کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6735829 | 1429054 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Multiple analytical mode decompositions for nonlinear system identification from forced vibration
ترجمه فارسی عنوان
تجزیه حالتهای چندگانه تحلیلی برای شناسایی سیستم غیرخطی از ارتعاش اجباری
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کلمات کلیدی
شناسایی سیستم غیر خطی، تبدیل هیلبرت، تجزیه حالت تحلیلی، فیلتر سازگار، پردازش سیگنال، ارتعاش اجباری، تحریک زلزله،
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
مهندسی ژئوتکنیک و زمین شناسی مهندسی
چکیده انگلیسی
In this study, multiple analytical mode decompositions (M-AMD) are proposed to identify the parameters of nonlinear structures from forced vibration. For the time-varying damping (or stiffness) coefficient of a weakly-to-moderately nonlinear system, the slow-varying part is first estimated from the system responses and their Hilbert transforms, which is corrected with an adaptive low-pass filter referred to as analytical mode decomposition (AMD). The fast-varying part can then be identified from the responses together with the estimated slow-varying part, which is again corrected with the AMD. The computational efficiency and accuracy of the proposed M-AMD are demonstrated with a Duffing oscillator subjected to harmonic loading. The errors in estimation of all model parameters are less than 3% from uncontaminated displacement responses, which is more accurate compared with the results from Hilbert spectral analysis. Changes of the fast-varying stiffness part have been taken into account with high accuracy. The M-AMD algorithm is then validated with a ¼-scale, 3-story building with one piezoelectric friction damper under earthquake excitations. The parameters of such a semi-active damper are identified with less than 1% error on average.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Structures - Volume 173, 15 October 2018, Pages 979-986
Journal: Engineering Structures - Volume 173, 15 October 2018, Pages 979-986
نویسندگان
Hongya Qu, Tiantian Li, Genda Chen,