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Predictive Maintenance in Dynamic Systems : Advanced Methods, Decision Support Tools and Real-World Applications / edited by Edwin Lughofer, Moamar Sayed-Mouchaweh

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink Bücher | Springer eBook CollectionPublisher: Cham : Springer International Publishing, 2019Description: Online-Ressource (XIII, 567 p. 200 illus., 144 illus. in color, online resource)ISBN:
  • 9783030056452
Subject(s): Additional physical formats: 9783030056445 | 9783030056469 | Erscheint auch als: Predictive maintenance in dynamic systems. Druck-Ausgabe. Cham : Springer, 2019. xiii, 567 Seiten | Printed edition: 9783030056445 | Printed edition: 9783030056469 DDC classification:
  • 621.382
LOC classification:
  • TK1-9971
DOI: DOI: 10.1007/978-3-030-05645-2Online resources: Summary: 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 powerSummary: 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 -- ConclusionPPN: PPN: 1067372377Package identifier: Produktsigel: ZDB-2-ENG | ZDB-2-SEB | ZDB-2-SXE
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