摘要
针对复杂装备状态监测所面临的海量数据采样与传输问题,提出一种基于提升小波的自适应压缩感知方法。针对方法中提升小波信号处理中最优参数确定问题,利用稀疏度作为控制因子对提升小波滤波器和分解层数进行优选,并结合分块阈值降噪方法实现对机械振动信号的最佳稀疏分解。基于分块压缩感知的思想和满足RIP条件下观测次数下限的指导原则,解决提升小波分解各节点信号观测数据量的确定问题,构建基于提升小波的自适应压缩感知的机械状态监测体系。研究结果表明:该方法能够有效地减少压缩感知观测数据量,提高信号的重构速度和重构质量。
In order to solve the problem of mass data acquisition and transmission of complicated equipment condition monitoring, a method of adaptive compressed sensing of machinery vibration based on lifting wavelet transform was put forward. Taking account of optimal parameters selection problem of lifting wavelet signal process, the lifting wavelet filter and layer numbers were optimally chosen by considering the factor of sparseness, then combined with block threshold noise reduction method to implement the sparseness decomposition of machinery vibration signal. Based on the block compressed sensing and the least quantity of measured data, which satisfy the restricted isometry property, the problem of measured data quantity of node signal based on lifting wavelet transform was solved, and the machine condition monitoring system of adaptive compressed sensing based on lifting wavelet transform was built. The results show that the proposed method can reduce the quantity of measured data and enhance the reconstruction speed and performance.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2016年第3期771-776,共6页
Journal of Central South University:Science and Technology
基金
国家自然科学基金资助项目(51305454)~~
关键词
自适应压缩感知
稀疏度
提升小波
数据压缩
机械振动
状态监测
adaptive compressed sensing
sparseness
lifting wavelet transform
data compression
machinery vibration
condition monitoring
