کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4924023 1430824 2017 22 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
On multi-site damage identification using single-site training data
ترجمه فارسی عنوان
در شناسایی آسیب چند سایت با استفاده از داده های آموزشی تک سایت
کلمات کلیدی
نظارت بر سلامت سازمانی، طبقه بندی بردار پشتیبانی، شناسایی آسیب چند سایت، تشخیص الگو،
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی عمران و سازه
چکیده انگلیسی

This paper proposes a methodology for developing multi-site damage location systems for engineering structures that can be trained using single-site damaged state data only. The methodology involves training a sequence of binary classifiers based upon single-site damage data and combining the developed classifiers into a robust multi-class damage locator. In this way, the multi-site damage identification problem may be decomposed into a sequence of binary decisions. In this paper Support Vector Classifiers are adopted as the means of making these binary decisions. The proposed methodology represents an advancement on the state of the art in the field of multi-site damage identification which require either: (1) full damaged state data from single- and multi-site damage cases or (2) the development of a physics-based model to make multi-site model predictions. The potential benefit of the proposed methodology is that a significantly reduced number of recorded damage states may be required in order to train a multi-site damage locator without recourse to physics-based model predictions. In this paper it is first demonstrated that Support Vector Classification represents an appropriate approach to the multi-site damage location problem, with methods for combining binary classifiers discussed. Next, the proposed methodology is demonstrated and evaluated through application to a real engineering structure - a Piper Tomahawk trainer aircraft wing - with its performance compared to classifiers trained using the full damaged-state dataset.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Sound and Vibration - Volume 409, 24 November 2017, Pages 43-64
نویسندگان
, , ,