Reprint

Maintenance Management of Wind Turbines

Edited by
July 2020
394 pages
  • ISBN978-3-03936-629-3 (Hardback)
  • ISBN978-3-03936-630-9 (PDF)

This book is a reprint of the Special Issue Maintenance Management of Wind Turbines that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements.

Format
  • Hardback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
ice detection; wind turbine blades; SCADA data; random forest classifier; power curve; confusion matrix; wind turbine; laser technology; diagnosis; pitch angle misalignment; efficiency; durability; wind turbine; anemometer; kernel-based multidimensional probability density function; ERA5 reanalysis; wind turbine; blade damage diagnosis; wavelet transform; operational modal analysis; modal strain energy (MSE); wind energy; rotor blade; wind turbine; drone inspection; damage detection; deep learning; Convolutional Neural Network (CNN); offshore wind farm; offshore wind turbine; maintenance; failure classification; resource decision; uncertainty; maintenance management; wind turbines; clustering; reliability; dynamic opportunistic maintenance; simulation; imbalance fault detection; LSTM; attention mechanism; blades with ice; condition monitoring; fault diagnosis; survey; wind turbine (WT); electrical signal; maintenance planning; maintenance strategy; maintenance; corrective maintenance; repair; offshore wind energy; maintenance scheduling; optimization; modeling; fault-tolerant control; Kalman filter; model predictive control; wind turbines; pitch system; dynamic fault monitoring; selective ensemble learning; small-world neural network (SWNN); reliability; condition monitoring; condition based maintenance; wind turbine; oil debris monitoring; gearbox; condition monitoring; preventive maintenance; sensor; uncertainty quantification; false positive; false negative; maintenance cost; wind turbines; fault prediction; stacking model; normal behavior model; change-point detection; SCADA; wind turbines; condition monitoring; inference; neural networks; remaining-useful-lifetime; main bearing; wind turbines; equivalent wind speed; rotor aerodynamic imbalance; stator winding asymmetry; fault characteristics; wind turbine; maintenance; autoencoder; machine learning; reliability; data driven model; service; performance; offshore wind energy; transmission system; HVDC; voltage source converter (VSC); maintenance; missing energy export; fault detection and diagnosis; wavelet transforms; non-destructive tests; guided waves; wind turbine blade; wind turbine maintenance; climbing robot; low cost; weather independent operations; condition monitoring; odometry on wind turbines