用于提取微弱特征以识别轴承缺陷的非参数集合经验模态分解
Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects
AI总结：
针对轴承缺陷微弱特征识别问题，提出无需预设噪声理想信噪比和集合数量的NPCEEMD方法，经仿真验证模态混叠更低，结合互信息选IMF和包络谱实现缺陷检测。
摘要：
本文提出了一种非参数互补集合经验模态分解（NPCEEMD）方法，用于利用微弱特征识别轴承缺陷。NPCEEMD之所以是非参数的，是因为与集合经验模态分解等现有分解方法不同，它在每次处理信号时无需定义噪声的理想信噪比和集合数量。仿真结果表明，NPCEEMD的模态混叠程度低于现有分解方法。经过深入仿真分析后，该方法被应用于实验数据。所提出的NPCEEMD方法按以下步骤运行：首先获取原始信号；其次对获取的信号进行分解；然后计算原始信号与NPCEEMD生成的本征模函数（IMF）的互信息（MI）；进一步选取MI高于0.1的IMF并组合形成结果信号；最后计算结果信号的包络谱以确认缺陷的存在。
英文摘要：
A non-parametric complementary ensemble empirical mode decomposition (NPCEEMD) is proposed for identifying bearing defects using weak features. NPCEEMD is non-parametric because, unlike existing decomposition methods such as ensemble empirical mode decomposition, it does not require defining the ideal SNR of noise and the number of ensembles, every time while processing the signals. The simulation results show that mode mixing in NPCEEMD is less than the existing decomposition methods. After conducting in-depth simulation analysis, the proposed method is applied to experimental data. The proposed NPCEEMD method works in following steps. First raw signal is obtained. Second, the obtained signal is decomposed. Then, the mutual information (MI) of the raw signal with NPCEEMD-generated IMFs is computed. Further IMFs with MI above 0.1 are selected and combined to form a resulting signal. Finally, envelope spectrum of resulting signal is computed to confirm the presence of defect.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
