小波轉換在橋梁非破壞檢測之應用

Fault Diagnosis of Bridge Structure using Wavelet Transform

吳智偉(Zhi-Wei Wu)

指導教授 ： 王安培

中原大學/工學院/土木工程研究所/碩士(2001年)

https://doi.org/10.6840/cycu200100506

摘要

由橋梁之振動訊號可以反應出橋梁之訊息。但是在實際情況下，這些振動訊號包含了雜訊，橋梁的特徵訊號往往會被雜訊覆蓋，因此很難辨別訊號的改變。為了獲得橋梁正確的訊息，我們必須將雜訊濾除。本文的目的即在消除雜訊，摘取橋梁之特徵訊號。因為傅立葉轉換無法知道訊號改變發生之時間，小波轉換具有良好的時間與頻率的解析度，改善傅立葉轉換在時域與頻域分析的局限性，所以使用小波分析來分析非穩態訊號。本文利用連續小波轉換於雜訊消除及特徵擷取，選取適當的小波基底，利用小波轉換係數，我們可以摘取橋梁之特徵訊號。有了橋梁之特徵訊號，我們便可以依據這些特徵訊號來診斷橋樑。

關鍵字

小波 ； 雜訊濾除 ； 特徵摘取

並列摘要

Vibration of the bridge always carries information about its function. But in general, the vibration contains many noises. In order to obtain correct information, the background noise must be removed or the vibration must be purified. A de-noising method is given in this paper and is successfully used in feature vibration extraction. We present a method on wavelet transform to eliminate the noise. It is difficult to detect an instantaneous frequency spectrum throughout the sampling period by the FFT. Since the FFT provides an averaged frequency by spectrum of the total sampling period, the CWT can be used for discovering the signal components, using a proper basic wavelet. We can obtain the feature components of a signal by reconstructing the wavelet coefficients. This method is used for extracting the vibration of the bridge with different types of failure. The feature vibration is extracted successfully.

並列關鍵字

De-noising ； Feature extraction ； Wavelet

參考文獻

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