Predictive Maintenance in Dynamic Systems

Advanced Methods, Decision Support Tools and Real-World Applications

  • Edwin Lughofer
  • Moamar Sayed-Mouchaweh

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Edwin Lughofer, Moamar Sayed-Mouchaweh
    Pages 1-23
  3. Eike Permin, Florian Lindner, Kevin Kostyszyn, Dennis Grunert, Karl Lossie, Robert Schmitt et al.
    Pages 25-51
  4. Anomaly Detection and Localization

    1. Front Matter
      Pages 95-95
    2. Chamari I. Kithulgoda, Russel Pears
      Pages 97-129
    3. Yevgeniy Bodyanskiy, Artem Dolotov, Dmytro Peleshko, Yuriy Rashkevych, Olena Vynokurova
      Pages 131-166
    4. Goran Andonovski, Sašo Blažič, Igor Škrjanc
      Pages 269-285
    5. Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer, Qing Cai, Huang Sheng
      Pages 287-309
  5. Prognostics and Forecasting

    1. Front Matter
      Pages 311-311
    2. Iñigo Lecuona, Rosa Basagoiti, Gorka Urchegui, Luka Eciolaza, Urko Zurutuza, Peter Craamer
      Pages 381-401
  6. Diagnosis, Optimization and Control

  7. Back Matter
    Pages 555-567

About this book


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.  


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

Editors and affiliations

  • Edwin Lughofer
    • 1
  • Moamar Sayed-Mouchaweh
    • 2
  1. 1.Fuzzy Logic Laboratorium Linz-Hagenberg, Department of Knowledge-Based Mathematical SystemsJohannes Kepler University LinzLinzAustria
  2. 2.Institute Mines-Telecom Lille DouaiDouaiFrance

Bibliographic information