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Fault Diagnosis of Rolling Bearing Based on an Improved Denoising Technique Using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method

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The vibration signals captured for rolling bearing are generally polluted by excessive noise and can lose the fault information at the early development phase. Therefore, denoising is required to enhance the signal quality by improving kurtosis parameter sensitivity and envelope spectrum performance for early fault detection.


In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using the CEEMD algorithm. The IMFs grouping and selection are formed based upon the approximate entropy and correlation coefficient value. After IMFs selection, the noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction.


The effectiveness of the proposed method denoised signals are tested on two experimental datasets based on kurtosis value and the envelope spectrum analysis. The results on the first dataset shows significant improvement in the kurtosis parameter values such as 92.99, 90.92, 97.39, and 78.35 for inner race fault in the bearing and 113.1, 170, 195.1, and 197.4 for outer race fault in the bearing rotating at four different speeds of 1797, 1772, 1750, and 1730 rpm, respectively, and enhances the amplitude of envelope spectrum to easily detect the bearing fault characteristics frequencies. The developed methods are further tested on the second dataset yielding similar improvement in kurtosis value and envelope spectrum analysis.


The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing compared to the original and other two conventional approaches.

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Availability of Data and Material

The data and material that support the findings of this study are openly available in the data repository of Case Western Reserve University and the Society for Machinery Failure Prevention Technology.

Code Availability

Not applicable.


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PKS: conceptualization, methodology, coding, writing—original draft, investigation, validation. RNR: data curation, visualization, supervision, writing—review and editing.

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Correspondence to Prashant Kumar Sahu.

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Sahu, P.K., Rai, R.N. Fault Diagnosis of Rolling Bearing Based on an Improved Denoising Technique Using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method. J. Vib. Eng. Technol. 11, 513–535 (2023).

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