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Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

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Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis. / Huo, Zhiqiang ; Zhang, Yu; Jombo, Gbanaibolou; Shu, Lei .

In: IEEE Access, Vol. 8, 8, 06.05.2020, p. 87529-87540.

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Huo, Zhiqiang ; Zhang, Yu ; Jombo, Gbanaibolou ; Shu, Lei . / Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis. In: IEEE Access. 2020 ; Vol. 8. pp. 87529-87540.

Bibtex

@article{484096ad9637401388ddb41682bbcdeb,
title = "Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis",
abstract = "Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.",
author = "Zhiqiang Huo and Yu Zhang and Gbanaibolou Jombo and Lei Shu",
note = "{\textcopyright} 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.",
year = "2020",
month = may,
day = "6",
doi = "10.1109/ACCESS.2020.2992935",
language = "English",
volume = "8",
pages = "87529--87540",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "IEEE",

}

RIS

TY - JOUR

T1 - Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

AU - Huo, Zhiqiang

AU - Zhang, Yu

AU - Jombo, Gbanaibolou

AU - Shu, Lei

N1 - © 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

PY - 2020/5/6

Y1 - 2020/5/6

N2 - Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.

AB - Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.

U2 - 10.1109/ACCESS.2020.2992935

DO - 10.1109/ACCESS.2020.2992935

M3 - Article

VL - 8

SP - 87529

EP - 87540

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 8

ER -