Details

Prognostics and Health Management


Prognostics and Health Management

A Practical Approach to Improving System Reliability Using Condition-Based Data
Quality and Reliability Engineering Series 1. Aufl.

von: Douglas Goodman, James P. Hofmeister, Ferenc Szidarovszky

100,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 01.04.2019
ISBN/EAN: 9781119356691
Sprache: englisch
Anzahl Seiten: 384

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Beschreibungen

<p><b>A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life.</b><i> </i></p> <p><i>Prognostics and Health Management</i> 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. </p> <p>Written by noted experts in the field, <i>Prognostics and Health Management</i> clearly describes how to extract signatures from conditioned-based data using conditioning methods such as data fusion and transformation, domain transformation, data type transformation and indirect and differential comparison. This important resource:</p> <ul> <li>Integrates data collecting, mathematical modelling and reliability prediction in one volume</li> <li>Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes</li> <li>Presents information from a panel of experts on the topic</li> <li>Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods</li> </ul> <p>Written for system engineers working in critical process industries and automotive and aerospace designers, <i>Prognostics and Health Management </i>offers a guide to the application of condition-based data to produce signatures for input to predictive algorithms to produce prognostic estimates of functional health and life.</p>
<p>List of Figures xi</p> <p>Series Editor’s Foreword xxi</p> <p>Preface xxiii</p> <p>Acknowledgments xxvii</p> <p><b>1 Introduction to Prognostics </b><b><i>1</i></b></p> <p>1.1 What Is Prognostics? 1</p> <p>1.1.1 Chapter Objectives 1</p> <p>1.1.2 Chapter Organization 3</p> <p>1.2 Foundation of Reliability Theory 3</p> <p>1.2.1 Time-to-Failure Distributions 4</p> <p>1.2.2 Probability and Reliability 6</p> <p>1.2.3 Probability Density Function 7</p> <p>1.2.4 Relationships of Distributions 10</p> <p>1.2.5 Failure Rate 10</p> <p>1.2.6 Expected Value and Variance 16</p> <p>1.3 Failure Distributions Under Extreme Stress Levels 18</p> <p>1.3.1 Basic Models 18</p> <p>1.3.2 Cumulative Damage Models 21</p> <p>1.3.3 General Exponential Models 21</p> <p>1.4 Uncertainty Measures in Parameter Estimation 23</p> <p>1.5 Expected Number of Failures 26</p> <p>1.5.1 Minimal Repair 26</p> <p>1.5.2 Failure Replacement 28</p> <p>1.5.3 Decreased Number of Failures Due to Partial Repairs 30</p> <p>1.5.4 Decreased Age Due to Partial Repairs 30</p> <p>1.6 System Reliability and Prognosis and Health Management 31</p> <p>1.6.1 General Framework for a CBM-Based PHM System 32</p> <p>1.6.2 Relationship of PHM to System Reliability 34</p> <p>1.6.3 Degradation Progression Signature (DPS) and Prognostics 35</p> <p>1.6.4 Ideal Functional Failure Signature (FFS) and Prognostics 37</p> <p>1.6.5 Non-ideal FFS and Prognostics 41</p> <p>1.7 Prognostic Information 41</p> <p>1.7.1 Non-ideality: Initial-Estimate Error and Remaining Useful Life (RUL) 42</p> <p>1.7.2 Convergence of RUL Estimates Given an Initial Estimate Error 44</p> <p>1.7.3 Prognostic Distance (PD) and Convergence 45</p> <p>1.7.4 Convergence: Figure of Merit (𝜒<sub>𝛼</sub>) 45</p> <p>1.7.5 Other Sources of Non-ideality in FFS Data 46</p> <p>1.8 Decisions on Cost and Benefits 47</p> <p>1.8.1 Product Selection 47</p> <p>1.8.2 Optimal Maintenance Scheduling 49</p> <p>1.8.3 Condition-Based Maintenance or Replacement 54</p> <p>1.8.4 Preventive Replacement Scheduling 55</p> <p>1.8.5 Model Variants and Extensions 58</p> <p>1.9 Introduction to PHM: Summary 60</p> <p>References 60</p> <p>Further Reading 62</p> <p><b>2 Approaches for Prognosis and Health Management/Monitoring (PHM) </b><b>63</b></p> <p>2.1 Introduction to Approaches for Prognosis and Health Management/Monitoring (PHM) 63</p> <p>2.1.1 Model-Based Prognostic Approaches 63</p> <p>2.1.2 Data-Driven Prognostic Approaches 63</p> <p>2.1.3 Hybrid Prognostic Approaches 64</p> <p>2.1.4 Chapter Objectives 64</p> <p>2.1.5 Chapter Organization 64</p> <p>2.2 Model-Based Prognostics 65</p> <p>2.2.1 Analytical Modeling 66</p> <p>2.2.2 Distribution Modeling 71</p> <p>2.2.3 Physics of Failure (PoF) and Reliability Modeling 72</p> <p>2.2.4 Acceleration Factor (AF) 74</p> <p>2.2.5 Complexity Related to Reliability Modeling 76</p> <p>2.2.6 Failure Distribution 78</p> <p>2.2.7 Multiple Modes of Failure: Failure Rate and FIT 79</p> <p>2.2.8 Advantages and Disadvantages of Model-Based Prognostics 79</p> <p>2.3 Data-Driven Prognostics 80</p> <p>2.3.1 Statistical Methods 80</p> <p>2.3.2 Machine Learning (ML): Classification and Clustering 85</p> <p>2.4 Hybrid-Driven Prognostics 90</p> <p>2.5 An Approach to Condition-Based Maintenance (CBM) 92</p> <p>2.5.1 Modeling of Condition-Based Data (CBD) Signatures 92</p> <p>2.5.2 Comparison of Methodologies: Life Consumption and CBD Signature 92</p> <p>2.5.3 CBD-Signature Modeling: An Illustration 93</p> <p>2.6 Approaches to PHM: Summary 103</p> <p>References 103</p> <p>Further Reading 106</p> <p><b>3 Failure Progression Signatures </b><b>107</b></p> <p>3.1 Introduction to Failure Signatures 107</p> <p>3.1.1 Chapter Objectives 107</p> <p>3.1.2 Chapter Organization 108</p> <p>3.2 Basic Types of Signatures 108</p> <p>3.2.1 CBD Signature 109</p> <p>3.2.2 FFP Signature 114</p> <p>3.2.3 Transforming FFP into FFS 118</p> <p>3.2.4 Transforming FFP into a Degradation Progression Signature (DPS) 120</p> <p>3.2.5 Transforming DPS into DPS-Based FFS 122</p> <p>3.3 Model Verification 124</p> <p>3.3.1 Signature Classification 124</p> <p>3.3.2 Verifying CBD Modeling 125</p> <p>3.3.3 Verifying FFP Modeling 127</p> <p>3.3.4 Verifying DPS Modeling 128</p> <p>3.3.5 Verifying DPS-Based FFS Modeling 129</p> <p>3.4 Evaluation of FFS Curves: Nonlinearity 130</p> <p>3.4.1 Sensing System 132</p> <p>3.4.2 FFS Nonlinearity 132</p> <p>3.5 Summary of Data Transforms 134</p> <p>3.6 Degradation Rate 140</p> <p>3.6.1 Constant Degradation Rate: Linear DPS-Based FFS 140</p> <p>3.6.2 Nonlinear Degradation Rate 141</p> <p>3.7 Failure Progression Signatures and System Nodes 142</p> <p>3.8 Failure Progression Signatures: Summary 144</p> <p>References 145</p> <p>Further Reading 146</p> <p><b>4 Heuristic-Based Approach to Modeling CBD Signatures </b><b>147</b></p> <p>4.1 Introduction to Heuristic-Based Modeling of Signatures 147</p> <p>4.1.1 Review of Chapter 3 147</p> <p>4.1.2 Theory: Heuristic Modeling of CBD Signatures 149</p> <p>4.1.3 Chapter Objectives 150</p> <p>4.1.4 Chapter Organization 151</p> <p>4.2 General Modeling Considerations: CBD Signatures 151</p> <p>4.2.1 Noise Margin 152</p> <p>4.2.2 Definition of a Degradation-Signature Model 152</p> <p>4.2.3 Feature Data: Nominal Value 152</p> <p>4.2.4 Feature Data, Fault-to-Failure Progression Signature, and Degradation-Signature Model 153</p> <p>4.2.5 Approach to Transforming CBD Signatures into FFS Data 153</p> <p>4.3 CBD Modeling: Degradation-Signature Models 154</p> <p>4.3.1 Representative Examples: Degradation-Signature Models 155</p> <p>4.3.2 Example Plots of Representative FFP Degradation Signatures 167</p> <p>4.3.3 Converting Decreasing Signatures to Increasing Signatures 167</p> <p>4.4 DPS Modeling: FFP to DPS Transform Models 168</p> <p>4.4.1 Developing Transform Models: FFP to DPS 168</p> <p>4.4.2 Example Plots of FFP Signatures and DPS Signatures 177</p> <p>4.5 FFS Modeling: Failure Level and Signature Modeling 177</p> <p>4.5.1 Developing DPS-Based Failure Level (FL) Models Using FFP Defined Failure Levels 177</p> <p>4.5.2 Modeling Results for Failure Levels: FFP-Based and DPS-Based 182</p> <p>4.5.3 Transforming DPS Data into FFS Data 183</p> <p>4.6 Heuristic-Based Approach to Modeling of Signatures: Summary 183</p> <p>References 186</p> <p>Further Reading 187</p> <p><b>5 Non-Ideal Data: Effects and Conditioning </b><b>189</b></p> <p>5.1 Introduction to Non-Ideal Data: Effects and Conditioning 189</p> <p>5.1.1 Review of Chapter 4 189</p> <p>5.1.2 Data Acquisition, Manipulation, and Transformation 189</p> <p>5.1.3 Chapter Objectives 191</p> <p>5.1.4 Chapter Organization 194</p> <p>5.2 Heuristic-Based Approach Applied to Non-Ideal CBD Signatures 194</p> <p>5.2.1 Summary of a Heuristic-Based Approach Applied to Non-Ideal CBD Signatures 195</p> <p>5.2.2 Example Target for Prognostic Enabling 196</p> <p>5.2.3 Noise is an Issue in Achieving High Accuracy in Prognostic Information 200</p> <p>5.3 Errors and Non-Ideality in FFS Data 202</p> <p>5.3.1 Noise Margin and Offset Errors 202</p> <p>5.3.2 Measurement Error, Uncertainty, and Sampling 203</p> <p>5.3.3 Other Sources of Noise 214</p> <p>5.3.4 Data Smoothing and Non-Ideality in FFS Data 218</p> <p>5.4 Heuristic Method for Adjusting FFS Data 223</p> <p>5.4.1 Description of a Method for Adjusting FFS Data 223</p> <p>5.4.2 Adjusted FFS Data 224</p> <p>5.4.3 Data Conditioning: Another Example Data Set 225</p> <p>5.5 Summary: Non-Ideal Data, Effects, and Conditioning 227</p> <p>References 229</p> <p>Further Reading 230</p> <p><b>6 Design: Robust Prototype of an Exemplary PHM System </b><b>233</b></p> <p>6.1 PHM System: Review 233</p> <p>6.1.1 Chapter 1: Introduction to Prognostics 233</p> <p>6.1.2 Chapter 2: Prognostic Approaches for Prognosis and Health Management 234</p> <p>6.1.3 Chapter 3: Failure Progression Signatures 237</p> <p>6.1.4 Chapter 4: Heuristic-Based Approach to Modeling CBD Signatures 239</p> <p>6.1.5 Chapter 5: Non-Ideal Data: Effects and Conditioning 239</p> <p>6.1.6 Chapter Objectives 243</p> <p>6.1.7 Chapter Organization 245</p> <p>6.2 Design Approaches for a PHM System 246</p> <p>6.2.1 Selecting and Evaluating Targets and Their Failure Modes 247</p> <p>6.2.2 Offline Prognostic Approaches: Selecting Results 248</p> <p>6.2.3 Selecting a Base Architecture for the Online Phase 248</p> <p>6.3 Sampling and Polling 249</p> <p>6.3.1 Continual – Periodic Sampling 249</p> <p>6.3.2 Periodic-Burst Sampling 250</p> <p>6.3.3 Polling 252</p> <p>6.4 Initial Design Specifications 253</p> <p>6.4.1 Operation: Test/Demonstration vs. Real 253</p> <p>6.4.2 Test Bed 255</p> <p>6.4.3 Test Bed: Results 260</p> <p>6.5 Special RMS Method for AC Phase Currents 261</p> <p>6.5.1 Peak-RMS Method 263</p> <p>6.5.2 Special Peak-RMS Method: Base Computational Routine 263</p> <p>6.5.3 Special Peak-RMS Method: FFP Computational Routine 264</p> <p>6.5.4 Peak-RMS Method: EMA 265</p> <p>6.6 Diagnostic and Prognostic Procedure 274</p> <p>6.6.1 SMPS Power Supply 274</p> <p>6.6.2 EMA 275</p> <p>6.7 Specifications: Robustness and Capability 275</p> <p>6.7.1 Node-Based Architecture 276</p> <p>6.7.2 Example Design 277</p> <p>6.8 Node Specifications 279</p> <p>6.8.1 System Node Definition 279</p> <p>6.8.2 Node Definition 279</p> <p>6.8.3 Other Node Definitions for the Prototype PHM System 287</p> <p>6.9 System Verification and Performance Metrics 288</p> <p>6.9.1 Offset Types of Errors 288</p> <p>6.9.2 Uncertainty in Determining Prognostic Distance 292</p> <p>6.9.3 Estimating Convergence to Within PHα 296</p> <p>6.9.4 Performance Metrics 297</p> <p>6.9.5 Prognostic Information: RUL, SoH, PH, and Degradation 299</p> <p>6.10 System Verification: Advanced Prognostics 300</p> <p>6.10.1 SMPS: FFP Signature Directly to FFS 300</p> <p>6.10.2 SMPS: FFP Signature to DPS to FFS 301</p> <p>6.11 PHM System Verification: EMA Faults 303</p> <p>6.11.1 EMA: Load (Friction) Type of Fault 304</p> <p>6.11.2 EMA: Winding Type of Fault 307</p> <p>6.11.3 EMA: Power-Switching Transistor Type of Fault 307</p> <p>6.12 PHM System Verification: Functional Integration 307</p> <p>6.12.1 Functional Integration: Control and Data Flow 307</p> <p>6.12.2 System Performance Metrics: Summary 309</p> <p>6.12.3 PHM System: Plans 311</p> <p>6.13 Summary: A Robust Prototype PHM System 315</p> <p>References 316</p> <p>Further Reading 317</p> <p><b>7 Prognostic Enabling: Selection, Evaluation, and Other Considerations </b><b>319</b></p> <p>7.1 Introduction to Prognostic Enabling 319</p> <p>7.1.1 Review of Chapter 6 319</p> <p>7.1.2 Electronic Health Solutions 320</p> <p>7.1.3 Critical Systems and Advance Warning 322</p> <p>7.1.4 Reduction in Maintenance 322</p> <p>7.1.5 Health Management, Maintenance, and Logistics 323</p> <p>7.1.6 Chapter Objectives 325</p> <p>7.1.7 Chapter Organization 325</p> <p>7.2 Prognostic Targets: Evaluation, Selection, and Specifications 325</p> <p>7.2.1 Criteria for Evaluation, Selection, and Winnowing 326</p> <p>7.2.2 Meaning of MTBF and MTTF 326</p> <p>7.2.3 MTTF and MTBF Uncertainty 328</p> <p>7.2.4 TTF and PITTFF 329</p> <p>7.3 Example: Cost-Benefit of Prognostic Approaches 332</p> <p>7.3.1 Cost-Benefit Situations 333</p> <p>7.3.2 Cost Analyses 336</p> <p>7.4 Reliability: Bathtub Curve 342</p> <p>7.4.1 Bathtub Curve: MTBF and MTTF 343</p> <p>7.4.2 Trigger Point and Prognostic Distance 343</p> <p>7.5 Chapter Summary and Book Conclusion 344</p> <p>References 345</p> <p>Further Reading 346</p> <p>Index 347</p>
<p><b>Douglas Goodman</b> is Founder and Chief Engineer of Ridgetop Group, Inc., Arizona, USA. <p><b>James P. Hofmeister</b> is Distinguished Engineer, Advanced Research Group, Ridgetop Group, Inc., Arizona, USA. <p><b> Ferenc Szidarovszky, Ph.D,</b> is Senior Researcher, Advanced Research Group, Ridgetop Group, Inc., Arizona, USA.
<p><b>PROGNOSTICS AND HEALTH MANAGEMENT</b> <p><b>A Practical Approach to Improving System Reliability Using Condition-Based Data</b> <p>A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life <p><i>Prognostics and Health Management</i> provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improving system reliability using condition-based data (CBD) to the monitoring and management of health of systems. This proven approach uses electronic signatures extracted from condition-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. <p>This important resource: <ul> <li>Integrates data collecting, mathematical modelling and reliability prediction in one volume</li> <li>Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes</li> <li>Presents information from a panel of experts on the topic</li> <li>Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods</li> </ul> <p>Written for system engineers working in critical process industries and automotive and aerospace designers, <i>Prognostics and Health Management</i> offers a guide to the application of condition-based data to produce signatures for input to predictive algorithms to produce prognostic estimates of functional health and life.

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