Data Processing Based on Wavelet Analysis in Structure Health Monitoring System

In order to obtain the useful information from the raw data which contain the state data reflecting the structure condition and the noise, de-noising and feature extraction techniques based on Wavelet analysis were studied. An improved wavelet thresholding algorithm to eliminate the noise for vibration signals was proposed. Comparison analysis with other thresholding algorithms shows that the new algorithm performs wave filtering well and improves the quality of noise reduction. Db5 wavelet was adopted to decompose the acceleration vibration signal acquired from the monitoring spot. Laboratory study shows that the feature vibration can be extracted successfully by reconstructing the wavelet coefficients. Comparison analysis about the original acceleration vibration signal, acceleration vibration signal with 10% and 20% damage degree shows that wavelet package can be used to detection the signal mutation and realize the structural damage alarming. The applicability of the proposed methods is demonstrated by using in two actual bridge health monitoring system.