融合阈值法的贝叶斯压缩感知振动信号重构

Vibration Responses Reconstruction Based on Bayesian Compressed Sensing Combined with Threshold Method

针对结构健康监测因海量数据导致的数据传输和储存压力大,以及实际振动信号受外界环境噪声影响导致稀疏性较差的问题,提出融合阈值法的贝叶斯压缩感知振动信号重构算法。引入贝叶斯压缩感知理论对数据进行压缩采样,融合阈值法增强信号的稀疏度以改善后续信号重构的精度,然后用快速超参数估计法估计稀疏向量,以高精度和高概率地恢复原始信号。利用江西省吉安大桥的环境振动试验数据验证融合阈值法的贝叶斯压缩感知振动信号重构算法的有效性和可行性。研究结果表明,使用阈值法的贝叶斯压缩感知信号重构算法精度要优于未使用算法重构的振动信号得到的结果,尤其是高压缩比的情况下。

Big data transmission and storage cost caused by massive data collected and poor sparsity of actual vibration signals polluted by external environmental noise are critical problems in structural health monitoring.Thus,vibration responses reconstruction based on Bayesian compressed sensing combined with threshold method is proposed in this paper.The Bayesian compressed sensing theory is introduced in this paper to reduce the sampling,and the threshold method is used to enhance the sparsity of the collected signals to improve the accuracy of responses reconstruction.Then the fast hyper-parameter estimation method is utilized to estimate the sparse vectors to reconstruct the original signals with high accuracy and probability.The effectiveness and feasibility of the Bayesian compressed sensing vibration signal reconstruction algorithm combined with the threshold method were verified using ambient vibration responses from the Ji'an Bridge of Jiangxi Province.The reconstructed results indicate that the accuracy of vibration signal reconstruction using the proposed method is superior to that of Bayesian compressed sensing reconstruction without using threshold method.