计算机与数字工程2025，Vol.53Issue(7)：1800-1807,8.DOI:10.3969/j.issn.1672-9722.2025.07.003
基于排列熵的CEEMDAN分解算法研究
Research on CEEMDAN Decomposition Algorithm Based on Permutation Entropy
摘要
Abstract
Complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method has good decomposi-tion ability for complex signals.However,there are still mode confusion and too many pseudo components in CEEMDAN decomposi-tion results.To solve the above problems,this paper proposes a Permutation entropy based complete ensemble empirical mode de-composition with adaptive noise method(PECEEMDAN).This method embeds permutation entropy threshold detection in CEEM-DAN decomposition of the original signal,separates intermittent and noise signals,and directly performs empirical mode decomposi-tion(EMD)on the remaining signals.The experimental results of simulation signals and measured bearing signals verify that PECEEMDAN has better advantages in suppressing mode confusion,decomposition accuracy and fault feature frequency extraction.关键词
噪声/模态混淆/模式分解/排列熵/故障特征Key words
noise/mode mixing/mode decomposition/permutation entropy/fault characteristics分类
信息技术与安全科学引用本文复制引用
邱林江,花小朋,徐森,孙鹏..基于排列熵的CEEMDAN分解算法研究[J].计算机与数字工程,2025,53(7):1800-1807,8.基金项目
国家自然科学基金项目面上项目(编号:62076215)资助. （编号:62076215）
