基于改进CEEMD的薄层污垢超声检测信号去噪  

Thin fouling ultrasonic detection signal denoising based on improved CEEMD

作　　者：孙灵芳[1] 王彤彤 徐曼菲[2] 李霞[2] 朴亨[2] 

机构地区：[1]东北电力大学节能与测控技术工程实验室,长春132012 [2]东北电力大学自动化工程学院,长春132012

出　　处：《仪器仪表学报》2017年第12期2879-2887,共9页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(51176028);吉林市科技创新发展计划(20166007)项目资助

摘　　要：对换热管污垢回波振型特征有效提取是实现污垢厚度定量检测的关键。针对薄层污垢回波声束能量不集中,易产生模态混叠等特点,提出一种基于改进CEEMD的小波收缩阈值信号处理方法。首先引入夹角余弦计算原始信号与固有模态函数相似程度,判断信号和噪声主导模态分界点,并结合能量密度谱判断分界点选取准确性,然后利用小波收缩阈值方法拾取噪声主导模态中的细节信息,最后重构得到降噪后信号。仿真和实验结果表明:该方法分界点判断准确性较高,去噪效果优于传统小波阈值方法,数值模拟与实验结果一致,对薄层污垢回波振型特征提取有重要指导意义。Effective feature extraction of the heat exchange tube fouling signal is the essential step for fouling thickness detection. In view of the echo energy decentralizing and model aliasing, a signal processing method based on CEEMD wavelet adaptive threshold is proposed. Firstly, the similarity between intrinsic mode function （IMF） and original signal is calculated by the angle cosine method. The signal and noise mode segmentation point is determined and evaluated combining with the energy spectrum. Besides, the wavelet adaptive threshold is used to collect detail information in noise modes. Finally, all of the remained IMFs are reconstructed to obtain a noise suppressed signal. The results show the accuracy of segmentation point is high. Improved CEEMD has better de-noising performance than wavelet threshold. The numerical simulation matches the test resuhs, proved that the proposed method is significant to extracting the feature of the thin fouling signal.

关 键 词：完备总体经验模态分解 夹角余弦 薄层污垢 数值模拟 实验检测 

分 类 号：TB551[理学—物理] TN911.71[理学—声学]