Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in assembly lines, where rapid and accurate fault classification of arms is essential. Despite gaining significant traction, the framework faces significant challenges, like hypersensitivity to learning parameters and limited generalization during meta-testing. To address these challenges, we proposed an ensemble-based meta-learning approach integrating majority voting with model-agnostic meta-learning (MAML), and operational grouping was implemented via Latin hypercube sampling (LHS) to enhance few-shot learning ability and generalization along with maintaining stable output. This approach demonstrates superior accuracy in classifying a significantly larger number of defective mechanical classes, particularly in cross-domain few-shot (CDFS) learning scenarios. The proposed methodology is validated using a synthetic vibration signal dataset of robotic arm faults generated via a digital twin. Comparative analysis with existing frameworks, including ANIL, Protonet, and Reptile, confirms that our approach achieves higher accuracy in the given scenario.

Few-shot meta-learning supports rapid adaptation with limited labeled data, but remains sensitive to hyperparameters and support set quality. We propose Uncertainty-Decomposed Reliability-Gated Meta-Learning (UDRML). It selects support sets with low predictive entropy, combines diverse adapted models to estimate aleatoric and epistemic uncertainty through mutual information, and uses a reliability score to accept, flag, or reject predictions. We evaluate UDRML on cross-domain few-shot LiDAR fault classification using a synthetically augmented nuScenes dataset. At 10-shot, UDRML improves accuracy over MAML by 11.8 points. Gating provides a further 13.3-point gain on accepted predictions, while measured epistemic uncertainty is 78% lower than that of the strongest ensemble baseline, ECMP. The framework supports selective automation by accepting reliable predictions and directing uncertain cases to verification or a fallback.