Compressive sampling system based on random demodulation for active and passive structural health monitoring

Structural health monitoring (SHM) is a revolutionary and innovative technique for determining structural integrity. It has broad application prospects in industrial equipment and infrastructure such as aircrafts, bridges and buildings. SHM system is used to monitor and diagnose the damages of these large structures, and it requires to install large sensor networks in or on large structures such as aircraft wings and airframes. The large number of sensors results in a very large amount of data for excited and received signals, especially for ultrasonic guided wave-based SHM with high frequency signals. According to the traditional Nyquist sampling theorem, the sampling rate is at least 2 times higher than the highest frequency of the received signal. The high sampling rate and the large amount of data pose a serious challenge on the signal acquisition, transmission, storage, and processing equipment, especially in the case that the signal needs to be transmitted to the base station for processing in real time. Therefore, how to compress and sample signals obtained by large sensor networks to improve monitoring efficiency and reduce costs is a research hotspot in current SHM. This paper adopts compressive sensing (CS) theory which has appeared in recent years to solve the big data and high frequency acquirement problem of SHM. The random demodulation system is chosen to realize CS, and the corresponding hardware and software systems are designed, which are achieved to compress and sample signal and reduce the sampling rate and the amount of data at the same time. The active and passive SHM signals based on piezoelectric sensor networks are compressed and sampled, and the new method are compared with the traditional Nyquist sampling in the experiment. The results show the effectiveness of the proposed method.