[1]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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Fault diagnosis using improved sliding coarsening and integrated fluctuation-based dispersion entropy

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 2 Page number: 363-375 Column: 学术论文—机器学习 Public date: 2025-03-05
Title:
Fault diagnosis using improved sliding coarsening and integrated fluctuation-based dispersion entropy
Author(s):
MU Lingxia1; 3;  TIAN Lu1;  FENG Nan2;  WANG Hongxin1;  ZHANG Jian1;  WU Shihai1; 3;  LIU Ding1; 3
1. School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China;
2. School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China;
3. Crystal Growth Equipm
Keywords:
sliding coarsening; sequence reconstruction; fault diagnosis; fault classification; integrated fluctuation-based dispersion entropy; rolling bearing; vibration signal; feature extraction
CLC:
TP273
DOI:
10.11992/tis.202401013
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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