In this paper, the main objective is to underline the possibility of identifying simplified
mechanical models of complex mechanical systems through the numerical techniques of …

To increase the contribution of offshore wind energy to the global energy mix in an
economically sustainable manner, it is required to reduce the costs associated with the …

The intersection of physics and machine learning has given rise to the physics-enhanced
machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the …

Operational modal analysis plays an important role in the structural health monitoring and
safety diagnosis of arch dam. However, due to background noise, it is difficult to accurately …

This paper proposes a linear recursive Bayesian filter for minimum variance unbiased joint
input and state estimation of structural systems. Unlike the augmented Kalman filter (AKF) …

The uncertainty afflicting modal parameter estimates stems from eg, the finite data length,
unknown, or partly measured inputs and the choice of the identification algorithm …

The mode shape is one of the important modal parameters that enables to visualize the
intrinsic behavior of a structure as well as the quantity of interest by extracting or separating …

We present a novel approach to harmonic disturbance removal in single-channel wind
turbine acceleration data by means of time-variant signal modeling. Harmonics are …

Many parameters can be used to express a machine's condition and to track its evolution
through time, such as modal parameters extracted from vibration signals. Operational Modal …

Abstract Model order selection is a fundamental task in subspace identification for estimation
of modal parameters, uncertainty propagation and damage diagnosis. However, the true …