Digital twin-driven few-shot fault data generation and intelligent diagnosis for rolling bearings

In the context of limited data availability under few-shot learning conditions, we propose an innovative fault data generation and intelligent diagnosis method for rolling bearings driven by a Digital Twin (DT) framework. To address limited fault samples, a Convolution-Bi-directional Long Short-Term Memory (Conv-BiLSTM) network integrates virtual and real-world signals to generate simulated vibration signals. Time-frequency characteristics are leveraged to reconstruct diverse fault scenarios for inner rings, outer rings, and rolling elements from a single fault signal, enriching the dataset. a Secretary Bird Optimisation Algorithm (SBOA) optimises a Kolmogorov-Arnold Network (KAN) for intelligent diagnosis using the generated data. Experiments show our method outperforms traditional approaches, achieving 97.9% accuracy. Validated on two datasets, it demonstrates robustness and practicality for few-shot fault diagnosis.