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12 November 2024 Fault diagnosis of low-speed heavy-duty rolling bearings based on acoustic emission and CEEMDAN energy entropy
Gewei Lou, Jun Chen, Xuliang Liu, Chao Ma
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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 133950O (2024) https://doi.org/10.1117/12.3048840
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
To address the limitations of conventional vibration analysis in detecting faults in low-speed heavy-duty rolling bearings, this paper employs acoustic emission technology to capture fault-related information during their operation. Subsequently, the gathered acoustic emission signals are decomposed using the CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) algorithm. By incorporating the correlation coefficient and variance contribution rate, a sensitive IMF (Intrinsic Mode Function) component is selected, and its energy entropy is calculated as the extracted fault feature. This feature is then fed into a neural network for accurate fault classification. To validate the effectiveness of this approach, a simulation test platform for low-speed heavy-duty bearing detection was established. Experimental results demonstrate that this method achieves high accuracy and promising outcomes in addressing the fault identification challenges of low-speed heavy-duty rolling bearings.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Gewei Lou, Jun Chen, Xuliang Liu, and Chao Ma "Fault diagnosis of low-speed heavy-duty rolling bearings based on acoustic emission and CEEMDAN energy entropy", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 133950O (12 November 2024); https://doi.org/10.1117/12.3048840
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KEYWORDS
Acoustic emission
Neural networks
Signal processing
Correlation coefficients
Modal decomposition
Feature extraction
Signal detection
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