Predictive Maintenance in Dynamic Systems

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.