Cover
List of Figures
Series Editor's Foreword
Preface
Acknowledgments
1 Introduction to Prognostics
1. 1.1 What Is Prognostics?
2. 1.2 Foundation of Reliability Theory
3. 1.3 Failure Distributions Under Extreme Stress Levels
4. 1.4 Uncertainty Measures in Parameter Estimation
5. 1.5 Expected Number of Failures
6. 1.6 System Reliability and Prognosis and Health Management
7. 1.7 Prognostic Information
8. 1.8 Decisions on Cost and Benefits
9. 1.9 Introduction to PHM: Summary
10. References
11. Further Reading
2 Approaches for Prognosis and Health Management/Monitoring (PHM)
1. 2.1 Introduction to Approaches for Prognosis and Health Management/Monitoring (PHM)
2. 2.2 Model‐Based Prognostics
3. 2.3 Data‐Driven Prognostics
4. 2.4 Hybrid‐Driven Prognostics
5. 2.5 An Approach to Condition‐Based Maintenance (CBM)
6. 2.6 Approaches to PHM: Summary
7. References
8. Further Reading
3 Failure Progression Signatures
1. 3.1 Introduction to Failure Signatures
2. 3.2 Basic Types of Signatures
3. 3.3 Model Verification
4. 3.4 Evaluation of FFS Curves: Nonlinearity
5. 3.5 Summary of Data Transforms
6. 3.6 Degradation Rate
7. 3.7 Failure Progression Signatures and System Nodes
8. 3.8 Failure Progression Signatures: Summary
9. References
10. Further Reading
4 Heuristic‐Based Approach to Modeling CBD Signatures
1. 4.1 Introduction to Heuristic‐Based Modeling of Signatures
2. 4.2 General Modeling Considerations: CBD Signatures
3. 4.3 CBD Modeling: Degradation‐Signature Models
4. 4.4 DPS Modeling: FFP to DPS Transform Models
5. 4.5 FFS Modeling: Failure Level and Signature Modeling
6. 4.6 Heuristic‐Based Approach to Modeling of Signatures: Summary
7. References
8. Further Reading
5 Non‐Ideal Data: Effects and Conditioning
1. 5.1 Introduction to Non‐Ideal Data: Effects and Conditioning
2. 5.2 Heuristic‐Based Approach Applied to Non‐Ideal CBD Signatures
3. 5.3 Errors and Non‐Ideality in FFS Data
4. 5.4 Heuristic Method for Adjusting FFS Data
5. 5.5 Summary: Non‐Ideal Data, Effects, and Conditioning
6. References
7. Further Reading
6 Design: Robust Prototype of an Exemplary PHM System
1. 6.1 PHM System: Review
2. 6.2 Design Approaches for a PHM System
3. 6.3 Sampling and Polling
4. 6.4 Initial Design Specifications
5. 6.5 Special RMS Method for AC Phase Currents
6. 6.6 Diagnostic and Prognostic Procedure
7. 6.7 Specifications: Robustness and Capability
8. 6.8 Node Specifications
9. 6.9 System Verification and Performance Metrics
10. 6.10 System Verification: Advanced Prognostics
11. 6.11 PHM System Verification: EMA Faults
12. 6.12 PHM System Verification: Functional Integration
13. 6.13 Summary: A Robust Prototype PHM System
14. References
15. Further Reading
7 Prognostic Enabling: Selection, Evaluation, and Other Considerations
1. 7.1 Introduction to Prognostic Enabling
2. 7.2 Prognostic Targets: Evaluation, Selection, and Specifications
3. 7.3 Example: Cost‐Benefit of Prognostic Approaches
4. 7.4 Reliability: Bathtub Curve
5. 7.5 Chapter Summary and Book Conclusion
6. References
7. Further Reading
Index
End User License Agreement

List of Tables

Chapter 1
1. Table 1.1 Expectations and variances.
2. Table 1.2 Decision table for purchasing a machine.
3. Table 1.3 Utility table for purchasing a machine.
Chapter 2
1. Table 2.1 Load types and examples.
2. Table 2.2 Failure distributions and example applications.
3. Table 2.3 Examples of reliability procedures and applications (White and Bernste...
4. Table 2.4 Examples of temperature acceleration models (White and Bernstein 2008 ...
5. Table 2.5 Parametric and nonparametric methods.
6. Table 2.6 Kernel functions.
7. Table 2.7 Supervised and unsupervised classification and clustering.
8. Table 2.8 Some advantages and disadvantages of model‐based and data‐driven progn...
9. Table 2.9 Differences in focus of model‐based and heuristic‐based approaches to ...
Chapter 3
1. Table 3.1 Range of failure thresholds: Lot 1 and Lot 2.
Chapter 4
1. Table 4.1 List of decreasing signatures and models and corresponding increasing ...
2. Table 4.2 Models: decreasing signature, increasing signature, FFP‐to‐DPS, and FL...
Chapter 5
1. Table 5.1 FFS nonlinearity procedure.
2. Table 5.2 List of calculations, specifications, and results for Example 5.2.
3. Table 5.3 List of calculations, specifications, and results for Example 5.3.
4. Table 5.4 Example of lookup values for resistance‐temperature, platinum RTD (ITS...
Chapter 6
1. Table 6.1 Differences in focus of model‐based and heuristic‐based approaches to ...
2. Table 6.2 Table of terms and definitions for performance metrics.
3. Table 6.3 SMPS example: table of number of data points and times to converge to ...
4. Table 6.4 Performance measurements and metrics.
Chapter 7
1. Table 7.1 Performance measurements and metrics.
2. Table 7.2 Summarized list of TBF and TTF calculations for various periods of ope...
3. Table 7.3 Tabulated calculations for PD(EST).
4. Table 7.4 Tabulated calculations using enhanced program with PITTFF0 = 4800.
5. Table 7.5 Cost estimates for benefits evaluation of prognostic enabling.
6. Table 7.6 Summarized list of test results.

List of Illustrations

Chapter 1
1. Figure 1.1 Core prognostic frameworks in a PHM system.
2. Figure 1.2 Framework diagram for a PHM system.
3. Figure 1.3 Graph of the exponential CDF with λ = 3.
4. Figure 1.4 Graph of the Weibull CDF with β = 1.2 and η = 5...
5. Figure 1.5 Graph of the exponential PDF with λ = 3 .
6. Figure 1.6 Graphs of gamma PDFs.
7. Figure 1.7 Graphs of Weibull PDFs.
8. Figure 1.8 Failure rates of Weibull variables.
9. Figure 1.9 Failure rates of gamma variables.
10. Figure 1.10 Failure rate of the standard normal variable.
11. Figure 1.11 Failure rate of the lognormal variable with μ = 0...
12. Figure 1.12 Logistic failure rate with μ = 0 and σ = 1...
13. Figure 1.13Figure 1.13 Gumbel failure rate.
14. Figure 1.14 Log‐logistic failure rate with μ = 0 .
15. Figure 1.15 Integration domain.
16. Figure 1.16 High‐level block diagram of a PHM system.
17. Figure 1.17 A framework for CBM for PHM.
18. Figure 1.18 Taxonomy of prognostic approaches.
19. Figure 1.19 Example of an FFP signature – a curvilinear (convex), noisy charact...
20. Figure 1.20 Ideal DPS transfer curve superimposed on an FFP signature.
21. Figure 1.21 Ideal DPS, degradation threshold, and functional failure.
22. Figure 1.22 Normalized and transformed FFP and DPS transformed into FFS.
23. Figure 1.23 Ideal FFS – transfer curve for CBD.
24. Figure 1.24 Variability in DPS transfer curves.
25. Figure 1.25 FFS transforms of the DPS plots shown in Figure 1.23 .
26. Figure 1.26 FFS and prognostic information.
27. Figure 1.27 FFS transfer curve exhibiting distortion, noise, and change in degr...
28. Figure 1.28 Example plots of an ideal RUL and ideal PH.
29. Figure 1.29 Example plots of an ideal SoH transfer curve and PH accuracy.
30. Figure 1.30 Example of RUL with an initial‐estimate error of 100 days.
31. Figure 1.31 Random‐walk with Kalman‐like filtering solution for a high‐value in...
32. Figure 1.32 Random‐walk with Kalman‐like filtering solution for a low‐value ini...
33. Figure 1.33 Example of FFS data exhibiting an offset error, distortion, and noi...
34. Figure 1.34 Utility function of price.
35. Figure 1.35 Utility function of expected lifetime.
36. Figure 1.36 Shape of the objective function.
Chapter 2
1. Figure 2.1 Block diagram showing three approaches to PHM.
2. Figure 2.2 Precision and complexity: relative comparison of classical PHM appro...
3. Figure 2.3 Model‐based approach to development and use.
4. Figure 2.4 Model‐use diagram.
5. Figure 2.5 A framework for CBM for PHM (CAVE3 2015 ).
6. Figure 2.6 Transition diagram.
7. Figure 2.7 Example of a fault tree showing an RTD fault and an RTD‐usage model.
8. Figure 2.8 Example plots of Weibull distributions.
9. Figure 2.9 A special system structure.
10. Figure 2.10 HALT result – 30 of 32 FPGA devices failed (Hofmeister et al. 2006...
11. Figure 2.11 Family of failure curves, failure distribution, and TTF.
12. Figure 2.12 Diagram of data‐driven approaches.
13. Figure 2.13 A special neural network.
14. Figure 2.14 Comparison of model‐based (PoF) and data‐driven prognostic approach...
15. Figure 2.15 Relative comparison of PHM approaches – PoF, data‐driven, and hybri...
16. Figure 2.16 Example diagram of a heuristic‐based CBM system using CBD‐based mod...
17. Figure 2.17 Diagram comparison of model‐based and CBD‐signature approaches to P...
18. Figure 2.18 Simplified diagram of a switch‐mode power supply (SMPS) with an out...
19. Figure 2.19 Unreliability plots for three models for capacitor failures (Alan e...
20. Figure 2.20 Example of the output of a SMPS.
21. Figure 2.21 Example of a ringing response from an electrical circuit to an abru...
22. Figure 2.22 Relationship of prognostic specifications (PD and PDα) to RUL and P...
23. Figure 2.23 Simulated change in resonant frequency as filter capacitance degrad...
24. Figure 2.24 Experimental change in resonant frequency as filter capacitance deg...
Chapter 3
1. Figure 3.1 Diagram of classical and CBD prognostic approaches for PHM systems.
2. Figure 3.2 Functional block diagram for CBD signature data and processing flow.
3. Figure 3.3 Example of CBD containing feature data and noise (FD + Noise).
4. Figure 3.4 Example of signals at an output node of an SMPS.
5. Figure 3.5 Example of a damped‐ringing response (Judkins and Hofmeister 2007 ...
6. Figure 3.6 Modeling a damped‐ringing response.
7. Figure 3.7 Example of a CBD signature: FD is the resonant frequency of a damped...
8. Figure 3.8 CBD signature and levels for SMPS lot 1 (top) and SMPS lot 2 (bottom...
9. Figure 3.9 Functional block diagram for FFP signature and processing flow.
10. Figure 3.10 FFP signatures using a fixed value and a calibrated value for nomin...
11. Figure 3.11 FFP signature, calibrated value for nominal frequency, failure thre...
12. Figure 3.12 FFS signatures for FL = 0.6 (top) and FL = 0.7 (bottom).
13. Figure 3.13 DPS and FFP signatures for data shown in Figure 3.11 .
14. Figure 3.14 FFP and DPS Showing FL = 0.4 and FL = 0.5.
15. Figure 3.15 DPS‐based FFS (FL = 0.65) and FFP‐based FFS (FL = 0.7).
16. Figure 3.16 Examples of signatures: decreasing (top) and increasing (bottom) sl...
17. Figure 3.17 Other examples of signatures: decreasing (top) and increasing (bott...
18. Figure 3.18 Simulated CBD‐based signature: FD = CBD − NM.
19. Figure 3.19 Differences: experimental and simulated signatures.
20. Figure 3.20 Simulated (top) and comparison of experiment (bottom) FFP signature...
21. Figure 3.21 Simulated DPS (top) and experimental DPS (bottom).
22. Figure 3.22 Simulated FFS from simulated (top) and from experimental DPS (botto...
23. Figure 3.23 Illustration of point‐by‐point FNL comparison.
24. Figure 3.24 Illustration of total FNL_E comparison.
25. Figure 3.25 Example plot of FFP‐based and DPS‐based FNL _i.
26. Figure 3.26 SMPS output and extracted damped‐ringing response.
27. Figure 3.27 CBD signature and FFP signature.
28. Figure 3.28 FFP‐based FFS and DPS.
29. Figure 3.29 DPS‐based FFS.
30. Figure 3.30 Procedural diagram for producing a DPS‐based FFS.
31. Figure 3.31 Linear DPS (left side) and nonlinear DPS (right side) data plots.
32. Figure 3.32 Simulated CBD plots using nonlinear and linear degradation rates.
33. Figure 3.33 Comparison of ideal DPS to DPS from data for a nonconstant degradat...
34. Figure 3.34 Node‐based framework for supporting failure progression signatures.
Chapter 4
1. Figure 4.1 Block diagram for offline modeling of CBD signatures.
2. Figure 4.2 Flow diagram for developing signature models.
3. Figure 4.3 Power function #1: increasing curves, decreasing slope angles.
4. Figure 4.4 Power function #1: increasing curves, increasing slope angles.
5. Figure 4.5 Power function #2: decreasing curves, decreasing slope angles.
6. Figure 4.6 Power function #2: decreasing curves, increasing slope angles.
7. Figure 4.7 Power function #3: increasing curves, vertically asymptotic, dP _i < P
8. Figure 4.8 Power function #4: decreasing curves, vertically asymptotic, dP _i < P
9. Figure 4.9 Power function #5: increasing curves, horizontally asymptotic.
10. Figure 4.10 Power function #6: decreasing curves, horizontally asymptotic.
11. Figure 4.11 Power function #7: increasing curves, slightly curvilinear, decreas...
12. Figure 4.12 Power function #7: increasing curves, slightly curvilinear, increas...
13. Figure 4.13 Power function #8: decreasing curves, slightly curvilinear, decreas...
14. Figure 4.14 Power function #8: decreasing curves, slightly curvilinear, increas...
15. Figure 4.15 Power function #9: increasing curves, vertically asymptotic, dP _i <
16. Figure 4.16 Power function #9: increasing curves, horizontally asymptotic, dP _i ...
17. Figure 4.17 Power function #10: decreasing curves, vertically asymptotic, dP _i <...
18. Figure 4.18 Power function #10: decreasing curves, horizontally asymptotic, dP _i
19. Figure 4.19 Exponential function #11: increasing curves, vertically asymptotic.
20. Figure 4.20 Exponential function #12: decreasing curves, vertically asymptotic.
21. Figure 4.21 Exponential function #13: increasing curves, horizontally asymptoti...
22. Figure 4.22 Exponential function #14: decreasing curves, horizontally asymptoti...
23. Figure 4.23 Simulated FFP signatures: power function #1.
24. Figure 4.24 Simulated DPS from FFP signatures: power function #1.
25. Figure 4.25 Simulated FFP signatures: power function #3.
26. Figure 4.26 Simulated DPS from FFP signatures: power function #3.
27. Figure 4.27 Simulated FFP signatures: power function #5.
28. Figure 4.28 Simulated DPS from FFP signatures: power function #5.
29. Figure 4.29 Simulated FFP signatures: power function #7.
30. Figure 4.30 Simulated DPS from FFP signatures: power function #7.
31. Figure 4.31 Simulated FFP signatures: power function #9.
32. Figure 4.32 Simulated DPS from FFP signatures: power function #9.
33. Figure 4.33 Simulated FFP signatures: exponential function #11.
34. Figure 4.34 Simulated DPS from FFP signatures: exponential function #11.
35. Figure 4.35 Simulated FFP signatures: exponential function #13.
36. Figure 4.36 Simulated DPS from FFP signatures: exponential function #13.
37. Figure 4.37 Simulated FFP and DPS signatures: power function #1 for n = 2.0.
38. Figure 4.38 Simulated FFP and DPS signatures: power function #3 for n = 2.0.
39. Figure 4.39 Simulated FFP and DPS signatures: power function #5 for n = 1.5.
40. Figure 4.40 Simulated FFP and DPS signatures: power function #7 for n = 0.75.
41. Figure 4.41 Simulated FFP and DPS signatures: power function #9 for n = 0.25.
42. Figure 4.42 Simulated FFP and DPS signatures: exponential function #11 for P₀ =...
43. Figure 4.43 Simulated FFP and DPS signatures: exponential function #13 for P₀ =...
44. Figure 4.44 Example plots: FFS (top) and FFP and DPS (bottom): power function #...
45. Figure 4.45 Example plots: FFS (top) and FFP and DPS (bottom): power function #...
Chapter 5
1. Figure 5.1 Example of a non‐ideal FFP signature and an ideal representation of...
2. Figure 5.2 Plots of a family of FFP signatures and DPS transfer curves.
3. Figure 5.3 Plots of a curvilinear FFP, the transform to a linear DPS (top), and...
4. Figure 5.4 Offline phase to develop a prognostic‐enabling solution of a PHM sys...
5. Figure 5.5 Diagram of an online phase to exploit a prognostic‐enabling solution...
6. Figure 5.6 Plot of a non‐ideal CBD signature data: noisy ripple voltage, output...
7. Figure 5.7 Example plots: non‐ideal FFP signature data (top) and transformed DP...
8. Figure 5.8 Example plots: non‐ideal and ideal FFS data (top) and FNL (bottom).
9. Figure 5.9 Example plots: non‐ideal and ideal FFS data (top) and FNL (bottom) a...
10. Figure 5.10 Example plot: non‐ideal ADC transfer curve.
11. Figure 5.11 Example plots: ideal input and non‐ideal output from an ADC.
12. Figure 5.12 Temperature (a), voltage (b), and current (c) plots.
13. Figure 5.13 Temperature‐dependent (a) and temperature‐independent (b) plots of ...
14. Figure 5.14 Example of a damped‐ringing response.
15. Figure 5.15 Sampling diagram for Example 5.8.
16. Figure 5.16 ADC example: saw‐tooth input, sampling, digital output value.
17. Figure 5.17 Simulated data before (top) and after (bottom) filtering of white (...
18. Figure 5.18 Degradation signature exhibiting a change in shape.
19. Figure 5.19 Experimental data: temperature measurements for a jet engine.
20. Figure 5.20 Differential signature from temperature measurements for each of tw...
21. Figure 5.21 Temperature data and differential signatures: four engines on an ai...
22. Figure 5.22 Composite differential‐distance signature.
23. Figure 5.23 Example of a noisy FFP signature.
24. Figure 5.24 Example of a noisy FFP signature: calculated nominal FD value, redu...
25. Figure 5.25 Example of a smoothed (3‐point moving average) FFP signature.
26. Figure 5.26 Example of a DPS from a smoothed FFP signature.
27. Figure 5.27 Example of an FFS from a smoothed FFP signature.
28. Figure 5.28 Example of a {FNLi} plot from a smoothed FFS.
29. Figure 5.29 Example random‐walk paths and FFS input.
30. Figure 5.30 Example plots of input FFS data and adjusted FFS data.
31. Figure 5.31 Example of a {FNLi} plot from an adjusted FFS.
32. Figure 5.32 Ripple voltage: plots of an unsmoothed (top) and smoothed (bottom) ...
33. Figure 5.33 Ripple voltage: plots of an unsmoothed (top) and smoothed (bottom) ...
34. Figure 5.34 Smoothed FFS: for NM = 3.0 mV (top) and for NM = 2.0 mV (bottom).
Chapter 6
1. Figure 6.1 Core prognostic frameworks in a PHM system.
2. Figure 6.2 A framework for CBM‐based PHM.
3. Figure 6.3 Random walk with Kalman‐like filtering solution for a high‐value ini...
4. Figure 6.4 Random walk with Kalman‐like filtering solution for a low‐value init...
5. Figure 6.5 Block diagram showing three approaches to PHM.
6. Figure 6.6 Model development and use.
7. Figure 6.7 Diagram: model‐based and CBD‐signature approaches to PHM.
8. Figure 6.8 Example diagram: heuristic‐based CBM system using CBD‐based modeling...
9. Figure 6.9 Procedural diagram for producing a DPS‐based FFS.
10. Figure 6.10 Examples of CBD, FFP, DPS, and DPS‐based FFS.
11. Figure 6.11 DPS‐based FFS and FFP‐based FFS.
12. Figure 6.12 FNL plots for the FFS shown in Figure 6.11 .
13. Figure 6.13 Plots of a family of FFP signatures and DPS transfer curves.
14. Figure 6.14 Offline phase to develop a prognostic‐enabling solution for a PHM s...
15. Figure 6.15 Online phase to exploit a prognostic‐enabling solution.
16. Figure 6.16 Multiple temperature signals: before and after differential‐distanc...
17. Figure 6.17 Extracted FD from fusing differential‐distance conditioned CBD.
18. Figure 6.18 Block diagram of an example EMA subsystem.
19. Figure 6.19 Offline modeling and development diagram.
20. Figure 6.20 Design and analysis diagram.
21. Figure 6.21 Continual (top) and periodic (bottom) sampling.
22. Figure 6.22 Period‐burst sampling: sampling period (TS) and burst period (TB).
23. Figure 6.23 Example of power supplies and EMA subsystems.
24. Figure 6.24 SMPS output showing ripple period (TR) and sampling period (TS).
25. Figure 6.25 Damped‐ringing response caused by an abrupt load change.
26. Figure 6.26 Example of burst of burst sampling.
27. Figure 6.27 Example of alerts issued using an unrealistic PHM system clock and/...
28. Figure 6.28 Steps in test data due to low‐resolution fault injection.
29. Figure 6.29 Diagram of a test bed to inject faults into a power supply.
30. Figure 6.30 Diagram of a test bed to inject faults into an EMA.
31. Figure 6.31 Sampled‐ and windowed‐phase currents: no load (top) and extra load ...
32. Figure 6.32 FFP signature of fault‐injected SMPS.
33. Figure 6.33 Sampled‐phase currents: no load (top) and extra load (bottom).
34. Figure 6.34 Current magnitude: no degradation (high), degraded transistor (redu...
35. Figure 6.35 Shifted levels due to a degraded power‐switching transistor.
36. Figure 6.36 Illustration of using peak positive and negative threshold values.
37. Figure 6.37 Special rms: threshold and truncation.
38. Figure 6.38 Special rms applied to three phase currents.
39. Figure 6.39 FFP signatures due to loading.
40. Figure 6.40 Smoothed FFP signatures due to loading.
41. Figure 6.41 FFS: EMA loading.
42. Figure 6.42 Smoothed FFP signatures due to winding faults.
43. Figure 6.43 FFS: EMA winding.
44. Figure 6.44 Phase A currents: transistor fault in the positive Phase A portion.
45. Figure 6.45 Phase B currents: transistor fault in the positive Phase A portion.
46. Figure 6.46 Phase C currents: transistor fault in the positive Phase A portion.
47. Figure 6.47 Peak‐RMS currents: transistor fault in the positive Phase A portion...
48. Figure 6.48 Close‐up of the EMA motor winding.
49. Figure 6.49 FFP: H bridge fault, sum of both halves of the Phase A current.
50. Figure 6.50 Smoothed FFP: H bridge fault.
51. Figure 6.51 Smoothed FFS: H bridge fault.
52. Figure 6.52 Block diagram for an example of a robust PHM system.
53. Figure 6.53 Architectural block diagram for a node definition.
54. Figure 6.54 Example of a system node definition.
55. Figure 6.55 Block diagram of an example node definition.
56. Figure 6.56 NDEF: node status.
57. Figure 6.57 NDEF: sampling specifications.
58. Figure 6.58 NDEF: alert specifications.
59. Figure 6.59 NDEF: Special Files specifications.
60. Figure 6.60 NDEF: feature‐vector framework – (a) primary, (b) smoothing, (c) FF...
61. Figure 6.61 NDEF: Prediction Framework.
62. Figure 6.62 NDEF: Performance Services/Graphics.
63. Figure 6.63 NDEF: Input & Output Files.
64. Figure 6.64 NDEF: Checkpoint Library & File Name.
65. Figure 6.65 NDEF: Device Driver Program ID and Units of Measure.
66. Figure 6.66 NDEF: Other Program IDs.
67. Figure 6.67 NDEF: End of Definition.
68. Figure 6.68 NDEF updates to support node second SMPS (node 60).
69. Figure 6.69 NDEF updates to support EMA 1 (node 51).
70. Figure 6.70 Illustration and relationship of PH to BD, sample time, RUL, and EO...
71. Figure 6.71 Relationship of degradation times and an FFS.
72. Figure 6.72 Illustration of the uncertainty of determining exactly when functio...
73. Figure 6.73 Uncertainty: prognostic distance.
74. Figure 6.74 CBD at 1‐hour sampling (top) and at 24‐hour sampling (bottom).
75. Figure 6.75 Family of failure curves, failure distribution, and TTF.
76. Figure 6.76 Diagram of an initial estimate for PD.
77. Figure 6.77 Comparison: FFS and plots of RUL and PH estimates.
78. Figure 6.78 Prognostic bus (log file).
79. Figure 6.79 SoH plot from DXARULE.
80. Figure 6.80 Output file for SMPS using ARULEAV.
81. Figure 6.81 Plots of the RUL, PH, and SoH estimates produced by ARULEAV.
82. Figure 6.82 Output file for SMPS using DPS‐based FFS and ARULEAV.
83. Figure 6.83 Plots of the RUL, PH, and SoH estimates using DPS‐based FFS and ARU...
84. Figure 6.84 CBD, FFP, and FFS for EMA node 51 (friction/load).
85. Figure 6.85 EMA (load) plots: RUL, PH, and SoH estimates using DPS‐based FFS an...
86. Figure 6.86 CBD, FFP, and FFS for EMA node 61 (winding).
87. Figure 6.87 EMA (winding) plots: RUL, PH, and SoH estimates; DPS‐based FFS and ...
88. Figure 6.88 CBD, FFP, and FFS for EMA node 62 (power transistor).
89. Figure 6.89 EMA (transistor) plots: RUL, PH, and SoH estimates; DPS‐based FFS a...
90. Figure 6.90 PHM: high‐level control and data flow.
91. Figure 6.91 Initialization: system nodes.
92. Figure 6.92 System alerts, part 1.
93. Figure 6.93 System alerts, part 2.
94. Figure 6.94 Example of a GUI for a PHM system.
Chapter 7
1. Figure 7.1 Example of a broad view of an ecosystem.
2. Figure 7.2 Example of a five‐level model for health solutions.
3. Figure 7.3 Example of alerts for SoH at or below 25%.
4. Figure 7.4 Example of alerts for a damage‐detection approach.
5. Figure 7.5 MTTF, TTF, and PITTFF0: CBD signature and failure distribution.
6. Figure 7.6 Same MTTF for different failure distributions and signatures.
7. Figure 7.7 Failure plots with average values for TTF and PD = PITTFF0.
8. Figure 7.8 Estimated PD and actual PD: power supply (top) and EMA load (bottom)...
9. Figure 7.9 Estimated PD and actual PD: EMA winding (top) and EMA transistor (bo...
10. Figure 7.10 RUL and PH plot for PITTFF0 = 4800 and for PITTFF0 = 2290.
11. Figure 7.11 RUL and PH plots for SMPS: before and after PITTFADJ = 2.0.
12. Figure 7.12Figure 7.12 RUL and PH plots for EMA 51: before and after PITTFADJ =...
13. Figure 7.13Figure 7.13 RUL and PH plots for EMA 61: before and after PITTFADJ =...
14. Figure 7.14 RUL and PH plots for EMA 62: before and after PITTFADJ = 2.0.
15. Figure 7.15 Plots: test results for six power supplies (top) and 12 EMAs (botto...
16. Figure 7.16 Bathtub curve showing three regions, MTBF, and a prognostic trigger...
17. Figure 7.17 Possible relationship of bathtub curve to failure distribution and ...
18. Figure 7.18 Multiple instances of CBD signatures and trigger points.

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Library of Congress Cataloging‐in‐Publication Data

Names: Goodman, Douglas (Industrial engineer), author. | Hofmeister, James

P., author. | Szidarovszky, Ferenc, author.

Title: Prognostics and health management : a practical approach to improving

system reliability using condition-based data / Douglas Goodman, Chief

Engineer, Ridgetop Group, Inc., James P. Hofmeister, Distinguished

Engineer, Ridgetop Group, Inc., Ferenc Szidarovszky, Senior

Researcher, Ridgetop Group, Inc.

Description: Hoboken, NJ, USA : Wiley, [2019] | Includes bibliographical

references and index. |

Identifiers: LCCN 2018060348 (print) | LCCN 2019000608 (ebook) | ISBN

9781119356691 (AdobePDF) | ISBN 9781119356707 (ePub) | ISBN 9781119356653

(hardcover)

Subjects: LCSH: Machinery--Reliability. | Equipment health monitoring. |

Machinery--Maintenance and repair--Planning. | Structural

failures--Mathematical models.

Classification: LCC TJ174 (ebook) | LCC TJ174 .G66 2019 (print) | DDC

621.8/16--dc23

LC record available at https://lccn.loc.gov/2018060348

Cover Design: Wiley

Wiley Series in Quality & Reliability Engineering

Prognostics and Health Management

A Practical Approach to Improving System Reliability Using Condition‐Based Data

Copyright