Baseline model based structural health monitoring method under varying environment
Xueyan Zhao and
Ziqiang Lang
Renewable Energy, 2019, vol. 138, issue C, 1166-1175
Abstract:
Environment has significant impacts on the structure performance and will change features of sensor measurements on the monitored structure. The effect of varying environment needs to be considered and eliminated while conducting structural health monitoring. In order to achieve this purpose, a baseline model based structural health monitoring method is proposed in this paper. The relationship between signal features and varying environment, known as a baseline model, is first established. Then, a tolerance range of the signal feature is evaluated via a data based statistical analysis. Furthermore, the health indicator, which is defined as the proportion of signal features within the tolerance range, is used to judge whether the structural system is in normal working condition or not so as to implement the structural health monitoring. Finally, experimental data analysis for an operating wind turbine is conducted and the results demonstrate the performance of the proposed new technique.
Keywords: Wind turbine; Varying environment; B-spline model; Structural health monitoring (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096014811930151X
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:138:y:2019:i:c:p:1166-1175
DOI: 10.1016/j.renene.2019.02.007
Access Statistics for this article
Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides
More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().