کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10281769 | 501796 | 2013 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Support vector regression for anomaly detection from measurement histories
ترجمه فارسی عنوان
رگرسیون بردار پشتیبانی برای تشخیص ناهنجاری از تاریخچه اندازه گیری
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کلمات کلیدی
تشخیص آنومالی، رگرسیون بردار پشتیبانی، پردازش سیگنال، تفسیر اطلاعات، نظارت بر سلامت سازمانی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
This research focuses on the analysis of measurements from distributed sensing of structures. The premise is that ambient temperature variations, and hence the temperature distribution across the structure, have a strong correlation with structural response and that this relationship could be exploited for anomaly detection. Specifically, this research first investigates whether support vector regression (SVR) models could be trained to capture the relationship between distributed temperature and response measurements and subsequently, if these models could be employed in an approach for anomaly detection. The study develops a methodology to generate SVR models that predict the thermal response of bridges from distributed temperature measurements, and evaluates its performance on measurement histories simulated using numerical models of a bridge girder. The potential use of these SVR models for damage detection is then studied by comparing their strain predictions with measurements collected from simulations of the bridge girder in damaged condition. Results show that SVR models that predict structural response from distributed temperature measurements could form the basis for a reliable anomaly detection methodology.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Advanced Engineering Informatics - Volume 27, Issue 4, October 2013, Pages 486-495
Journal: Advanced Engineering Informatics - Volume 27, Issue 4, October 2013, Pages 486-495
نویسندگان
Rolands Kromanis, Prakash Kripakaran,