Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 110

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

Gaussian process models for mitigation of operational variability in the structural health monitoring of wind turbines

Avendaño-Valencia, Luis David, Chatzi, Eleni N., Tcherniak, Dmitri·2020·Mechanical Systems and Signal Processing
DOI 10.1016/j.ymssp.2020.106686
Open this paper in CoChat — free account

Abstract

Abstract The analysis presented in this work relates to the quantification of the effect of a selected set of measured Environmental and Operational Parameters (EOPs) on the dynamic properties of low and high frequency vibration, in the context of a vibration monitoring campaign implemented on the blade of an operating wind turbine. To this end, a Gaussian Process (GP) time-series modelling approach is adopted, in which the coefficients of a time-series model are driven by a Gaussian Process Regression on the selected EOPs. The properties of the data acquisition system allow to evaluate low and high frequency dynamics, the former associated with the structural dynamics of the blade, and the latter with the wave transmission properties of the material, assessed with the help of an electro-mechanical actuator installed on the blade. In this form, a multi-temporal-scale approach is adopted here, where a GP Linear Parameter Varying Auto-Regressive model is selected to represent low frequency (structural) dynamics, while in parallel a GP Continuous Wavelet Transform model is used to represent high frequency dynamics (associated with wave transmission properties in the material). In both cases the blade is considered in its healthy state as well as in various operational regimes, including idle, and rotating at two different set points. As a result, it is demonstrated that GP time-series modelling succeeds in evaluating and isolating the influence of different EOPs in the features of the vibration response of the wind turbine blade, and at the same time, normalize their effects to enhance the detectability of damage.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.ymssp.2020.106686

Cites

35 of 35 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 1

  1. F. Kopsaftopoulos, R. Nardari, Y. Li, F. Chang, A stochastic global identification framework for aerospace vehicles operating under varying flight states, CoRR abs/1612.04896 (2016). arXiv:1612.04896. · arXiv

Cited by

See all 110

Metadata

Authors
Avendaño-Valencia, L. D., Chatzi, E. N., & Tcherniak, D.
ORCID
Tcherniak, Dmitri
Venue
Mechanical Systems and Signal Processing
Publisher
Elsevier BV
Published
2020-08
Volume
142
Pages
106686
ISSN
0888-3270
DOI
10.1016/j.ymssp.2020.106686
Cited by
110
References
35
Language
en
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
  • Eidgenössische Technische Hochschule Zürich
  • European Research Council

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.