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Predictive maintenance in dynamic sy...

Lughofer, Edwin.

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Predictive maintenance in dynamic systems = advanced methods, decision support tools and real-world applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predictive maintenance in dynamic systems/ edited by Edwin Lughofer, Moamar Sayed-Mouchaweh.
Reminder of title:	advanced methods, decision support tools and real-world applications /
other author:	Lughofer, Edwin.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xiii, 567 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Plant maintenance. -
Online resource:	https://doi.org/10.1007/978-3-030-05645-2
ISBN:	9783030056452

Predictive maintenance in dynamic systems = advanced 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

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

656672
Plant maintenance.

LC Class. No.: TS192 / .P743 2019

Dewey Class. No.: 658.202

Predictive maintenance in dynamic systems = advanced methods, decision support tools and real-world applications /
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245 0 0 $a Predictive maintenance in dynamic systems $h [electronic resource] : $b advanced methods, decision support tools and real-world applications / $c edited by Edwin Lughofer, Moamar Sayed-Mouchaweh.
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300 $a xiii, 567 p. : $b ill., digital ; $c 24 cm.
505 0 $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.
520 $a 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.
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700 1 $a Lughofer, Edwin. $3 1569766
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