Wavelet-based AR–SVM for health monitoring of smart structures
| Autor: | Jo Woon Chong, Yeesock Kim, JungMi Kim, Ki H. Chon |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Discrete wavelet transform
Engineering business.industry Speech recognition Pattern recognition Condensed Matter Physics Atomic and Molecular Physics and Optics Support vector machine Wavelet Autoregressive model Mechanics of Materials Feature (computer vision) Signal Processing Magnetorheological fluid General Materials Science Structural health monitoring Artificial intelligence Electrical and Electronic Engineering business Civil and Structural Engineering Building automation |
| Zdroj: | Smart Materials and Structures. 22:015003 |
| ISSN: | 1361-665X 0964-1726 |
| Popis: | This paper proposes a novel structural health monitoring framework for damage detection of smart structures. The framework is developed through the integration of the discrete wavelet transform, an autoregressive (AR) model, damage-sensitive features, and a support vector machine (SVM). The steps of the method are the following: (1) the wavelet-based AR (WAR) model estimates vibration signals obtained from both the undamaged and damaged smart structures under a variety of random signals; (2) a new damage-sensitive feature is formulated in terms of the AR parameters estimated from the structural velocity responses; and then (3) the SVM is applied to each group of damaged and undamaged data sets in order to optimally separate them into either damaged or healthy groups. To demonstrate the effectiveness of the proposed structural health monitoring framework, a three-story smart building equipped with a magnetorheological (MR) damper under artificial earthquake signals is studied. It is shown from the simulation that the proposed health monitoring scheme is effective in detecting damage of the smart structures in an efficient way. |
| Databáze: | OpenAIRE |
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