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Predictive maintenance in dynamic sy...

Lughofer, Edwin.

Predictive maintenance in dynamic systemsadvanced methods, decision support tools and real-world applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predictive maintenance in dynamic systemsedited by Edwin Lughofer, Moamar Sayed-Mouchaweh.
其他題名:	advanced methods, decision support tools and real-world applications /
其他作者:	Lughofer, Edwin.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xiii, 567 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Plant maintenance.
電子資源:	https://doi.org/10.1007/978-3-030-05645-2
ISBN:	9783030056452$q(electronic bk.)

Predictive maintenance in dynamic systemsadvanced methods, decision support tools and real-world applications /
Predictive maintenance in dynamic systemsadvanced methods, decision support tools and real-world applications /[electronic resource] :edited by Edwin Lughofer, Moamar Sayed-Mouchaweh. - Cham :Springer International Publishing :2019. - xiii, 567 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030056452$q(electronic bk.)

Standard No.: 10.1007/978-3-030-05645-2doiSubjects--Topical Terms:

213984
Plant maintenance.

LC Class. No.: TS192 / .P743 2019

Dewey Class. No.: 658.202

Predictive maintenance in dynamic systemsadvanced methods, decision support tools and real-world applications /
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