Schlagwörter
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