基于特征模态分解及多尺度模糊散布熵的滚动轴承故障诊断
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安徽理工大学电气与信息工程学院

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TH133

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Fault diagnosis of rolling bearings based on feature mode decomposition and multiscale fuzzy dispersion entropy
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Electrical and Information Engineering College, Anhui University of Science and Technology

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    摘要:

    针对复杂环境下的滚动轴承故障信息有效提取与辨识问题,提出一种基于特征模态分解(feature mode decomposition, FMD)及多尺度模糊散布熵(multiscale fuzzy dispersion entropy, MFDE)和斑马优化算法(zebra optimization algorithm, ZOA)优化支持向量机的滚动轴承故障诊断方法。为了解决FMD中关键参数不具有自适应性这一问题,使用以最小包络熵为目标函数,采用白鲸优化算法(whale optimization algorithm, BWO)优化FMD寻找最优参数组合,实现对故障信号的最优分解;引入多尺度模糊散布熵构建分解后不同模态下的特征向量;最后,将特征向量输入到支持向量机中进行训练和识别,通过公开数据集和自制实验平台数据集验证了提出方法的有效性。

    Abstract:

    Aiming at the problem of effective extraction and identification of rolling bearing fault information in complex environments, a fault diagnosis method for rolling bearings based on feature mode decomposition (FMD) combined with multiscale fuzzy dispersion entropy (MFDE) and zebra optimization algorithm (ZOA) optimization support vector machine was proposed. In order to solve the problem that the key parameters in FMD are not adaptive, the minimum envelope entropy is used as the objective function, and the whale optimization algorithm (BWO) is used to optimize FMD to find the optimal parameter combination to achieve the optimal decomposition of fault signals. Multiscale fuzzy dispersion entropy is introduced to construct the eigenvectors under different modes after decomposition. Finally, the feature vectors were input into the support vector machine for training and recognition. The effectiveness of the proposed method is verified by the public dataset and the self-made experimental platform dataset.

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梁翔宇,胡业林,马向阳,等. 基于特征模态分解及多尺度模糊散布熵的滚动轴承故障诊断[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-03-11
  • 最后修改日期:2024-05-09
  • 录用日期:2024-05-21
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