高速数据采集系统中基于EMD的异常信号捕获方法研究  

Research on the anomaly detection method based on EMD in the high-speed data acquisition system

作　　者：李承阳[1] 田书林[1] 杨扩军[1] 叶芃[1] 赵禹[1] Li Chengyang;Tian Shulin;Yang Kuojun;Ye Peng;Zhao Yu(University of Electronic Science and Technology of China,Chengdu 611731,China)

机构地区：[1]电子科技大学,成都611731

出　　处：《仪器仪表学报》2024年第12期98-106,共9页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(62371097,62201125)项目资助。

摘　　要：高速采集系统面对数十GSa/s的高速数据时,由于实时处理速度的限制无法实现对偶发异常信号的实时检测,导致信号遗漏。传统异常捕获方法依赖于信号特征的先验知识,对特征不明确、形态不固定的异常信号,捕获效率较低。因此,提出基于经验模态分解(EMD)的实时异常检测方法,以提高系统对异常信号的捕获能力。首先基于沿特征提取特征点并作为EMD分解起始点,以降低异常检测复杂度。其次,根据EMD分解得到的非噪声固有模态函数重构正常信号模板,并基于待测信号与正常信号模板的匹配程度进行异常检测。最后,在硬件实现并行EMD以提高异常捕获效率。通过对调制信号中异常信号检测,本方法实时异常捕获率达95%,比传统捕获方法有明显的提高。When high-speed acquisition systems handle data streams reaching tens of GSa/s,the limitations of real-time processing speed prevent the system from detecting occasional abnormal signals in real time,leading to signal omissions.Traditional anomaly detection relies on the a priori characteristics of the signal.However,these methods have low capture efficiency for episodic abnormal signals with unclear characteristics and irregular morphology.Thus,this article proposes a real-time anomaly detection method using empirical mode decomposition(EMD)to improve the system′s ability to capture anomalous signals.Firstly,the feature point extracted based on edge features is used as the start point of EMD,reducing the complexity of anomaly detection.Secondly,the non-noise intrinsic mode functions obtained from the EMD are used to reconstruct the normal signal template,and anomaly detection is carried out based on the degree of match between the test signal and the normal signal template.Finally,a parallel EMD is implemented in hardware to improve anomaly detection efficiency.By detecting anomalies in the modulated signals,the real-time anomaly capture rate of the proposed method is 95%,which represents a significant improvement over the traditional method.

关 键 词：异常检测 经验模态分解 高速数据采集系统 模板匹配 并行EMD 

分 类 号：TH7[机械工程—仪器科学与技术]