Prognostics:
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
[Erscheinungsort nicht ermittelbar]
CreateSpace
[2017]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Titelzusatz auf Buchdeckel: the science of prediction |
| Beschreibung: | xviii, 378 Seiten Illustrationen, Diagramme |
| ISBN: | 1539074838 9781539074830 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV044295755 | ||
| 003 | DE-604 | ||
| 005 | 20190311 | ||
| 007 | t | ||
| 008 | 170504s2017 a||| |||| 00||| eng d | ||
| 020 | |a 1539074838 |9 1-5390-7483-8 | ||
| 020 | |a 9781539074830 |9 978-1-5390-7483-0 | ||
| 035 | |a (OCoLC)992480094 | ||
| 035 | |a (DE-599)GBV884044998 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-91G |a DE-634 |a DE-83 |a DE-706 | ||
| 084 | |a ZG 9270 |0 (DE-625)156055: |2 rvk | ||
| 084 | |a WIR 540f |2 stub | ||
| 100 | 1 | |a Göbel, Kai |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Prognostics |c Kai Goebel, Matthew Daigle, Abhinav Saxena, Shankar Sankararaman, Indranil Roychoudhury and José Celaya |
| 264 | 1 | |a [Erscheinungsort nicht ermittelbar] |b CreateSpace |c [2017] | |
| 300 | |a xviii, 378 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Titelzusatz auf Buchdeckel: the science of prediction | ||
| 650 | 0 | 7 | |a Prognostik |0 (DE-588)4175837-7 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Prognostik |0 (DE-588)4175837-7 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Daigle, Matthew |e Verfasser |4 aut | |
| 700 | 1 | |a Saxena, Abhinav |e Verfasser |4 aut | |
| 700 | 1 | |a Sankararaman, Shankar |e Verfasser |4 aut | |
| 700 | 1 | |a Roychoudhury, Indranil |e Verfasser |4 aut | |
| 700 | 1 | |a Celaya, José |e Verfasser |4 aut | |
| 856 | 4 | 2 | |m HEBIS Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029699783&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-029699783 | ||
Datensatz im Suchindex
| _version_ | 1804177497445629952 |
|---|---|
| adam_text | Contents
1 Introduction 1
1 1 The Science of Prediction 1
1 2 Background 3
121 Predictions in PHM 3
Reliability-based Prediction Methods 4
Damage Accumulation-based Prediction 5
Data Analytics-based Prediction or Predictive Analytics 6
Condition-based Prediction or Prognostics 7
122 Prognostics for PHM 7
123 Prognostics Principles 8
124 Importance of Uncertainty 9
125 An Illustrative Prognostics Example 10
1 3 Prognostic Algorithm Elements 11
131 System Model 12
132 Propagation Model 13
133 Prediction Algorithm 13
134 Threshold Event 14
1 4 Predictions in Other Fields 15
1 5 Summary 16
Bibliography 18
2 Architecture and Design 19
2 1 Introduction 19
2 2 Problem Definition 19
2 3 Prognostics Architectures 23
231 Preprocessing 23
Sensing 24
xi
CONTE
Data Processing
232 Estimation
233 Prediction
234 Prognostics within a PHM Architecture
2 4 Prognostics Design
241 Analysis
242 Concept
243 Synthesis
244 Example: Prognostics Design for Legacy Systems
2 5 Feature Extraction Examples
251 Precursor of failure features
2 6 Summary
Bibliography
3 Modeling
3 1 Introduction
3 2 Prognostic Modeling Framework
321 Distinctions for End-of-life Modeling
322 Modeling Approaches
3 3 Learning Models from Data
331 Model Parameters
332 Uncertainty in Learning Models
333 Algorithm Choice
3 4 Physics-based Modeling
341 Modeling for Failure Prediction
3 5 Model Development
351 Model Scope
352 Model Abstraction
353 Model Fidelity
354 Computational Costs
355 Model Development Process
3 6 Case Studies
361 Pneumatic Valve
Nominal Model
Damage Modeling
362 Li-ion Battery 1
Background •»
Electrical Circuit Equivalent Modeling
Electrochemistry-based Modeling
363 Crack Growth in Metals _■
Crack Growth Modeling
CONTENTS
xiii
Concept of Equivalent Initial Flaw Size 105
Fatigue Life Prediction 107
3 7 Summary 107
Bibliography 109
4 Uncertainty 113
4 1 Introduction 113
4 2 Sources of Uncertainty 117
4 3 Formal Treatment of Uncertainty 120
431 Uncertainty Representation and Interpretation 121
432 Uncertainty Quantification 122
433 Uncertainty Propagation 122
434 Uncertainty Management 123
4 4 Fundamentals of Probability 124
441 Discrete Probability 124
442 Continuous Probability 124
443 Random Processes 126
Definition of a Random Process 126
Importance in Prognostics 128
Representation through Surrogate Variables 129
4 5 Uncertainty Interpretation 131
451 Physical Probability and Reliability-based Prediction 131
Physical Probability 131
Reliability-based Prediction 132
Confidence Intervals 133
452 Subjective Probability and Condition-Based Prognostics 133
Subjective Probabilities 133
Subjective Probabilites in Prognostics 134
4 6 Uncertainty Quantification 134
461 Probabilistic Prediction 135
462 Probabilistic Estimation 139
| Least Squares Optimization 140
The Likelihood Method 140
Bayesian Inference for Estimation 141
Parameter Estimation vs State Estimation 143
Evaluating the Bayesian Posterior Distribution 143
Sequential Inference in Prognostics 144
4 7 Uncertainty Propagation 144
471 Prediction through Uncertainty Propagation 145
472 Computing Prediction Uncertainty 146
473 Simple Analytical Illustration 148
CONTE
474 Practical Problems
475 Uncertainty Propagation Methods for Prediction
4 8 Uncertainty Management T
4 9 Existing Challenges 3
4 10 Summary ,
Bibliography 1
5 State Estimation S
5 1 Introduction 1
5 2 Machine Learning Methods
5 3 Filtering Approaches 1
531 Filtering for Linear Systems 16
Kalman Filter
532 Filtering for Nonlinear Systems 1, ;
Extended Kalman Filter 1
Unscented Kalman Filter 16-
Particle Filter t ‘
5 4 Summary j
Bibliography W ■
6 Prediction iff
6 1 Introduction /
6 2 Data-driven Prediction Methods 1 1
621 Pattern Matching Approaches 1; 1
622 Regression Approaches 1
Neural Networks /■
Relevance Vector Machine 18
Gaussian Process Regression 1
623 Example Comparison /■
6 3 Sampling-based Methods •
631 Monte Carlo Prediction ’
632 Unscented Transform Prediction
6 4 Analytical Methods
641 First-Order Second Moment Method
642 First-Order Reliability Method
Extension to Non-normal Variables •
643 Inverse First-Order Reliability Method
644 Practical Challenges 2
6 5 Planetary Rover Application 21
CONTENTS
xv
651 Battery Model 205
652 Future Input Characterization 205
653 Results 206
Constant-Loading Scenario 207
Variable-Loading Scenario 211
654 Discussion 215
6 6 Summary 217
Bibliography 218
7 Performance Evaluation 221
7 1 Introduction 221
7 2 Challenges 222
7 3 Significance of Metrics 224
7 4 Key Considerations 226
741 Attributes of Prediction Performance 226
Correctness 227
Timeliness 227
Confidence 228
742 Type of Prediction Method 229
743 Purpose of Performance Evaluation 232
744 Offline vs Online Evaluation 235
745 Conventional Methods 237
7 5 Prognostic Performance Metrics 241
751 Other Metrics 247
7 6 Summary 249
Bibliography 250
8 Decision Making 253
8 1 Introduction 253
8 2 Controller Reallocation 256
821 Controller Optimization 257
822 Example: Optimization for Deteriorated Engines 259
Experiment Setup 261
Optimization of Control System Modifiers for a Deterio-
rated Engine 263
Discussion 264
8 3 Mission Rescheduling 266
831 Search Algorithms 266
832 Receding-Horizon Search 269
CONTE ;•
833 Prognosis-enhanced Mission Rescheduling
834 Illustrative Example
835 Discussion
8 4 Plan Optimization
841 Evolutionary Optimization
842 Multi-objective Decision Support
843 Human-Machine Interface
Interactive Decision Preference Expression
844 Illustrative Example
Input Data
Optimization Results
8 5 Summary
Bibliography
2r
27
2
W
278
280
281
283
2 /
286
286
287
289,
9 Cost Benefit and Risk
29
9 1 Introduction
9 2 Cost-Benefit for Prognostics
921 Role of Prognostic Performance
922 Safety vs Cost Goals
Cost
Safety
923 Deriving Requirements for Prognostics
9 3 Cost-Benefit Analysis Approaches
931 Goal Driven Cost-Benefit Approaches
932 CBA for PHM Applications
Functional Category
Analytical Category
933 Example Cost-benefit Analyses
9 4 PHM Factors to Consider in CBA
941 PHM System Complexity
942 Types of Failures Targeted
943 System Usage Pattern
944 Design for PHM vs Retrofit Legacy Systems
945 Payback Period
946 Stakeholder Perspectives
947 Other PHM-related Parameters
9 5 Challenges
951 How to Quantify PHM Benefits
952 Accounting for Costs
953 Access to Information
9 6 Summary
29
, 292
29
29
290
290
29
29
29
299
2
30
30
307
308
30
3/
3 3
• 3l
3
• 31
3i
3
3
• 3,
CONTENTS xvii
Bibliography 315
10 Prognostic Requirements 317
10 1 Introduction 317
10 2 Role of Requirements 318
10 3 Requirements Process 319
10 3 1 Requirements Engineering 322
10 4 Prognostic Requirements 325
10 4 1 Metrics Driving Requirements 328
Lead Time for Advance Warning 328
Limits on Early Predictions 329
Limits on Late Predictions 330
Confidence in Prognosis 331
10 5 Requirements Flowdown 332
10 5 1 The electric-UAV Scenario 333
Safety Assessment and Derivation of Functional Re-
quirements for UAVs 333
Risks of Battery Failures 334
High-Level Requirements 335
10 5 2 Deriving Prognostic Requirements 340
Lead Time for Advance Warning 340
Limits on Early Prediction 341
Limits on Late Predictions 342
Required Confidence in Prognosis 343
10 6 Summary 344
Bibliography 345
11 Verification amp; Validation 347
11 1 Introduction 347
11 2 Product vs Technology 348
11 2 1 Product Development 349
Verification and Validation in Product Development 350
11 2 2 Technology Maturation 351
Verification and Validation in Technology Maturation 352
11 3 Verification of Prognostics 354
11 4 Validation of Prognostics 357
11 5 Case Study 358
11 6 Summary 366
Bibliography 367
xviii CONTI
A Notation
A l Abbreviations
A 2 Variables 3
A 3 Functions 9
|
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| id | DE-604.BV044295755 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:49:01Z |
| institution | BVB |
| isbn | 1539074838 9781539074830 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029699783 |
| oclc_num | 992480094 |
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| owner | DE-91G DE-BY-TUM DE-634 DE-83 DE-706 |
| owner_facet | DE-91G DE-BY-TUM DE-634 DE-83 DE-706 |
| physical | xviii, 378 Seiten Illustrationen, Diagramme |
| publishDate | 2017 |
| publishDateSearch | 2017 |
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| publisher | CreateSpace |
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| spelling | Göbel, Kai Verfasser aut Prognostics Kai Goebel, Matthew Daigle, Abhinav Saxena, Shankar Sankararaman, Indranil Roychoudhury and José Celaya [Erscheinungsort nicht ermittelbar] CreateSpace [2017] xviii, 378 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Titelzusatz auf Buchdeckel: the science of prediction Prognostik (DE-588)4175837-7 gnd rswk-swf Prognostik (DE-588)4175837-7 s DE-604 Daigle, Matthew Verfasser aut Saxena, Abhinav Verfasser aut Sankararaman, Shankar Verfasser aut Roychoudhury, Indranil Verfasser aut Celaya, José Verfasser aut HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029699783&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Göbel, Kai Daigle, Matthew Saxena, Abhinav Sankararaman, Shankar Roychoudhury, Indranil Celaya, José Prognostics Prognostik (DE-588)4175837-7 gnd |
| subject_GND | (DE-588)4175837-7 |
| title | Prognostics |
| title_auth | Prognostics |
| title_exact_search | Prognostics |
| title_full | Prognostics Kai Goebel, Matthew Daigle, Abhinav Saxena, Shankar Sankararaman, Indranil Roychoudhury and José Celaya |
| title_fullStr | Prognostics Kai Goebel, Matthew Daigle, Abhinav Saxena, Shankar Sankararaman, Indranil Roychoudhury and José Celaya |
| title_full_unstemmed | Prognostics Kai Goebel, Matthew Daigle, Abhinav Saxena, Shankar Sankararaman, Indranil Roychoudhury and José Celaya |
| title_short | Prognostics |
| title_sort | prognostics |
| topic | Prognostik (DE-588)4175837-7 gnd |
| topic_facet | Prognostik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029699783&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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