An adaptive-noise Augmented Kalman Filter approach for input-state estimation in structural dynamics

S. Vettori; E. Di Lorenzo; Bart Peeters; Marcin Łuczak; E. Chatzi

doi:10.1016/j.ymssp.2022.109654

An adaptive-noise Augmented Kalman Filter approach for input-state estimation in structural dynamics

Abstrakt

The establishment of a Digital Twin of an operating engineered system can increase the potency of Structural Health Monitoring (SHM) tools, which are then bestowed with enhanced predictive capabilities. This is particularly relevant for wind energy infrastructures, where the definition of remaining useful life is a main driver for assessing the efficacy of these systems. In order to ensure a proper representation of the physical structure, the monitored response of the Digital Twin should match the one experienced by the actual system throughout the complete spectrum of its operational conditions. In most typical SHM configurations, it is only possible to rely on output-only measurements, acquired from finite positions within a structure, which naturally raises the challenge of recovering the full-field operational response, including unmeasured locations. This problem, also known as Virtual Sensing (VS), has been treated using different schemes, including Bayesian filtering and Modal Expansion (ME). In this paper, the Augmented Kalman Filter (AKF) is exploited to this end; a tool which allows for simultaneous full-field response and unmeasured input prediction. The common issue of Bayesian filtering relies on calibration of the filters defining parameters, namely the assumed measurement and process noise covariance levels. While the first is directly related to the accuracy of the employed physical sensors, the latter often acts as a tuning parameter for improving the reliability of the prediction. The process noise covariance adjustment is often performed in an offline fashion, either by making use of regularization methods, e.g., the L-curve method, or via trial and error. In this work, we propose a methodology for automated process noise covariance adaptation, relying on response estimates recovered by means of an improved ME approach. The method is validated on experimental data from a large scale research Wind Turbine (WT) blade made of glass fiber reinforced plastics.

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Autorzy (5)

S. Vettori
E. Di Lorenzo
Bart Peeters
Marcin Łuczak dr hab. inż.
E. Chatzi

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Wersja publikacji: Accepted albo Published Version
DOI:: Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.ymssp.2022.109654
Licencja: otwiera się w nowej karcie

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Słowa kluczowe

AUGMENTED KALMAN FILTER INPUT-STATE ESTIMATION NOISE STATISTICS MODAL EXPANSION WIND TURBINE BLADES DIGITAL TWIN

Informacje szczegółowe

Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

MECHANICAL SYSTEMS AND SIGNAL PROCESSING nr 184,
ISSN: 0888-3270

Język:

angielski

Rok wydania:

2023

Opis bibliograficzny:

Vettori S., Di Lorenzo E., Peeters B., Łuczak M., Chatzi E.: An adaptive-noise Augmented Kalman Filter approach for input-state estimation in structural dynamics// MECHANICAL SYSTEMS AND SIGNAL PROCESSING -Vol. 184, (2023), s.109654-

DOI: