Compressive Sampling Based on Wavelet Analysis for Lamb Wave Signals in Wireless Structural Health Monitoring

Abstract

In the Wireless Sensor Networks (WSNs) for structural health monitoring (SHM), data compression is often used to reduce the cost of data transfer and storage, because of the large amounts of original data acquired from the monitoring system. Traditionally, we firstly sample the full signal and then compress it. However, the traditional approach for data compression will cause a lot of computing resources and energy loss on sensor nodes. Recently, a new data compression method named compressive sampling (CS) which acquires data in compressed form directly by using special sensors has been presented. In this work, we established a suitability CS approach for lamb wave signals in wireless SHM. For reconstruction of the signal, different wavelet orthogonal bases are examined. The lamb wave data acquired from the SHM system of LF- 21M aviation antirust aluminum plate is used to analyze the data compression ability of CS. Through the experimental demonstration, the application of this method could ensure the accuracy of the data as well as balance the network energy consumption. And it can also reduce the cost of data storage and transmission.