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Book synopsis

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 and 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.

Book details

• Full title: Predictive Maintenance in Dynamic Systems
• Subtitle: Advanced Methods, Decision Support Tools and Real-World Applications
• Author: Edwin Lughofer, Moamar Sayed-Mouchaweh
• Language: English
• Binding: Hardback
• Number of pages: 567
• EAN: 9783030056445
• ID: 20637644
• Publisher: Springer Nature Switzerland AG
• Weight: 1033 g
• Dimensions: 235 × 155 × 37 mm
• Date of publishing: 12. March 2019

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