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

Edwin Lughofer (Curatore)

Moamar Sayed-Mouchaweh (Curatore)

Springer Nature Switzerland AG, 2019

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 ofThings. 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.  

• Curatore: Edwin Lughofer, Moamar Sayed-Mouchaweh
• Editore: Springer Nature Switzerland AG
• Anno: 2019
• Rilegatura: Hardback
• Pagine: 567 p.
• Dimensioni: 235 x 155 mm
• EAN: 9783030056445

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