Prognostics and health management: a practical approach to improving system reliability using condition-based data
A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life. Prognostics and Health Management provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improvi...
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2019
|
| Schriftenreihe: | Wiley series in quality & reliability engineering
|
| Schlagwörter: | |
| Links: | https://ebookcentral.proquest.com/lib/th-deggendorf/detail.action?docID=5744605 https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=5744605 |
| Zusammenfassung: | A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life. Prognostics and Health Management 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. Written by noted experts in the field, Prognostics and Health Management 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: -Integrates data collecting, mathematical modelling and reliability prediction in one volume -Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes -Presents information from a panel of experts on the topic -Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods Written for system engineers working in critical process industries and automotive and aerospace designers, Prognostics and Health Management 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 |
| Beschreibung: | 3.1 Introduction to Failure Signatures |
| Umfang: | 1 Online-Ressource (xxvii, 354 Seiten) |
| ISBN: | 9781119356691 9781119356707 |
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| 245 | 1 | 0 | |a Prognostics and health management |b a practical approach to improving system reliability using condition-based data |c Douglas Goodman, James P. Hofmeister and Ferenc Szidarovszky |
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2019 | |
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| 500 | |a 3.1 Introduction to Failure Signatures | ||
| 505 | 8 | |a Cover; Title Page; Copyright; Contents; List of Figures; Series Editor's Foreword; Preface; Acknowledgments; Chapter 1 Introduction to Prognostics; 1.1 What Is Prognostics?; 1.1.1 Chapter Objectives; 1.1.2 Chapter Organization; 1.2 Foundation of Reliability Theory; 1.2.1 Time-to-Failure Distributions; 1.2.2 Probability and Reliability; 1.2.3 Probability Density Function; 1.2.4 Relationships of Distributions; 1.2.5 Failure Rate; 1.2.6 Expected Value and Variance; 1.3 Failure Distributions Under Extreme Stress Levels; 1.3.1 Basic Models; 1.3.2 Cumulative Damage Models | |
| 505 | 8 | |a 1.3.3 General Exponential Models1.4 Uncertainty Measures in Parameter Estimation; 1.5 Expected Number of Failures; 1.5.1 Minimal Repair; 1.5.2 Failure Replacement; 1.5.3 Decreased Number of Failures Due to Partial Repairs; 1.5.4 Decreased Age Due to Partial Repairs; 1.6 System Reliability and Prognosis and Health Management; 1.6.1 General Framework for a CBM-Based PHM System; 1.6.2 Relationship of PHM to System Reliability; 1.6.3 Degradation Progression Signature (DPS) and Prognostics; 1.6.4 Ideal Functional Failure Signature (FFS) and Prognostics; 1.6.5 Non-ideal FFS and Prognostics | |
| 505 | 8 | |a Chapter 2 Approaches for Prognosis and Health Management/Monitoring (PHM)2.1 Introduction to Approaches for Prognosis and Health Management/Monitoring (PHM); 2.1.1 Model-Based Prognostic Approaches; 2.1.2 Data-Driven Prognostic Approaches; 2.1.3 Hybrid Prognostic Approaches; 2.1.4 Chapter Objectives; 2.1.5 Chapter Organization; 2.2 Model-Based Prognostics; 2.2.1 Analytical Modeling; 2.2.2 Distribution Modeling; 2.2.3 Physics of Failure (PoF) and Reliability Modeling; 2.2.4 Acceleration Factor (AF); 2.2.5 Complexity Related to Reliability Modeling; 2.2.6 Failure Distribution | |
| 505 | 8 | |a 2.2.7 Multiple Modes of Failure: Failure Rate and FIT2.2.8 Advantages and Disadvantages of Model-Based Prognostics; 2.3 Data-Driven Prognostics; 2.3.1 Statistical Methods; 2.3.2 Machine Learning (ML): Classification and Clustering; 2.4 Hybrid-Driven Prognostics; 2.5 An Approach to Condition-Based Maintenance (CBM); 2.5.1 Modeling of Condition-Based Data (CBD) Signatures; 2.5.2 Comparison of Methodologies: Life Consumption and CBD Signature; 2.5.3 CBD-Signature Modeling: An Illustration; 2.6 Approaches to PHM: Summary; References; Further Reading; Chapter 3 Failure Progression Signatures | |
| 520 | |a A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life. Prognostics and Health Management 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. Written by noted experts in the field, Prognostics and Health Management 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: -Integrates data collecting, mathematical modelling and reliability prediction in one volume -Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes -Presents information from a panel of experts on the topic -Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods Written for system engineers working in critical process industries and automotive and aerospace designers, Prognostics and Health Management 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 | ||
| 650 | 7 | |a TECHNOLOGY & ENGINEERING / Quality Control |2 bisacsh | |
| 650 | 4 | |a Technology | |
| 650 | 7 | |a Technology |2 fast | |
| 650 | 4 | |a Machinery-Reliability. | |
| 650 | 4 | |a Equipment health monitoring. | |
| 650 | 4 | |a Machinery-Maintenance and repair-Planning. | |
| 650 | 4 | |a Structural failures-Mathematical models | |
| 700 | 1 | |a Hofmeister, James P. |e Verfasser |4 aut | |
| 700 | 1 | |a Szidarovszky, Ferenc |d 1945- |e Verfasser |0 (DE-588)121012158 |4 aut | |
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Datensatz im Suchindex
| DE-BY-TUM_katkey | 2555498 |
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| _version_ | 1821936157801316354 |
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| author | Goodman, Douglas Hofmeister, James P. Szidarovszky, Ferenc 1945- |
| author_GND | (DE-588)121012158 |
| author_facet | Goodman, Douglas Hofmeister, James P. Szidarovszky, Ferenc 1945- |
| author_role | aut aut aut |
| author_sort | Goodman, Douglas |
| author_variant | d g dg j p h jp jph f s fs |
| building | Verbundindex |
| bvnumber | BV046227570 |
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| contents | Cover; Title Page; Copyright; Contents; List of Figures; Series Editor's Foreword; Preface; Acknowledgments; Chapter 1 Introduction to Prognostics; 1.1 What Is Prognostics?; 1.1.1 Chapter Objectives; 1.1.2 Chapter Organization; 1.2 Foundation of Reliability Theory; 1.2.1 Time-to-Failure Distributions; 1.2.2 Probability and Reliability; 1.2.3 Probability Density Function; 1.2.4 Relationships of Distributions; 1.2.5 Failure Rate; 1.2.6 Expected Value and Variance; 1.3 Failure Distributions Under Extreme Stress Levels; 1.3.1 Basic Models; 1.3.2 Cumulative Damage Models 1.3.3 General Exponential Models1.4 Uncertainty Measures in Parameter Estimation; 1.5 Expected Number of Failures; 1.5.1 Minimal Repair; 1.5.2 Failure Replacement; 1.5.3 Decreased Number of Failures Due to Partial Repairs; 1.5.4 Decreased Age Due to Partial Repairs; 1.6 System Reliability and Prognosis and Health Management; 1.6.1 General Framework for a CBM-Based PHM System; 1.6.2 Relationship of PHM to System Reliability; 1.6.3 Degradation Progression Signature (DPS) and Prognostics; 1.6.4 Ideal Functional Failure Signature (FFS) and Prognostics; 1.6.5 Non-ideal FFS and Prognostics Chapter 2 Approaches for Prognosis and Health Management/Monitoring (PHM)2.1 Introduction to Approaches for Prognosis and Health Management/Monitoring (PHM); 2.1.1 Model-Based Prognostic Approaches; 2.1.2 Data-Driven Prognostic Approaches; 2.1.3 Hybrid Prognostic Approaches; 2.1.4 Chapter Objectives; 2.1.5 Chapter Organization; 2.2 Model-Based Prognostics; 2.2.1 Analytical Modeling; 2.2.2 Distribution Modeling; 2.2.3 Physics of Failure (PoF) and Reliability Modeling; 2.2.4 Acceleration Factor (AF); 2.2.5 Complexity Related to Reliability Modeling; 2.2.6 Failure Distribution 2.2.7 Multiple Modes of Failure: Failure Rate and FIT2.2.8 Advantages and Disadvantages of Model-Based Prognostics; 2.3 Data-Driven Prognostics; 2.3.1 Statistical Methods; 2.3.2 Machine Learning (ML): Classification and Clustering; 2.4 Hybrid-Driven Prognostics; 2.5 An Approach to Condition-Based Maintenance (CBM); 2.5.1 Modeling of Condition-Based Data (CBD) Signatures; 2.5.2 Comparison of Methodologies: Life Consumption and CBD Signature; 2.5.3 CBD-Signature Modeling: An Illustration; 2.6 Approaches to PHM: Summary; References; Further Reading; Chapter 3 Failure Progression Signatures |
| ctrlnum | (OCoLC)1126557205 (DE-599)BVBBV046227570 |
| dewey-full | 621.816 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.816 |
| dewey-search | 621.816 |
| dewey-sort | 3621.816 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Maschinenbau / Maschinenwesen Technik Technik |
| format | Electronic eBook |
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| id | DE-604.BV046227570 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T18:46:38Z |
| institution | BVB |
| isbn | 9781119356691 9781119356707 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031606102 |
| oclc_num | 1126557205 |
| open_access_boolean | |
| owner | DE-1050 DE-91 DE-BY-TUM |
| owner_facet | DE-1050 DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (xxvii, 354 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE FHD01_PQE_Kauf ZDB-30-PQE TUM_PDA_PQE_Kauf |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in quality & reliability engineering |
| spellingShingle | Goodman, Douglas Hofmeister, James P. Szidarovszky, Ferenc 1945- Prognostics and health management a practical approach to improving system reliability using condition-based data Cover; Title Page; Copyright; Contents; List of Figures; Series Editor's Foreword; Preface; Acknowledgments; Chapter 1 Introduction to Prognostics; 1.1 What Is Prognostics?; 1.1.1 Chapter Objectives; 1.1.2 Chapter Organization; 1.2 Foundation of Reliability Theory; 1.2.1 Time-to-Failure Distributions; 1.2.2 Probability and Reliability; 1.2.3 Probability Density Function; 1.2.4 Relationships of Distributions; 1.2.5 Failure Rate; 1.2.6 Expected Value and Variance; 1.3 Failure Distributions Under Extreme Stress Levels; 1.3.1 Basic Models; 1.3.2 Cumulative Damage Models 1.3.3 General Exponential Models1.4 Uncertainty Measures in Parameter Estimation; 1.5 Expected Number of Failures; 1.5.1 Minimal Repair; 1.5.2 Failure Replacement; 1.5.3 Decreased Number of Failures Due to Partial Repairs; 1.5.4 Decreased Age Due to Partial Repairs; 1.6 System Reliability and Prognosis and Health Management; 1.6.1 General Framework for a CBM-Based PHM System; 1.6.2 Relationship of PHM to System Reliability; 1.6.3 Degradation Progression Signature (DPS) and Prognostics; 1.6.4 Ideal Functional Failure Signature (FFS) and Prognostics; 1.6.5 Non-ideal FFS and Prognostics Chapter 2 Approaches for Prognosis and Health Management/Monitoring (PHM)2.1 Introduction to Approaches for Prognosis and Health Management/Monitoring (PHM); 2.1.1 Model-Based Prognostic Approaches; 2.1.2 Data-Driven Prognostic Approaches; 2.1.3 Hybrid Prognostic Approaches; 2.1.4 Chapter Objectives; 2.1.5 Chapter Organization; 2.2 Model-Based Prognostics; 2.2.1 Analytical Modeling; 2.2.2 Distribution Modeling; 2.2.3 Physics of Failure (PoF) and Reliability Modeling; 2.2.4 Acceleration Factor (AF); 2.2.5 Complexity Related to Reliability Modeling; 2.2.6 Failure Distribution 2.2.7 Multiple Modes of Failure: Failure Rate and FIT2.2.8 Advantages and Disadvantages of Model-Based Prognostics; 2.3 Data-Driven Prognostics; 2.3.1 Statistical Methods; 2.3.2 Machine Learning (ML): Classification and Clustering; 2.4 Hybrid-Driven Prognostics; 2.5 An Approach to Condition-Based Maintenance (CBM); 2.5.1 Modeling of Condition-Based Data (CBD) Signatures; 2.5.2 Comparison of Methodologies: Life Consumption and CBD Signature; 2.5.3 CBD-Signature Modeling: An Illustration; 2.6 Approaches to PHM: Summary; References; Further Reading; Chapter 3 Failure Progression Signatures TECHNOLOGY & ENGINEERING / Quality Control bisacsh Technology Technology fast Machinery-Reliability. Equipment health monitoring. Machinery-Maintenance and repair-Planning. Structural failures-Mathematical models |
| title | Prognostics and health management a practical approach to improving system reliability using condition-based data |
| title_auth | Prognostics and health management a practical approach to improving system reliability using condition-based data |
| title_exact_search | Prognostics and health management a practical approach to improving system reliability using condition-based data |
| title_full | Prognostics and health management a practical approach to improving system reliability using condition-based data Douglas Goodman, James P. Hofmeister and Ferenc Szidarovszky |
| title_fullStr | Prognostics and health management a practical approach to improving system reliability using condition-based data Douglas Goodman, James P. Hofmeister and Ferenc Szidarovszky |
| title_full_unstemmed | Prognostics and health management a practical approach to improving system reliability using condition-based data Douglas Goodman, James P. Hofmeister and Ferenc Szidarovszky |
| title_short | Prognostics and health management |
| title_sort | prognostics and health management a practical approach to improving system reliability using condition based data |
| title_sub | a practical approach to improving system reliability using condition-based data |
| topic | TECHNOLOGY & ENGINEERING / Quality Control bisacsh Technology Technology fast Machinery-Reliability. Equipment health monitoring. Machinery-Maintenance and repair-Planning. Structural failures-Mathematical models |
| topic_facet | TECHNOLOGY & ENGINEERING / Quality Control Technology Machinery-Reliability. Equipment health monitoring. Machinery-Maintenance and repair-Planning. Structural failures-Mathematical models |
| work_keys_str_mv | AT goodmandouglas prognosticsandhealthmanagementapracticalapproachtoimprovingsystemreliabilityusingconditionbaseddata AT hofmeisterjamesp prognosticsandhealthmanagementapracticalapproachtoimprovingsystemreliabilityusingconditionbaseddata AT szidarovszkyferenc prognosticsandhealthmanagementapracticalapproachtoimprovingsystemreliabilityusingconditionbaseddata |