基于故障丰富度指标的稀疏信号分解方法  

Sparse signal decomposition method based on fault richness index

作　　者：关金发[1] 马力炜 周申申 贺王鹏 王宇 Guan Jinfa;Ma Liwei;Zhou Shenshen;He Wangpeng;Wang Yu(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 611756,China;School of Mechanical Engineering,Xi′an Jiaotong University,Xi′an 710049,China;School of Aerospace Science and Technology,Xidian University,Xi′an 710071,China)

机构地区：[1]西南交通大学电气工程学院,成都611756 [2]西安交通大学机械工程学院,西安710049 [3]西安电子科技大学空间科学与技术学院,西安710071

出　　处：《仪器仪表学报》2025年第1期270-284,共15页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(52472421)项目资助。

摘　　要：针对船载天线传动系统中存在的复杂振动信号问题,提出了一种基于K-SVD字典学习的稀疏信号分解方法。船载天线传动系统在实际运行中面临着多变和复杂的环境条件,这些条件导致振动信号具有高度的非线性和非平稳性,从而增加了故障诊断和健康监测的难度。考虑到传统参数字典难以匹配多样化的振动信号特征,首先引入了基于频率加权能量算子的故障丰富度指标,用以量化信号中的故障信息。接着,通过互补集成经验模式分解技术对信号进行降噪预处理,提高了在高噪声环境下K-SVD算法的信号重构精度。详细描述了CEEMD在实际信号处理中的应用步骤,并通过实验数据验证了其在高噪声环境下的降噪效果,进一步提高了K-SVD算法的信号重构精度。此外,还采用基于故障丰富度指标的敏感分量选取方法,确保恢复信号在降噪过程中保留尽可能多的有效故障信息。进一步,使用K-SVD算法对信号进行二次分解,并通过一种新颖的字典初始化方式增强字典原子的故障特征表达能力,从而提高算法的运行效率和故障特征提取精度。最后,通过仿真和实验验证了所提出方法的有效性和精确性。使用真实船载天线的振动数据进行测试,结果显示,该方法能够显著提高故障特征的提取精度和可靠性,为船载天线传动系统的健康监测和故障诊断提供了有力支持。A sparse signal decomposition method based on K-SVD dictionary learning is proposed to address the issue of complex vibration signals in shipborne antenna transmission systems.These systems face variable and complex environmental conditions during actual operation,leading to highly nonlinear and non-stationary vibration signals,which increase the difficulty of fault diagnosis and health monitoring.Given the difficulty of traditional parameter dictionaries in matching the diverse characteristics of vibration signals,this paper first introduces a fault richness index based on the frequency-weighted energy operator to quantify fault information in signals.Subsequently,the complementary ensemble empirical mode decomposition(CEEMD)technique is employed for signal denoising preprocessing,enhancing the signal reconstruction accuracy of the K-SVD algorithm in high-noise environments.We detail the application steps of CEEMD in practical signal processing and verify its denoising effect in high-noise environments through experimental data,further improving the signal reconstruction accuracy of the K-SVD algorithm.Additionally,this paper utilizes a sensitive component selection method based on the fault richness index to ensure that the recovered signal retains as much effective fault information as possible during the denoising process.Furthermore,the K-SVD algorithm is applied for secondary signal decomposition,and a novel dictionary initialization method is used to enhance the fault feature expression ability of dictionary atoms,thereby improving the algorithm′s operational efficiency and fault feature extraction accuracy.Finally,the effectiveness and accuracy of the proposed method are validated through simulations and experiments.The results indicate that the proposed method significantly enhances the accuracy and reliability of fault feature extraction,providing strong support for health monitoring and fault diagnosis of shipborne antenna transmission systems.0x09 A sparse signal decomposition method based on K-SVD

关 键 词：船载天线传动机构 滚动轴承 稀疏信号分解 故障诊断 

分 类 号：TH133.33[机械工程—机械制造及自动化]