Equipment health monitoring in complex systems /:
This timely resource provides a practical introduction to equipment health monitoring (EHM) to ensure the cost effective operation and control of critical systems in defense, industrial, and healthcare applications. This book highlights how to frame health monitoring design applications within a sys...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston :
Artech House,
[2018]
|
| Schriftenreihe: | Artech House computing library.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This timely resource provides a practical introduction to equipment health monitoring (EHM) to ensure the cost effective operation and control of critical systems in defense, industrial, and healthcare applications. This book highlights how to frame health monitoring design applications within a system engineering process, to ensure an optimized EHM functional architecture and practical algorithm design.n nThis book clarifies the need for intelligent diagnostics and proposed health monitoring framework. Machine learning for health monitoring, including feature extraction, data visualization, model boundaries and performance is presented. Details about monitoring aircraft engines and model based monitoring systems are described in detail. Packed with two full chapters of case studies within industrial and healthcare settings, this book identifies key problems and provides insightful techniques for solving them. This resource provides a look into the future direction in health monitoring and emerging developments within sensing technology, big data analytics, and advanced computing capabilities. |
| Beschreibung: | 1 online resource (ix, 208 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781630814977 1630814970 |
Internformat
MARC
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| 100 | 1 | |a King, Stephen P., |e author. |0 http://id.loc.gov/authorities/names/no2018003727 | |
| 245 | 1 | 0 | |a Equipment health monitoring in complex systems / |c Stephen P. King, Andrew R. Mills, Visakan Kadirkamanathan, David A. Clifton. |
| 264 | 1 | |a Boston : |b Artech House, |c [2018] | |
| 300 | |a 1 online resource (ix, 208 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Artech House computing library | |
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | 0 | |g Machine generated contents note: |g 1. |t Introduction -- |g 1.1. |t Maintenance Strategies -- |g 1.2. |t Overview of Health Monitoring -- |g 1.3. |t Organization of Book Contents -- |t References -- |g 2. |t Systems Engineering for EHM -- |g 2.1. |t Introduction -- |g 2.2. |t Introduction to Systems Engineering -- |g 2.2.1. |t Systems Engineering Processes -- |g 2.2.2. |t Overview of Systems Engineering for EHM Design -- |g 2.2.3. |t Summary -- |g 2.3. |t EHM Design Intent -- |g 2.3.1. |t State the Problem: Failure Analysis and Management -- |g 2.3.2. |t Model the System: Approaches for Failure Modeling -- |g 2.3.3. |t Investigate Alternatives: Failure Models -- |g 2.3.4. |t Assess Performance: Case Study -- |g 2.4. |t EHM Functional Architecture Design -- |g 2.4.1. |t State the Problem: EHM Functional Architecture Design -- |g 2.4.2. |t Model the System: Function Modeling and Assessment -- |g 2.4.3. |t Investigate Alternatives: Tools for Functional Architecture Design -- |g 2.4.4. |t Assess Performance: Gas Turbine EHM Architecture Optimization -- |g 2.5. |t EHM Algorithm Design -- |g 2.5.1. |t State the Problem: Monitoring Algorithm Design Process -- |g 2.5.2. |t Model the System: Detailed Fault Mode Modeling -- |g 2.5.3. |t Investigate Alternatives: Development Approaches -- |g 2.5.4. |t Assess Performance: Algorithm Design Case Study -- |g 2.6. |t Conclusion -- |t References -- |g 3. |t The Need for Intelligent Diagnostics -- |g 3.1. |t Introduction -- |g 3.2. |t The Need for Intelligent Diagnostics -- |g 3.3. |t Overview of Machine Learning Capability -- |g 3.4. |t Proposed Health Monitoring Framework -- |g 3.4.1. |t Feature Extraction -- |g 3.4.2. |t Data Visualization -- |g 3.4.3. |t Model Construction -- |g 3.4.4. |t Definition of Model Boundaries -- |g 3.4.5. |t Verification of Model Performance -- |t References -- |g 4. |t Machine Learning for Health Monitoring -- |g 4.1. |t Introduction -- |g 4.2. |t Feature Extraction -- |g 4.3. |t Data Visualization -- |g 4.3.1. |t Principal Component Analysis -- |g 4.3.2. |t Kohonen Network -- |g 4.3.3. |t Sammon's Mapping -- |g 4.3.4. |t NeuroScale -- |g 4.4. |t Model Construction -- |g 4.5. |t Definition of Model Boundaries -- |g 4.6. |t Verification of Model Performance -- |g 4.6.1. |t Verification of Regression Models -- |g 4.6.2. |t Verification of Classification Models -- |t References -- |g 5. |t Case Studies of Medical Monitoring Systems -- |g 5.1. |t Introduction -- |g 5.2. |t Kernel Density Estimates -- |g 5.3. |t Extreme Value Statistics -- |g 5.3.1. |t Type-I EVT -- |g 5.3.2. |t Type-II EVT -- |g 5.3.3. |t Gaussian Processes -- |g 5.4. |t Advanced Methods -- |t References -- |g 6. |t Monitoring Aircraft Engines -- |g 6.1. |t Introduction -- |g 6.1.1. |t Aircraft Engines -- |g 6.1.2. |t Model-Based Monitoring Systems -- |g 6.2. |t Case Study -- |g 6.2.1. |t Aircraft Engine Air System Event Detection -- |g 6.2.2. |t Data and the Detection Problem -- |g 6.3. |t Kalman Filter-Based Detection -- |g 6.3.1. |t Kalman Filter Estimation -- |g 6.3.2. |t Kalman Filter Parameter Design -- |g 6.3.3. |t Change Detection and Threshold Selection -- |g 6.4. |t Multiple Model-Based Detection -- |g 6.4.1. |t Hypothesis Testing and Change Detection -- |g 6.4.2. |t Multiple Model Change Detection -- |g 6.5. |t Change Detection with Additional Signals -- |g 6.6. |t Summary -- |t References -- |g 7. |t Future Directions in Health Monitoring -- |g 7.1. |t Introduction -- |g 7.2. |t Emerging Developments Within Sensing Technology -- |g 7.2.1. |t Low-Cost and Ubiquitous Sensing -- |g 7.2.2. |t Ultra-Minaturization -- Nano and Quantum -- |g 7.2.3. |t Bio-Inspired -- |g 7.2.4. |t Summary -- |g 7.3. |t Sensor Informatics for Medical Monitoring -- |g 7.3.1. |t Deep Learning for Patient Monitoring -- |g 7.4. |t Big Data Analytics and Health Monitoring -- |g 7.5. |t Growth in Use of Digital Storage -- |g 7.5.1. |t Example Health Monitoring Application Utilizing Grid Capability -- |g 7.5.2. |t Cloud Alternatives -- |t References. |
| 520 | 3 | |a This timely resource provides a practical introduction to equipment health monitoring (EHM) to ensure the cost effective operation and control of critical systems in defense, industrial, and healthcare applications. This book highlights how to frame health monitoring design applications within a system engineering process, to ensure an optimized EHM functional architecture and practical algorithm design.n nThis book clarifies the need for intelligent diagnostics and proposed health monitoring framework. Machine learning for health monitoring, including feature extraction, data visualization, model boundaries and performance is presented. Details about monitoring aircraft engines and model based monitoring systems are described in detail. Packed with two full chapters of case studies within industrial and healthcare settings, this book identifies key problems and provides insightful techniques for solving them. This resource provides a look into the future direction in health monitoring and emerging developments within sensing technology, big data analytics, and advanced computing capabilities. |c Publisher abstract. | |
| 650 | 0 | |a Systems engineering. |0 http://id.loc.gov/authorities/subjects/sh85131750 | |
| 650 | 0 | |a Structural health monitoring. |0 http://id.loc.gov/authorities/subjects/sh2009006088 | |
| 650 | 6 | |a Ingénierie des systèmes. | |
| 650 | 6 | |a Surveillance de l'état des structures. | |
| 650 | 7 | |a systems engineering. |2 aat | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Engineering (General) |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Reference. |2 bisacsh | |
| 650 | 7 | |a Structural health monitoring |2 fast | |
| 650 | 7 | |a Systems engineering |2 fast | |
| 700 | 1 | |a Mills, Andrew R., |e author. |0 http://id.loc.gov/authorities/names/no2018003714 | |
| 700 | 1 | |a Kadirkamanathan, Visakan, |d 1962- |e author. |1 https://id.oclc.org/worldcat/entity/E39PBJbtqwHdkXw6cgW8xKFYfq |0 http://id.loc.gov/authorities/names/n2001001135 | |
| 700 | 1 | |a Clifton, David A., |e author. |0 http://id.loc.gov/authorities/names/nb2017003609 | |
| 758 | |i has work: |a Equipment health monitoring in complex systems (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFtH3cHHTpPtCDyBP47wP3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a King, Stephen P. |t Equipment health monitoring in complex systems. |d Boston : Artech House, [2018] |z 1608079724 |w (DLC) 2017285746 |w (OCoLC)1013764660 |
| 830 | 0 | |a Artech House computing library. |0 http://id.loc.gov/authorities/names/n2004051466 | |
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Datensatz im Suchindex
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| _version_ | 1816882414963654656 |
| adam_text | |
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| author | King, Stephen P. Mills, Andrew R. Kadirkamanathan, Visakan, 1962- Clifton, David A. |
| author_GND | http://id.loc.gov/authorities/names/no2018003727 http://id.loc.gov/authorities/names/no2018003714 http://id.loc.gov/authorities/names/n2001001135 http://id.loc.gov/authorities/names/nb2017003609 |
| author_facet | King, Stephen P. Mills, Andrew R. Kadirkamanathan, Visakan, 1962- Clifton, David A. |
| author_role | aut aut aut aut |
| author_sort | King, Stephen P. |
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| bvnumber | localFWS |
| callnumber-first | T - Technology |
| callnumber-label | TA168 |
| callnumber-raw | TA168 .K56 2018eb |
| callnumber-search | TA168 .K56 2018eb |
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| callnumber-subject | TA - General and Civil Engineering |
| collection | ZDB-4-EBA |
| contents | Introduction -- Maintenance Strategies -- Overview of Health Monitoring -- Organization of Book Contents -- References -- Systems Engineering for EHM -- Introduction to Systems Engineering -- Systems Engineering Processes -- Overview of Systems Engineering for EHM Design -- Summary -- EHM Design Intent -- State the Problem: Failure Analysis and Management -- Model the System: Approaches for Failure Modeling -- Investigate Alternatives: Failure Models -- Assess Performance: Case Study -- EHM Functional Architecture Design -- State the Problem: EHM Functional Architecture Design -- Model the System: Function Modeling and Assessment -- Investigate Alternatives: Tools for Functional Architecture Design -- Assess Performance: Gas Turbine EHM Architecture Optimization -- EHM Algorithm Design -- State the Problem: Monitoring Algorithm Design Process -- Model the System: Detailed Fault Mode Modeling -- Investigate Alternatives: Development Approaches -- Assess Performance: Algorithm Design Case Study -- Conclusion -- The Need for Intelligent Diagnostics -- Overview of Machine Learning Capability -- Proposed Health Monitoring Framework -- Feature Extraction -- Data Visualization -- Model Construction -- Definition of Model Boundaries -- Verification of Model Performance -- Machine Learning for Health Monitoring -- Principal Component Analysis -- Kohonen Network -- Sammon's Mapping -- NeuroScale -- Verification of Regression Models -- Verification of Classification Models -- Case Studies of Medical Monitoring Systems -- Kernel Density Estimates -- Extreme Value Statistics -- Type-I EVT -- Type-II EVT -- Gaussian Processes -- Advanced Methods -- Monitoring Aircraft Engines -- Aircraft Engines -- Model-Based Monitoring Systems -- Case Study -- Aircraft Engine Air System Event Detection -- Data and the Detection Problem -- Kalman Filter-Based Detection -- Kalman Filter Estimation -- Kalman Filter Parameter Design -- Change Detection and Threshold Selection -- Multiple Model-Based Detection -- Hypothesis Testing and Change Detection -- Multiple Model Change Detection -- Change Detection with Additional Signals -- Future Directions in Health Monitoring -- Emerging Developments Within Sensing Technology -- Low-Cost and Ubiquitous Sensing -- Ultra-Minaturization -- Nano and Quantum -- Bio-Inspired -- Sensor Informatics for Medical Monitoring -- Deep Learning for Patient Monitoring -- Big Data Analytics and Health Monitoring -- Growth in Use of Digital Storage -- Example Health Monitoring Application Utilizing Grid Capability -- Cloud Alternatives -- References. |
| ctrlnum | (OCoLC)1027678986 |
| dewey-full | 620.001/171 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 620 - Engineering and allied operations |
| dewey-raw | 620.001/171 |
| dewey-search | 620.001/171 |
| dewey-sort | 3620.001 3171 |
| dewey-tens | 620 - Engineering and allied operations |
| format | Electronic eBook |
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| id | ZDB-4-EBA-on1027678986 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:28:14Z |
| institution | BVB |
| isbn | 9781630814977 1630814970 |
| language | English |
| oclc_num | 1027678986 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (ix, 208 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Artech House, |
| record_format | marc |
| series | Artech House computing library. |
| series2 | Artech House computing library |
| spelling | King, Stephen P., author. http://id.loc.gov/authorities/names/no2018003727 Equipment health monitoring in complex systems / Stephen P. King, Andrew R. Mills, Visakan Kadirkamanathan, David A. Clifton. Boston : Artech House, [2018] 1 online resource (ix, 208 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Artech House computing library Print version record. Includes bibliographical references and index. Machine generated contents note: 1. Introduction -- 1.1. Maintenance Strategies -- 1.2. Overview of Health Monitoring -- 1.3. Organization of Book Contents -- References -- 2. Systems Engineering for EHM -- 2.1. Introduction -- 2.2. Introduction to Systems Engineering -- 2.2.1. Systems Engineering Processes -- 2.2.2. Overview of Systems Engineering for EHM Design -- 2.2.3. Summary -- 2.3. EHM Design Intent -- 2.3.1. State the Problem: Failure Analysis and Management -- 2.3.2. Model the System: Approaches for Failure Modeling -- 2.3.3. Investigate Alternatives: Failure Models -- 2.3.4. Assess Performance: Case Study -- 2.4. EHM Functional Architecture Design -- 2.4.1. State the Problem: EHM Functional Architecture Design -- 2.4.2. Model the System: Function Modeling and Assessment -- 2.4.3. Investigate Alternatives: Tools for Functional Architecture Design -- 2.4.4. Assess Performance: Gas Turbine EHM Architecture Optimization -- 2.5. EHM Algorithm Design -- 2.5.1. State the Problem: Monitoring Algorithm Design Process -- 2.5.2. Model the System: Detailed Fault Mode Modeling -- 2.5.3. Investigate Alternatives: Development Approaches -- 2.5.4. Assess Performance: Algorithm Design Case Study -- 2.6. Conclusion -- References -- 3. The Need for Intelligent Diagnostics -- 3.1. Introduction -- 3.2. The Need for Intelligent Diagnostics -- 3.3. Overview of Machine Learning Capability -- 3.4. Proposed Health Monitoring Framework -- 3.4.1. Feature Extraction -- 3.4.2. Data Visualization -- 3.4.3. Model Construction -- 3.4.4. Definition of Model Boundaries -- 3.4.5. Verification of Model Performance -- References -- 4. Machine Learning for Health Monitoring -- 4.1. Introduction -- 4.2. Feature Extraction -- 4.3. Data Visualization -- 4.3.1. Principal Component Analysis -- 4.3.2. Kohonen Network -- 4.3.3. Sammon's Mapping -- 4.3.4. NeuroScale -- 4.4. Model Construction -- 4.5. Definition of Model Boundaries -- 4.6. Verification of Model Performance -- 4.6.1. Verification of Regression Models -- 4.6.2. Verification of Classification Models -- References -- 5. Case Studies of Medical Monitoring Systems -- 5.1. Introduction -- 5.2. Kernel Density Estimates -- 5.3. Extreme Value Statistics -- 5.3.1. Type-I EVT -- 5.3.2. Type-II EVT -- 5.3.3. Gaussian Processes -- 5.4. Advanced Methods -- References -- 6. Monitoring Aircraft Engines -- 6.1. Introduction -- 6.1.1. Aircraft Engines -- 6.1.2. Model-Based Monitoring Systems -- 6.2. Case Study -- 6.2.1. Aircraft Engine Air System Event Detection -- 6.2.2. Data and the Detection Problem -- 6.3. Kalman Filter-Based Detection -- 6.3.1. Kalman Filter Estimation -- 6.3.2. Kalman Filter Parameter Design -- 6.3.3. Change Detection and Threshold Selection -- 6.4. Multiple Model-Based Detection -- 6.4.1. Hypothesis Testing and Change Detection -- 6.4.2. Multiple Model Change Detection -- 6.5. Change Detection with Additional Signals -- 6.6. Summary -- References -- 7. Future Directions in Health Monitoring -- 7.1. Introduction -- 7.2. Emerging Developments Within Sensing Technology -- 7.2.1. Low-Cost and Ubiquitous Sensing -- 7.2.2. Ultra-Minaturization -- Nano and Quantum -- 7.2.3. Bio-Inspired -- 7.2.4. Summary -- 7.3. Sensor Informatics for Medical Monitoring -- 7.3.1. Deep Learning for Patient Monitoring -- 7.4. Big Data Analytics and Health Monitoring -- 7.5. Growth in Use of Digital Storage -- 7.5.1. Example Health Monitoring Application Utilizing Grid Capability -- 7.5.2. Cloud Alternatives -- References. This timely resource provides a practical introduction to equipment health monitoring (EHM) to ensure the cost effective operation and control of critical systems in defense, industrial, and healthcare applications. This book highlights how to frame health monitoring design applications within a system engineering process, to ensure an optimized EHM functional architecture and practical algorithm design.n nThis book clarifies the need for intelligent diagnostics and proposed health monitoring framework. Machine learning for health monitoring, including feature extraction, data visualization, model boundaries and performance is presented. Details about monitoring aircraft engines and model based monitoring systems are described in detail. Packed with two full chapters of case studies within industrial and healthcare settings, this book identifies key problems and provides insightful techniques for solving them. This resource provides a look into the future direction in health monitoring and emerging developments within sensing technology, big data analytics, and advanced computing capabilities. Publisher abstract. Systems engineering. http://id.loc.gov/authorities/subjects/sh85131750 Structural health monitoring. http://id.loc.gov/authorities/subjects/sh2009006088 Ingénierie des systèmes. Surveillance de l'état des structures. systems engineering. aat TECHNOLOGY & ENGINEERING Engineering (General) bisacsh TECHNOLOGY & ENGINEERING Reference. bisacsh Structural health monitoring fast Systems engineering fast Mills, Andrew R., author. http://id.loc.gov/authorities/names/no2018003714 Kadirkamanathan, Visakan, 1962- author. https://id.oclc.org/worldcat/entity/E39PBJbtqwHdkXw6cgW8xKFYfq http://id.loc.gov/authorities/names/n2001001135 Clifton, David A., author. http://id.loc.gov/authorities/names/nb2017003609 has work: Equipment health monitoring in complex systems (Text) https://id.oclc.org/worldcat/entity/E39PCFtH3cHHTpPtCDyBP47wP3 https://id.oclc.org/worldcat/ontology/hasWork Print version: King, Stephen P. Equipment health monitoring in complex systems. Boston : Artech House, [2018] 1608079724 (DLC) 2017285746 (OCoLC)1013764660 Artech House computing library. http://id.loc.gov/authorities/names/n2004051466 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1825914 Volltext |
| spellingShingle | King, Stephen P. Mills, Andrew R. Kadirkamanathan, Visakan, 1962- Clifton, David A. Equipment health monitoring in complex systems / Artech House computing library. Introduction -- Maintenance Strategies -- Overview of Health Monitoring -- Organization of Book Contents -- References -- Systems Engineering for EHM -- Introduction to Systems Engineering -- Systems Engineering Processes -- Overview of Systems Engineering for EHM Design -- Summary -- EHM Design Intent -- State the Problem: Failure Analysis and Management -- Model the System: Approaches for Failure Modeling -- Investigate Alternatives: Failure Models -- Assess Performance: Case Study -- EHM Functional Architecture Design -- State the Problem: EHM Functional Architecture Design -- Model the System: Function Modeling and Assessment -- Investigate Alternatives: Tools for Functional Architecture Design -- Assess Performance: Gas Turbine EHM Architecture Optimization -- EHM Algorithm Design -- State the Problem: Monitoring Algorithm Design Process -- Model the System: Detailed Fault Mode Modeling -- Investigate Alternatives: Development Approaches -- Assess Performance: Algorithm Design Case Study -- Conclusion -- The Need for Intelligent Diagnostics -- Overview of Machine Learning Capability -- Proposed Health Monitoring Framework -- Feature Extraction -- Data Visualization -- Model Construction -- Definition of Model Boundaries -- Verification of Model Performance -- Machine Learning for Health Monitoring -- Principal Component Analysis -- Kohonen Network -- Sammon's Mapping -- NeuroScale -- Verification of Regression Models -- Verification of Classification Models -- Case Studies of Medical Monitoring Systems -- Kernel Density Estimates -- Extreme Value Statistics -- Type-I EVT -- Type-II EVT -- Gaussian Processes -- Advanced Methods -- Monitoring Aircraft Engines -- Aircraft Engines -- Model-Based Monitoring Systems -- Case Study -- Aircraft Engine Air System Event Detection -- Data and the Detection Problem -- Kalman Filter-Based Detection -- Kalman Filter Estimation -- Kalman Filter Parameter Design -- Change Detection and Threshold Selection -- Multiple Model-Based Detection -- Hypothesis Testing and Change Detection -- Multiple Model Change Detection -- Change Detection with Additional Signals -- Future Directions in Health Monitoring -- Emerging Developments Within Sensing Technology -- Low-Cost and Ubiquitous Sensing -- Ultra-Minaturization -- Nano and Quantum -- Bio-Inspired -- Sensor Informatics for Medical Monitoring -- Deep Learning for Patient Monitoring -- Big Data Analytics and Health Monitoring -- Growth in Use of Digital Storage -- Example Health Monitoring Application Utilizing Grid Capability -- Cloud Alternatives -- References. Systems engineering. http://id.loc.gov/authorities/subjects/sh85131750 Structural health monitoring. http://id.loc.gov/authorities/subjects/sh2009006088 Ingénierie des systèmes. Surveillance de l'état des structures. systems engineering. aat TECHNOLOGY & ENGINEERING Engineering (General) bisacsh TECHNOLOGY & ENGINEERING Reference. bisacsh Structural health monitoring fast Systems engineering fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85131750 http://id.loc.gov/authorities/subjects/sh2009006088 |
| title | Equipment health monitoring in complex systems / |
| title_alt | Introduction -- Maintenance Strategies -- Overview of Health Monitoring -- Organization of Book Contents -- References -- Systems Engineering for EHM -- Introduction to Systems Engineering -- Systems Engineering Processes -- Overview of Systems Engineering for EHM Design -- Summary -- EHM Design Intent -- State the Problem: Failure Analysis and Management -- Model the System: Approaches for Failure Modeling -- Investigate Alternatives: Failure Models -- Assess Performance: Case Study -- EHM Functional Architecture Design -- State the Problem: EHM Functional Architecture Design -- Model the System: Function Modeling and Assessment -- Investigate Alternatives: Tools for Functional Architecture Design -- Assess Performance: Gas Turbine EHM Architecture Optimization -- EHM Algorithm Design -- State the Problem: Monitoring Algorithm Design Process -- Model the System: Detailed Fault Mode Modeling -- Investigate Alternatives: Development Approaches -- Assess Performance: Algorithm Design Case Study -- Conclusion -- The Need for Intelligent Diagnostics -- Overview of Machine Learning Capability -- Proposed Health Monitoring Framework -- Feature Extraction -- Data Visualization -- Model Construction -- Definition of Model Boundaries -- Verification of Model Performance -- Machine Learning for Health Monitoring -- Principal Component Analysis -- Kohonen Network -- Sammon's Mapping -- NeuroScale -- Verification of Regression Models -- Verification of Classification Models -- Case Studies of Medical Monitoring Systems -- Kernel Density Estimates -- Extreme Value Statistics -- Type-I EVT -- Type-II EVT -- Gaussian Processes -- Advanced Methods -- Monitoring Aircraft Engines -- Aircraft Engines -- Model-Based Monitoring Systems -- Case Study -- Aircraft Engine Air System Event Detection -- Data and the Detection Problem -- Kalman Filter-Based Detection -- Kalman Filter Estimation -- Kalman Filter Parameter Design -- Change Detection and Threshold Selection -- Multiple Model-Based Detection -- Hypothesis Testing and Change Detection -- Multiple Model Change Detection -- Change Detection with Additional Signals -- Future Directions in Health Monitoring -- Emerging Developments Within Sensing Technology -- Low-Cost and Ubiquitous Sensing -- Ultra-Minaturization -- Nano and Quantum -- Bio-Inspired -- Sensor Informatics for Medical Monitoring -- Deep Learning for Patient Monitoring -- Big Data Analytics and Health Monitoring -- Growth in Use of Digital Storage -- Example Health Monitoring Application Utilizing Grid Capability -- Cloud Alternatives -- References. |
| title_auth | Equipment health monitoring in complex systems / |
| title_exact_search | Equipment health monitoring in complex systems / |
| title_full | Equipment health monitoring in complex systems / Stephen P. King, Andrew R. Mills, Visakan Kadirkamanathan, David A. Clifton. |
| title_fullStr | Equipment health monitoring in complex systems / Stephen P. King, Andrew R. Mills, Visakan Kadirkamanathan, David A. Clifton. |
| title_full_unstemmed | Equipment health monitoring in complex systems / Stephen P. King, Andrew R. Mills, Visakan Kadirkamanathan, David A. Clifton. |
| title_short | Equipment health monitoring in complex systems / |
| title_sort | equipment health monitoring in complex systems |
| topic | Systems engineering. http://id.loc.gov/authorities/subjects/sh85131750 Structural health monitoring. http://id.loc.gov/authorities/subjects/sh2009006088 Ingénierie des systèmes. Surveillance de l'état des structures. systems engineering. aat TECHNOLOGY & ENGINEERING Engineering (General) bisacsh TECHNOLOGY & ENGINEERING Reference. bisacsh Structural health monitoring fast Systems engineering fast |
| topic_facet | Systems engineering. Structural health monitoring. Ingénierie des systèmes. Surveillance de l'état des structures. systems engineering. TECHNOLOGY & ENGINEERING Engineering (General) TECHNOLOGY & ENGINEERING Reference. Structural health monitoring Systems engineering |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1825914 |
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