Improving the adaptability of digital instrument data prediction and fault diagnosis using meta-learning

With the development of industrial automation, equipment fault diagnosis has become increasingly important for ensuring production safety and improving work efficiency. Existing fault diagnosis methods primarily rely on traditional rule-based systems and machine learning techniques. However, these methods face challenges in terms of accuracy and generalization ability when dealing with complex fault types and high-dimensional data. To address these challenges, this paper proposes a fault diagnosis method based on meta-learning. By leveraging the ability to learn from a small number of samples, this method enhances the model’s adaptability to new fault types and effectively solves issues related to data scarcity and class imbalance. The proposed approach combines deep learning and transfer learning techniques, optimizing the training strategy of the model to improve classification accuracy and robustness for multi-class faults. Experimental results on multiple fault diagnosis datasets demonstrate that the proposed method outperforms traditional approaches in diagnostic accuracy and model adaptability, with strong practical application value. This method not only offers a new perspective for fault diagnosis but also provides technical support for intelligent monitoring and fault prediction of industrial equipment.