Fault diagnosis method for gearbox systems using OVMD and KLIRVM

To achieve precise diagnostic outcomes for gearbox systems, this study proposes an integrated methodology combining Orthogonalized Variational Mode Decomposition (OVMD) with Kernel Learning Incremental Relevance Vector Machine (KLIRVM). Capitalizing on the inherent efficacy of Bessel basis functions in detecting abrupt transient fluctuations, OVMD achieves pristine isolation of non-stationary characteristics stemming from damaged rolling elements. A novel KLIRVM approach is introduced and formulated for gearbox fault classification, wherein adaptive kernel parameter learning is seamlessly integrated with incremental updates-enabling each basis function to dynamically adjust its position and width for accurate characterization of locally varying signal features in streaming data. Ablation studies are conducted to validate the superiority of the proposed OVMD-KLIRVM framework. Empirical findings demonstrate that this approach outperforms competing methodologies in diagnostic accuracy.