault 307\u003c\/p\u003e \u003cp\u003e6.11.3 EMA: Power-Switching Transistor Type of Fault 307\u003c\/p\u003e \u003cp\u003e6.12 PHM System Verification: Functional Integration 307\u003c\/p\u003e \u003cp\u003e6.12.1 Functional Integration: Control and Data Flow 307\u003c\/p\u003e \u003cp\u003e6.12.2 System Performance Metrics: Summary 309\u003c\/p\u003e \u003cp\u003e6.12.3 PHM System: Plans 311\u003c\/p\u003e \u003cp\u003e6.13 Summary: A Robust Prototype PHM System 315\u003c\/p\u003e \u003cp\u003eReferences 316\u003c\/p\u003e \u003cp\u003eFurther Reading 317\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Prognostic Enabling: Selection, Evaluation, and Other Considerations \u003c\/b\u003e\u003cb\u003e319\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction to Prognostic Enabling 319\u003c\/p\u003e \u003cp\u003e7.1.1 Review of Chapter 6 319\u003c\/p\u003e \u003cp\u003e7.1.2 Electronic Health Solutions 320\u003c\/p\u003e \u003cp\u003e7.1.3 Critical Systems and Advance Warning 322\u003c\/p\u003e \u003cp\u003e7.1.4 Reduction in Maintenance 322\u003c\/p\u003e \u003cp\u003e7.1.5 Health Management, Maintenance, and Logistics 323\u003c\/p\u003e \u003cp\u003e7.1.6 Chapter Objectives 325\u003c\/p\u003e \u003cp\u003e7.1.7 Chapter Organization 325\u003c\/p\u003e \u003cp\u003e7.2 Prognostic Targets: Evaluation, Selection, and Specifications 325\u003c\/p\u003e \u003cp\u003e7.2.1 Criteria for Evaluation, Selection, and Winnowing 326\u003c\/p\u003e \u003cp\u003e7.2.2 Meaning of MTBF and MTTF 326\u003c\/p\u003e \u003cp\u003e7.2.3 MTTF and MTBF Uncertainty 328\u003c\/p\u003e \u003cp\u003e7.2.4 TTF and PITTFF 329\u003c\/p\u003e \u003cp\u003e7.3 Example: Cost-Benefit of Prognostic Approaches 332\u003c\/p\u003e \u003cp\u003e7.3.1 Cost-Benefit Situations 333\u003c\/p\u003e \u003cp\u003e7.3.2 Cost Analyses 336\u003c\/p\u003e \u003cp\u003e7.4 Reliability: Bathtub Curve 342\u003c\/p\u003e \u003cp\u003e7.4.1 Bathtub Curve: MTBF and MTTF 343\u003c\/p\u003e \u003cp\u003e7.4.2 Trigger Point and Prognostic Distance 343\u003c\/p\u003e \u003cp\u003e7.5 Chapter Summary and Book Conclusion 344\u003c\/p\u003e \u003cp\u003eReferences 345\u003c\/p\u003e \u003cp\u003eFurther Reading 346\u003c\/p\u003e \u003cp\u003eIndex 347\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407037014359,"sku":"9781119356653","price":94.0,"currency_code":"GBP","in_stock":false}],"url":"https:\/\/bookcurl.com\/products\/prognostics-and-health-management-9781119356653","provider":"Book Curl","version":"1.0","type":"link"}