| 刘鹏,王佳庆,柳江,刘碧龙.基于改进BOMP的微弱故障特征增强显化方法[J].电子测量与仪器学报,2026,40(4):40-50 |
| 基于改进BOMP的微弱故障特征增强显化方法 |
| Weak fault feature enhancement and manifestation methodbased on improved BOMP |
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| DOI： |
| 中文关键词: 早期微弱故障诊断 信号重构 BOMP算法 字典学习 |
| 英文关键词:early weak fault diagnosis signal reconstruction BOMP algorithm dictionary learning |
| 基金项目:国家自然科学基金(62501348)、国家自然科学基金(62401312)、兵团科技计划项目(2025AB026)、山东省高校青年创新科技支持计划(2024KJH024)、湖南省自然科学基金(2024116462)、
山东省自然科学基金（ZR2025MS987）、工业流体节能与污染控制教育部重点实验室开放基金(CK-2024-0040)项目资助 |
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| Author | Institution |
| Liu Peng | School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525,China |
| Wang Jiaqing | School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525,China |
| Liu Jiang | School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525,China |
| Liu Bilong | School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525,China |
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| 中文摘要: |
| 为克服传统模态分解方法在强噪环境下容易导致故障特征能量衰减与模态混叠的问题，稀疏重构理论被引入轴承故障诊断领域。该类方法通过在过完备字典中寻找与信号结构最匹配的原子组合，实现了对微弱故障冲击的精确逼近，从机理上保证了特征能量的完整性。贝叶斯正交匹配追踪算法（Bayesian orthogonal matching pursuit, BOMP）在弱特征重构与辨识方面展现出优势。然而，现有方法在迭代过程中通常会完全剔除贡献度低于预设阈值的原子，导致这些原子中蕴含的微弱但重要的故障特征能量被丢弃，从而影响信号重构精度和降噪效果。针对此问题，提出一种改进的贝叶斯正交匹配追踪模型（improved Bayesian orthogonal matching pursuit, IBOMP）。其核心改进在于优化了支持集原子选择的判断阈值策略，旨在最大限度保留低贡献原子中包含的微弱故障特征能量。与正交匹配追踪算法（orthogonal matching pursuit, OMP）、贝叶斯正交匹配追踪算法等经典稀疏重构算法的对比实验表明，所提IBOMP 方法能更有效地抑制噪声干扰并增强故障特征。基于南京航空航天大学智能诊断与专家系统研究室的轴承故障数据集验证，相较于OMP与BOMP算法，能显著提升轴承滚动体和外圈故障特征频率能量占比—分别提升22.71%、22.73%和46.22%、46.52%。 |
| 英文摘要: |
| To overcome the issues of energy attenuation in fault features and modal overlap that traditional modal decomposition methods often encounter in high-noise environments, sparse reconstruction theory has been introduced into the field of bearing fault diagnosis. These methods achieve precise approximation of weak fault impacts by identifying the atomic combinations that best match the signal structure within an overcomplete dictionary, thereby mechanistically ensuring the integrity of feature energy. The Bayesian orthogonal matching pursuit (BOMP) algorithm demonstrates advantages in reconstructing and identifying weak features. However, existing methods typically eliminate atoms with contributions below a preset threshold during iteration, discarding the faint yet crucial fault feature energy contained within these atoms. This compromises signal reconstruction accuracy and noise reduction effectiveness. To address this issue, this paper proposes an improved Bayesian orthogonal matching pursuit (IBOMP) model. Its core enhancement lies in optimizing the decision threshold strategy for support set atom selection, aiming to maximize the retention of faint fault feature energy contained within low-contribution atoms. Comparative experiments against classical sparse reconstruction algorithms—including orthogonal matching pursuit (OMP) and Bayesian orthogonal matching pursuit (BOMP)—demonstrate that the proposed IBOMP method more effectively suppresses noise interference and enhances fault feature signals. Validation using the bearing fault dataset from the intelligent diagnosis and expert system laboratory at Nanjing university of aeronautics and astronautics demonstrates that compared to OMP and BOMP algorithms, the proposed IBOMP significantly enhances the frequency energy contribution of bearing rolling element and outer ring fault features—by 22.71%, 22.73%, 46.22%, and 46.52%, respectively. |
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