Application Study Of Wavelet Analysis Used In Bridge Health Monitoring System

Signal de-noising in bridge health monitoring system is in an important position, only by means of a good signal de-noising to effectively monitor the removal of the signal in the noise, and could well retain the desired signal characteristics, in order to bridge health monitoring provides an important information and the basis of the correct data.Bridge health monitoring is a complicated system, are faced with complex and changing environment. Monitoring the signal is very complex, often contain many mutations shaped peak or non-stationary components, the traditional Fourier transform of this signal de-noising can not. In recent years, wavelet analysis developed new signal processing tools, it is considered a breakthrough method of Fourier analysis, the signal has a strong time-frequency domain analysis. Wavelet analysis theory, can distinguish signal and noise in the mutant part, in order to achieve the noise reduction. The measured signal is decomposed, no complex algorithms, only the deep processing of the data collected can express the characteristics of multi-scale, multi-scale analysis from the point of view to study the test signal.This paper describes the composition of bridge health monitoring system and function as well as the basics of wavelet analysis theory. Second, under the bridge to monitor signal and the characteristics of wavelet transform features, wavelet transform modulus maxima de-noising and wavelet threshold de-noising the signal, and compares three different threshold de-noising results. Then the design of new wavelet lifting scheme and based on the average threshold of wavelet packet de-noising. Wavelet analysis is an extension of wavelet analysis the signal out of a more detailed analysis and reconstruction methods, he not only low-frequency part of the decomposition, but also made a second high-frequency part of the decomposition, making it the signal to noise shown a clear advantage of the signal stronger. Finally, the experimental data using a series of simulation experiments to verify the above method of de-noising results and effectiveness.