Model-Agnostic Meta-Learning-based Fault Classification for Industrial Processes with Small Sample

To address the challenge of limited fault data in industrial production, this paper introduces a fault classification method based on model-agnostic meta-learning (MAML). This method synergizes the quick adaptability characteristic of MAML with the robust feature extraction capabilities of fully connected neural networks, targeting the intricate demands of feature extraction and modeling in classification tasks. MAML has attracted widespread attention in the field of machine learning due to its ability to rapidly adapt to novel tasks with a small amount of data. In this paper, we explore the application of MAML to classify industrial fault data, revealing that our approach can facilitate efficient and precise fault classification, even with extremely limited data samples. This research not only enhances the integration of MAML technology in the field of industrial fault detection but also provides a new perspective and solution for addressing similar issues of data scarcity.