基于形态提升小波的机械状态监测数据压缩研究

发布时间：2018-04-19 17:02

本文选题：形态滤波器 + 提升小波　；参考：《振动与冲击》2014年10期

【摘要】：针对大型复杂机电设备状态分布式监测面临的海量数据传输问题,对比研究基于提升小波与形态提升小波的振动信号数据压缩方法。对实测浮点型振动信号,利用小波稀疏分解特性提出基于提升小波变换的机械振动信号数据压缩方法,通过变换后小波系数优化组合及改进编码方法,可提高阈值处理数据压缩效果。针对实测数据含大量冗余信息缺点,结合状态监测目的提出基于形态提升小波的数据压缩方法,利用形态学滤波器非线性分析特性,在振动信号网络监测数据压缩传输过程中实现信号预处理,预制噪声干扰,较好保留有用信息。所提最大区分度准优化选择分解层数,通过对比分析两种数据压缩方法表明,形态提升小波方法具有计算量小、分析速度快、压缩比高等优点。
[Abstract]:Aiming at the problem of mass data transmission in large-scale complex electromechanical equipment condition monitoring, a method of vibration signal data compression based on lifting wavelet and morphological lifting wavelet is studied.Based on the characteristics of wavelet sparse decomposition, a data compression method for mechanical vibration signals based on lifting wavelet transform is proposed. The wavelet coefficients are optimized and the coding method is improved after the wavelet transform.The data compression effect of threshold processing can be improved.Aiming at the shortcoming of redundant information in measured data, a new data compression method based on morphological lifting wavelet is proposed for state monitoring, and the nonlinear analysis of morphological filter is used.In the process of data compression and transmission of monitoring data in vibration signal network, signal preprocessing and prefabricated noise interference are realized, and useful information is better preserved.By comparing and analyzing the two data compression methods, it is shown that the morphological lifting wavelet method has the advantages of small computation, fast analysis speed and high compression ratio.
【作者单位】：军械工程学院车辆与电气工程系;
【基金】：国家自然科学基金项目(51305454,51205405) 军队重点科研项目([20XX]X号)
【分类号】：TN911.7

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