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Volume 8 Issue 6
Jun.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Chuan Sun, Hongpeng Yin, Yanxia Li and Yi Chai, "A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning," IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1188-1198, June 2021. doi: 10.1109/JAS.2020.1003438
Citation: Chuan Sun, Hongpeng Yin, Yanxia Li and Yi Chai, "A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning," IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1188-1198, June 2021. doi: 10.1109/JAS.2020.1003438

A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning

doi: 10.1109/JAS.2020.1003438
Funds:  This work was supported by the National Natural Science Foundation of China (61773080, 61633005), the Fundamental Research Funds for the Central Universities (2019CDYGZD001), and Scientific Reserve Talent Programs of Chongqing University (cqu2018CDHB1B04)
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  • The localized faults of rolling bearings can be diagnosed by its vibration impulsive signals. However, it is always a challenge to extract the impulsive feature under background noise and non-stationary conditions. This paper investigates impulsive signals detection of a single-point defect rolling bearing and presents a novel data-driven detection approach based on dictionary learning. To overcome the effects harmonic and noise components, we propose an autoregressive-minimum entropy deconvolution model to separate harmonic and deconvolve the effect of the transmission path. To address the shortcomings of conventional sparse representation under the changeable operation environment, we propose an approach that combines K-clustering with singular value decomposition (K-SVD) and split-Bregman to extract impulsive components precisely. Via experiments on synthetic signals and real run-to-failure signals, the excellent performance for different impulsive signals detection verifies the effectiveness and robustness of the proposed approach. Meanwhile, a comparison with the state-of-the-art methods is illustrated, which shows that the proposed approach can provide more accurate detected impulsive signals.

     

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    Highlights

    • A novel data-driven detection approach based on dictionary learning is proposed to detect rolling bearing vibration impulsive signal under background noise and nonstationary conditions.
    • An AR algorithm based linear prediction is used to estimate frequencies and phases of harmonics, which can efficiently separate harmonic components.
    • An improved MED model is used to weaken Gaussian noise without destroying the inner structure of impulsive signals, which improves the quality of the training dataset to build an appropriate dictionary.
    • A novel adaptive parameter estimation SB algorithm is proposed to solve the problem that L1-norm underestimates the amplitude of impulses, which accurately extracts the impulsive signal.

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