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Keywords = NEITD-ADTL-JS

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20 pages, 10227 KB  
Article
A Novel Rolling Bearing Fault Diagnosis Method Based on the NEITD-ADTL-JS Algorithm
by Shi Zhuo, Xiaofeng Bai, Junlong Han, Jianpeng Ma, Bojun Sun, Chengwei Li and Liwei Zhan
Sensors 2025, 25(3), 873; https://doi.org/10.3390/s25030873 - 31 Jan 2025
Viewed by 884
Abstract
This paper proposes an innovative bearing fault diagnosis method aimed at enhancing the accuracy and effectiveness of transfer learning. The innovation lies in the signal preprocessing stage, where a Noise Eliminated Intrinsic Time-Scale Decomposition (NEITD) algorithm is introduced. This algorithm adaptively decomposes unified-phase [...] Read more.
This paper proposes an innovative bearing fault diagnosis method aimed at enhancing the accuracy and effectiveness of transfer learning. The innovation lies in the signal preprocessing stage, where a Noise Eliminated Intrinsic Time-Scale Decomposition (NEITD) algorithm is introduced. This algorithm adaptively decomposes unified-phase sine wave signals to effectively extract the geometric mean of the intrinsic rotational component, and selects the optimal decomposition result based on the orthogonality index, significantly improving the quality and reliability of the signals. In addition, fault diagnosis parameters are adaptively optimized using an improved adaptive deep transfer learning (ADTL) network combined with the Jellyfish Search (JS) algorithm, further enhancing diagnostic performance. By innovatively combining signal noise reduction, feature extraction, and deep learning optimization techniques, this method significantly improves fault diagnosis accuracy and robustness. Comparative simulations and experimental analyses show that the NEITD algorithm outperforms traditional methods in both signal decomposition performance and diagnostic accuracy. Furthermore, the NEITD-ADTL-JS method demonstrates stronger sensitivity and recognition capabilities across various fault types, achieving a 5.29% improvement in accuracy. Full article
(This article belongs to the Special Issue Fault Diagnosis Based on Sensing and Control Systems)
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