Bearing fault identification method based on CEEMD and RF
Pages 74 - 78
Abstract
Traditional fault diagnosis methods often rely on experienced engineers for signal analysis, which is not only time-consuming but also susceptible to subjective factors. In order to improve the accuracy and efficiency of fault diagnosis, this paper proposes a bearing fault diagnosis method based on Complementary Ensemble Empirical Mode Decomposition and Random Forest algorithms. This method takes the vibration signal of the bearing as the original signal, decomposes the original signal using the CEEMD method, and calculates the fuzzy entropy of each component signal to form a bearing state feature vector. These features are used to train a random forest classifier to identify different bearing states. This article uses publicly available bearing datasets as experimental data to verify the feasibility and superiority of the proposed method. The experimental results show that this method has good diagnostic results, with a diagnostic rate of 98.75%, which has practical guidance significance for bearing fault diagnosis.
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Index Terms
- Bearing fault identification method based on CEEMD and RF
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Published In
July 2024
179 pages
ISBN:9798400718038
DOI:10.1145/3688268
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Published: 18 December 2024
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ICCCM 2024: 2024 the 12th International Conference on Computer and Communications Management (ICCCM)
July 19 - 21, 2024
Kagoshima-shi, Japan
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