@ARTICLE{Lakikza_Abderrahmane_Optimized_2024,
 author={Lakikza, Abderrahmane and Cheghib, Hocine and Kahoul, Nabil},
 volume={Articles accepted},
 journal={Archive of Mechanical Engineering},
 howpublished={online},
 year={2024},
 publisher={Polish Academy of Sciences, Committee on Machine Building},
 abstract={This research presents an enhanced methodology for diagnosing bearing faults using Variational Mode Decomposition (VMD) based on L-Kurtosis analysis. The proposed method focuses on selecting optimal parameters for VMD to extract the mode containing the most information related to the fault. The selection of these parameters is based on comparing the energy ratio of each mode and the absolute difference in L-Kurtosis between the Intrinsic Mode Function (IMF) with the highest energy and the original signal. The extracted mode is further refined using a specified kurtosis rate threshold to ensure the most relevant significant modes are captured. The proposed methodology was tested using real fault data from the CWRU, XJTU-SY, and a real-world wind turbine dataset related to electric motors and wind turbine systems. The results demonstrated high accuracy in fault detection compared to other methods such as the Gini Index, correlation, and traditional decomposition techniques like EMD. Furthermore, due to the simple computational nature of the improved VMD method, it is faster and more efficient compared to methods that rely on complex calculations or frequency band analysis, making it suitable for applications requiring real-time, reliable fault diagnosis},
 type={Article accepted},
 title={Optimized variational mode decomposition for improved bearing fault diagnosis and performance evaluation},
 URL={http://journals.pan.pl/Content/133074/PDF/AME_2024_4_2.pdf},
 doi={10.24425/ame.2024.152615},
 keywords={bearing fault, variational mode decomposition, L-Kurtosis, energy ratio, diagnosis},
}