This paper proposes a novel fault diagnosis framework that integrates the Osprey–Cauchy–Sparrow Search Algorithm (OCSSA) optimized Variational Mode Decomposition (VMD) with a Bidirectional Temporal Convolutional Network-Attention mechanism (BiTCN-Attention). To address the limitations of empirical parameter selection in VMD, OCSSA adaptively optimizes the decomposition parameters (penalty factor α and mode number K) through a hybrid strategy that combines chaotic initialization, Osprey-inspired global search, and Cauchy mutation. Subsequently, the BiTCN captures bidirectional temporal dependencies from vibration signals, while the attention mechanism dynamically filters critical fault features, constructing an end-to-end diagnostic model. Experiments on the CWRU dataset demonstrate that the proposed method achieves an average accuracy of 99.44% across 10 fault categories, outperforming state-of-the-art models (e.g., VMD-TCN: 97.5%, CNN-BiLSTM: 84.72%).
