Reprint

Structural Prognostics and Health Management in Power & Energy Systems

Edited by
January 2020
216 pages
  • ISBN978-3-03921-766-3 (Paperback)
  • ISBN978-3-03921-767-0 (PDF)

This book is a reprint of the Special Issue Structural Prognostics and Health Management in Power & Energy Systems that was published in

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

The idea of preparing an Energies Special Issue on “Structural Prognostics and Health Management in Power & Energy Systems” is to compile information on the recent advances in structural prognostics and health management (SPHM). Continued improvements on SPHM have been made possible through advanced signature analysis, performance degradation assessment, as well as accurate modeling of failure mechanisms by introducing advanced mathematical approaches/tools. Through combining deterministic and probabilistic modeling techniques, research on SPHM can provide assurance for new structures at a design stage and ensure construction integrity at a fabrication phase. Specifically, power and energy system failures occur under multiple sources of uncertainty/variability resulting from load variations in usage, material properties, geometry variations within tolerances, and other uncontrolled variations. Thus, advanced methods and applications for theoretical, numerical, and experimental contributions that address these issues on SPHM are desired and expected, which attempt to prevent overdesign and unnecessary inspection and provide tools to enable a balance between safety and economy to be achieved. This Special Issue has attracted submissions from China, USA, Portugal, and Italy. A total of 26 submissions were received and 11 articles finally published.

Format
  • Paperback
License and Copyright
© 2020 by the authors; CC BY-NC-ND license
Keywords
prognostics; residual useful life; similarity-based approach; supporting vector machine (SVM); reliability; non-probabilistic reliability index; sensitivity analysis; techno-economic assessments; life cycle cost; vibration transmission mechanism; underground powerhouse; lateral-river vibration; low frequency tail fluctuation; rotation of hydraulic generator; vertical axis wind turbine; structural health monitoring; operational modal analysis; stochastic subspace identification; vibration test; offshore structures; oil and gas platforms; offshore wind turbines; retrofitting activities; renewable energy; dynamic analysis; wind and wave analysis; dynamic analysis of the structure; wave–structure interaction (WSI); probabilistic analyses of stochastic processes and frequency; data-driven; machine learning; deep learning; DNN; prognostic and Health Management; lithium-ion battery; wind turbines; health monitoring; fault detection; optimized deep belief networks; supervisory control and data acquisition system; multioperation condition; wind turbine blade; full-scale static test; neural networks; strain prediction; dynamic fuzzy reliability analysis; extremum surface response method; weighted regression; turbine blisk; fuzzy safety criterion; lithium-ion battery; remaining useful life; regeneration phenomenon; wavelet decomposition; NAR neural network; empirical mode decomposition; analysis mode decomposition; analysis-empirical mode decomposition; mode mixing; sifting stop criterion