Predictive Maintenance in Dynamic Systems
Advanced Methods, Decision Support Tools and Real-World Applications
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Beschreibung
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.
Covers recent developments in predictive maintenance, including basic algorithms and methods, required notions, and definitions
Discusses problems in application domains such as on-line production lines, factories of the future, (cyber) security installations, IoT, and energy facilities
Studies the links between methods and techniques as well as open challenges of predictive maintenance, early fault diagnosis, and on-line decision support tools in dynamic systems
Covers recent developments in predictive maintenance, including basic algorithms and methods, required notions, and definitions Discusses problems in application domains such as on-line production lines, factories of the future, , IoT, power plants and turbines, conditioning systems, railway infrastructures, equipment and tool monitoring, and energy facilities Studies the links between methods and techniques as well as open challenges of predictive maintenance, addresses early fault detection, prognostics and diagnosis, and optimization and self–healing techniques for on-line decision support tools in dynamic systems
Autor*in
Edwin Lughofer
Themen in »Predictive Maintenance in Dynamic Systems«
Predictive Maintenance in Dynamic Systems Fault Detection and Diagnosis Fault Prognostics and Forecasting Applications of Predictive Maintenance Industry 4.0 challenges Prediction in dynamic networks quality control, reliability, safety and risk