| Structural health monitoring(SHM)is a revolutionary and innovative technique for determining structural integrity.The structure is monitored in real time and online by the sensor networks that generating and receiving diagnostic signals.Then the structural damage can be found,located and evaluated in time.SHM has important implications for the lightweight design of structures,as well as the improvement of structural safety and lower maintenance costs.It has broad application prospects in industrial equipment and infrastructure such as aircrafts,bridges and buildings.It is required to install large sensor network on large area structure such as aircraft wings and airframes,when SHM is used to monitor and diagnose the damages of these large structures.The large number of sensors results in a very large amount of data for excitation and received signals,especially for ultrasonic guided wave-based structural health monitoring through excitation and received signals with the high frequency.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 sensing 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 theory which has appeared in recent years to solve the compression sampling problem of structural health monitoring signals.The Random Demodulation system is chosen to realize the Compressive Sensing,and the corresponding hardware and software systems are designed,which achieved to compress and sample signal and reduce the sampling rate and the amount of data at the same time.The main research contents and results of this thesis are as follows:(1)The characteristics of structural health monitoring signals and the principle of random demodulation were studied.The MATLAB simulation of random demodulation structure was used to compress the sampled health monitoring signals.The influence of filter parameters,compression ratio and signal length on the reconstruction effect were explored,which provides the theoretical reference for the design of Random Demodulation system and experiment.(2)A random demodulation experiment system platform that can be applied to structural health monitoring is established.The experimental system includes both hardware and software aspects.The hardware system focuses on the design of a random demodulation system,which integrates the acquisition card and the signal generation card,so that the hardware can achieve SHM and Random Demodulation functions.The software includes the synchronization control of each hardware module and signal reconstruction algorithm,which can generate the required signals,collect,reconstruct,and store signals.(3)The random demodulation system is embedded into existing SHM,and the active and passive SHM signals based on piezoelectric sensor networks are compressed and sampled.The compressed sampled SHM signals and the signals sampled by the traditional Nyquist sampling theorem are compared.The results show that in the active monitoring,the phase and amplitude difference between the reference and damage signals reconstructed with the sampling rate and data amount of 10 times compression are not significantly different from the phase and amplitude differences between the uncompressed sampling reference and the damage signal,and the trend is consistent with the frequency changes.In the passive experiment,the signal reconstructed with the sampling rate and data amount of 10 times compression has a small difference from the uncompressed sampled signal,and the average SNR of 9 experiments is 16.38 dB. |
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