稀疏度拟合的自适应机械振动信号压缩感知

针对利用机械振动信号进行设备故障诊断和状态监测过程中，存在采样数据量多、存储容量大、传输带宽高和信号重构精度低等问题，提出一种稀疏度拟合的自适应机械振动信号压缩感知方法。首先，对机械振动信号进行多尺度小波包变换，再将小波包系数按一定阈值进行置零处理并求取其稀疏度；然后，采用迭代方法求取各稀疏度下满足重构信号精度条件的最低采样率，并对信号的稀疏度和采样率采用最小二乘法进行拟合，消除信号测量误差，求取最佳信号采样率；最后，采用K-奇异值分解算法构造与各信号块相适应的过完备字典，并利用正交匹配追踪算法实现信号重构。实验证明，与传统压缩算法相比较，该算法的信号压缩率和重构精度均得到较大提高。

Adaptive Compressed Sensing of Mechanical Vibration Signals Based on Sparsity Fitting

The fault diagnosis and state monitoring of the mechanical vibration signal often struggle with large amount of sampled data, large storage capacity, high transmission bandwidth and low signal reconstruction accuracy. In light of this problem, an adaptive compressed sensing of mechanical vibration signals based on sparsity fitting method is proposed. First, the multi-scale wavelet packet transform is carried out on the mechanical vibration signal, and its sparsity is obtained by zeroing the wavelet packet coefficient at a certain threshold value. Then, the iterative method is adopted to obtain the minimum sampling rate that meets the requirements of reconstruction signal accuracy under each sparsity degree, and the sparsity degree and sampling rate are fitted with the least square method to eliminate the signal measurement error for an optimal signal sampling rate. Finally, an over-complete dictionary adapted to each signal block is constructed by K-singular value decomposition algorithm, and the signals are reconstructed by orthogonal matching pursuit algorithm. Experiments show that the signal compression rate and reconstruction accuracy of this algorithm are greatly improved compared with the traditional compression algorithm.