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Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications

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Personen und Körperschaften: Lughofer, Edwin (HerausgeberIn), Sayed-Mouchaweh, Moamar (HerausgeberIn)
Titel: Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications/ edited by Edwin Lughofer, Moamar Sayed-Mouchaweh
Format: E-Book
Sprache: Englisch
veröffentlicht:
Cham Springer International Publishing 2019

Gesamtaufnahme: SpringerLink
Springer eBook Collection
Schlagwörter:
Telecommunication
System safety
Engineering
Information systems
Communications Engineering, Networks
Electrical engineering.
Quality control.
Reliability.
Industrial safety.
Control engineering.
Computational intelligence.
Computers.
Erscheint auch als: Predictive maintenance in dynamic systems, Cham : Springer, 2019, xiii, 567 Seiten
Quelle: Verbunddaten SWB
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520 |a Introduction -- Predictive Maintenance and (Early) FDD in Dynamic Systems -- Beyond State-of-the-Art -- Early Fault Detection and Diagnosis Approaches -- Prognostics and Forecasting -- Self-Reaction and Self-Healing Techniques -- Applications of Predictive Maintenance with emphasize on Industry 4.0 challenges -- Conclusion 
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contents This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power, Introduction -- Predictive Maintenance and (Early) FDD in Dynamic Systems -- Beyond State-of-the-Art -- Early Fault Detection and Diagnosis Approaches -- Prognostics and Forecasting -- Self-Reaction and Self-Healing Techniques -- Applications of Predictive Maintenance with emphasize on Industry 4.0 challenges -- Conclusion
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spelling Predictive Maintenance in Dynamic Systems Advanced Methods, Decision Support Tools and Real-World Applications edited by Edwin Lughofer, Moamar Sayed-Mouchaweh, Cham Springer International Publishing 2019, Online-Ressource (XIII, 567 p. 200 illus., 144 illus. in color, online resource), Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, SpringerLink Bücher, Springer eBook Collection, This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power, Introduction -- Predictive Maintenance and (Early) FDD in Dynamic Systems -- Beyond State-of-the-Art -- Early Fault Detection and Diagnosis Approaches -- Prognostics and Forecasting -- Self-Reaction and Self-Healing Techniques -- Applications of Predictive Maintenance with emphasize on Industry 4.0 challenges -- Conclusion, Springer eBook Collection. Engineering, Telecommunication, System safety, Engineering, Information systems, Communications Engineering, Networks, Electrical engineering., Quality control., Reliability., Industrial safety., Control engineering., Computational intelligence., Computers., Lughofer, Edwin HerausgeberIn (DE-588)136536476 (DE-627)584497326 (DE-576)301070369 edt, Sayed-Mouchaweh, Moamar HerausgeberIn edt, 9783030056445, 9783030056469, Erscheint auch als Druck-Ausgabe Predictive maintenance in dynamic systems Cham : Springer, 2019 xiii, 567 Seiten (DE-627)1048596737 9783030056445, Printed edition 9783030056445, Printed edition 9783030056469, https://doi.org/10.1007/978-3-030-05645-2 X:SPRINGER Verlag lizenzpflichtig Volltext, http://dx.doi.org/10.1007/978-3-030-05645-2 Resolving-System Volltext, https://swbplus.bsz-bw.de/bsz518435717cov.jpg V:DE-576 X:Springer image/jpeg 20190307133959 Cover, https://doi.org/10.1007/978-3-030-05645-2 DE-14, DE-14 epn:358857008X 2021-02-05T11:07:47Z, https://doi.org/10.1007/978-3-030-05645-2 DE-Ch1, DE-Ch1 epn:3384136543 2019-03-07T12:08:17Z, DE-105 epn:3384136616 2019-03-07T12:08:17Z, https://doi.org/10.1007/978-3-030-05645-2 DE-Zwi2, DE-Zwi2 epn:3384136810 2019-03-07T12:08:17Z
spellingShingle Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications, This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power, Introduction -- Predictive Maintenance and (Early) FDD in Dynamic Systems -- Beyond State-of-the-Art -- Early Fault Detection and Diagnosis Approaches -- Prognostics and Forecasting -- Self-Reaction and Self-Healing Techniques -- Applications of Predictive Maintenance with emphasize on Industry 4.0 challenges -- Conclusion, Telecommunication, System safety, Engineering, Information systems, Communications Engineering, Networks, Electrical engineering., Quality control., Reliability., Industrial safety., Control engineering., Computational intelligence., Computers.
swb_id_str 518435717
title Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications
title_auth Predictive Maintenance in Dynamic Systems Advanced Methods, Decision Support Tools and Real-World Applications
title_full Predictive Maintenance in Dynamic Systems Advanced Methods, Decision Support Tools and Real-World Applications edited by Edwin Lughofer, Moamar Sayed-Mouchaweh
title_fullStr Predictive Maintenance in Dynamic Systems Advanced Methods, Decision Support Tools and Real-World Applications edited by Edwin Lughofer, Moamar Sayed-Mouchaweh
title_full_unstemmed Predictive Maintenance in Dynamic Systems Advanced Methods, Decision Support Tools and Real-World Applications edited by Edwin Lughofer, Moamar Sayed-Mouchaweh
title_short Predictive Maintenance in Dynamic Systems
title_sort predictive maintenance in dynamic systems advanced methods, decision support tools and real-world applications
title_sub Advanced Methods, Decision Support Tools and Real-World Applications
title_unstemmed Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications
topic Telecommunication, System safety, Engineering, Information systems, Communications Engineering, Networks, Electrical engineering., Quality control., Reliability., Industrial safety., Control engineering., Computational intelligence., Computers.
topic_facet Telecommunication, System safety, Engineering, Information systems, Communications Engineering, Networks, Electrical engineering., Quality control., Reliability., Industrial safety., Control engineering., Computational intelligence., Computers.
url https://doi.org/10.1007/978-3-030-05645-2, http://dx.doi.org/10.1007/978-3-030-05645-2, https://swbplus.bsz-bw.de/bsz518435717cov.jpg
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