The role of rotating machinery in industrial operations is fundamentally important, necessitating proficient maintenance strategies that are significantly dependent on accurate fault diagnosis methodologies. The present study introduces an optimized Variational Mode Decomposition (VMD) strategy intended for the analysis of vibration signals for the monitoring of this machinery. The proposed approach employs the Sine-Cosine Algorithm (SCA) to refine VMD parameters, namely, the number of modes (K), the penalty factor ( \(\alpha \) ), and the convergence tolerance ( \(\tau \) ), using an energy difference metric as a performance criterion. Using the Case Western Reserve University Bearing Data Dataset, an optimal configuration has been identified displaying a significant reduction in energy discrepancies between original signals and their decomposed components. Furthermore, an examination of the frequency content, coupled with statistical and correlation analyses, have validated the quality of the decomposition and elucidated the impact of VMD parameters on energy conservation. The synthesis of these analyses demonstrated the efficacy of the proposed methodology in the precise selection of VMD parameters for the analysis of vibration signals of faulty machinery components.