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Fault diagnosis of rolling bearings in non-stationary running conditions using improved CEEMDAN and multivariate denoising based on wavelet and principal component analyses

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Abstract

This paper presents a fault diagnosis method for rolling bearings working in non-stationary running conditions. The proposed approach is based on an improved version of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the multivariate denoising using wavelet analysis and principal component analysis (PCA), the spectral kurtosis, and the order tracking analysis (OTA). The results show that the improved CEEMDAN has completely decomposed the raw signal into different intrinsic mode functions (IMFs) representing the natural oscillatory modes embedded into the signal. The most relevant IMF from which the defect was extracted is selected by the kurtogram plot which allows locating the optimal frequency band having the highest kurtosis value. Multivariate denoising based on wavelet analysis and PCA is used to increase the signal-to-noise ratio (SNR) of the selected IMF. The results show the great contribution of the denoising approach when comparing the selected denoised IMF with the original one. Finally, order tracking analysis is applied on the denoised IMF’s envelope to remove the effect of speed variation, and an envelope order spectrum is obtained. The proposed approach is first applied on theoretical signal simulating rolling bearing defect in variable regime including three different phases. The final order spectrum shows exactly the simulated defect order and several of its harmonics. For the experimental validation, several signals of defective rolling bearings have been measured on the Machine Fault Simulator test rig in variable regime. Despite the combined variable regime including acceleration-constant regime-deceleration, at the same time, the obtained results indicate the efficiency of the proposed method to extract the fault order with high accuracy. The maximum error between the theoretical order and the experimentally obtained one was 1.3% for outer race defect and 1% for inner race defect. Finally, the performances of the proposed method are compared to those of another diagnosis method designed for variable regime conditions. Both outer race and inner race defects are considered in acceleration regime. The results show the superiority of the proposed method to highlight the defect order with highest clarity.

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Authors and Affiliations

  1. Laboratory of Electrical Engineering of Guelma, May 8, 1945 University of Guelma, Guelma, Algeria

    Lilia Chaabi & Ahcene Lemzadmi

  2. Laboratory of Mechanics and Structures, May 8, 1945 University of Guelma, Guelma, Algeria

    Abderrazek Djebala, Mohamed Lamine Bouhalais & Nouredine Ouelaa

Authors
  1. Lilia Chaabi
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  2. Ahcene Lemzadmi
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  3. Abderrazek Djebala
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  4. Mohamed Lamine Bouhalais
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  5. Nouredine Ouelaa
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Correspondence to Abderrazek Djebala.

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Chaabi, L., Lemzadmi, A., Djebala, A. et al. Fault diagnosis of rolling bearings in non-stationary running conditions using improved CEEMDAN and multivariate denoising based on wavelet and principal component analyses. Int J Adv Manuf Technol 107, 3859–3873 (2020). https://doi.org/10.1007/s00170-020-05311-z

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  • DOI: https://doi.org/10.1007/s00170-020-05311-z

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