@ARTICLE{Bousseloub_Yahia_An_2025,
 author={Bousseloub, Yahia and Medjani, Farida and Benmassoud, Ahmed and Benamira, Nadir and Belhamra, Ali},
 volume={vol. 72},
 number={No 1},
 pages={105-130},
 journal={Archive of Mechanical Engineering},
 howpublished={online},
 year={2025},
 publisher={Polish Academy of Sciences, Committee on Machine Building},
 abstract={The early detection of bearing faults is critical for ensuring the reliability and performance of electromechanical systems. Vibration signals provide valuable insights into fault characteristics. However, effectively extracting fault-related features remains challenging due to issues like over-decomposition and mode mixing in traditional signal processing methods such as Empirical Mode Decomposition (EMD). To address these challenges, this paper proposes an optimized Empirical Mode Decomposition (OEMD) technique enhanced by cross-correlation (CC) and root mean square (RMS) statistical analysis. The proposed method introduces three novel correlation-based stopping criteria to ensure the independence of Intrinsic Mode Functions (IMFs) in the decomposition result. Furthermore, an RMS-based selection strategy is implemented to identify optimal IMFs that retain fault-related information. The proposed approach is validated using real-world vibration signals from two datasets: an experimental bearing vibration dataset and a public dataset from the Case Western Reserve University (CWRU). The results highlight the feasibility and effectiveness of the method in accurately and automatically selecting the optimal Intrinsic Mode Functions (IMFs) containing high-amplitude peaks at defect characteristic frequencies. These findings demonstrate the robustness of the proposed method in both fault detection and identification.},
 title={An optimized EMD method based on cross correlation and root mean square statistical analysis and its application to bearing faults detection},
 type={Article},
 URL={http://ochroma.man.poznan.pl/Content/134198/PDF/AME_153736.pdf},
 doi={10.24425/ame.2025.153736},
 keywords={vibration signal, signal processing, bearing fault detection, empirical mode decomposition (EMD), root mean square (RMS)},
}