[1]穆凌霞,田璐,冯楠,等.改进滑动粗粒化和集成波动色散熵的故障诊断方法[J].智能系统学报,2025,20(2):363-375.[doi:10.11992/tis.202401013]
MU Lingxia,TIAN Lu,FENG Nan,et al.Fault diagnosis using improved sliding coarsening and integrated fluctuation-based dispersion entropy[J].CAAI Transactions on Intelligent Systems,2025,20(2):363-375.[doi:10.11992/tis.202401013]
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MU Lingxia,TIAN Lu,FENG Nan,et al.Fault diagnosis using improved sliding coarsening and integrated fluctuation-based dispersion entropy[J].CAAI Transactions on Intelligent Systems,2025,20(2):363-375.[doi:10.11992/tis.202401013]
改进滑动粗粒化和集成波动色散熵的故障诊断方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第2期
页码:
363-375
栏目:
学术论文—机器学习
出版日期:
2025-03-05
- Title:
- Fault diagnosis using improved sliding coarsening and integrated fluctuation-based dispersion entropy
- Keywords:
- sliding coarsening; sequence reconstruction; fault diagnosis; fault classification; integrated fluctuation-based dispersion entropy; rolling bearing; vibration signal; feature extraction
- 分类号:
- TP273
- 摘要:
- 在多尺度波动色散熵中，多尺度粗粒化会忽略重构子序列部分相邻点之间的信息，并且子序列长度随着尺度因子的增大长度减小，这种特征提取的方法不利于故障分类。为了解决这个问题，本文提出了一种n次滑动粗粒化的方法，在确定的比例因子下，利用n次滑动方法保留了每点之间的信息，保证重构后的序列与重构前的序列长度一致。针对波动色散熵中映射技术过于单一的问题，本文利用集成波动色散熵对重构后的序列进行特征提取，使得熵计算更加准确。用西储大学等轴承数据集对算法进行了验证，所提方法的故障诊断精确度显著提高。
- Abstract:
- In multiscale fluctuation-based dispersion entropy, multiscale coarse graining loses the information between adjacent points in the reconstructed subsequences. Additionally, the length decreases as the scale factor increases, and the features extracted through this coarse-grained method are not conducive to fault classification. To address this problem, this paper proposes a method of n steps sliding. This method ensures that the information between points is preserved under the given scale factor, maintaining the length of the reconstructed sequence to be consistent with the original sequence. Aiming at the problem that the mapping technology in the fluctuation dispersion entropy is too simple, integrated dispersion entropy is used to extract features from the reconstructed sequence, enhancing the accuracy of entropy calculations. The algorithm is verified using bearing datasets from Case Western Reserve University and other institutions, the proposed method notably improves fault diagnosis accuracy.
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备注/Memo
收稿日期:2024-1-9。
基金项目:国家自然科学基金项目(62373299, 62127809); 陕西省重点研发计划项目(2024GX-YBXM-093); 中国博士后科学基金项目(2022MD723834); 陕西省科协青年人才托举计划项目(20210114).
作者简介:穆凌霞，副教授，主要研究方向为故障诊断与容错控制。主持/参与国家自然科学基金项目5 项，主持省部级项目、企业委托技术开发项目7项，获国家发明专利授权9项，发表学术论文 30 余篇。E-mail：mulingxia@xaut.edu.cn;田璐，硕士研究生，主要研究方向为故障诊断。E-mail：tianlu@stu.xaut.edu.cn;冯楠，工程师，主要研究方向为工业系统故障诊断与健康管理。参与国家级、省部级项目4项，获国家发明专利授权10余项，发表学术论文10余篇。E-mail：fengnan@ustb.edu.cn。
通讯作者:冯楠. E-mail：fengnan@ustb.edu.cn
更新日期/Last Update:
2025-03-05