Gradient-Oriented Prioritization in Meta-Learning for Enhanced Few-Shot Fault Diagnosis in Industrial Systems

Abstract

In this paper, we propose the gradient-oriented prioritization meta-learning (GOPML) algorithm, a new approach for few-shot fault diagnosis in industrial systems. The GOPML algorithm utilizes gradient information to prioritize tasks, aiming to improve learning efficiency and diagnostic accuracy. This method contrasts with conventional techniques by considering both the magnitude and direction of gradients for task prioritization, which potentially enhances fault classification performance in scenarios with limited data. Our evaluation of GOPML’s performance across varied fault conditions and operational contexts includes extensive testing on the Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB) datasets. The results indicate a consistent level of performance across different dataset divisions, suggesting its utility in practical industrial settings. The adaptability of GOPML to specific task characteristics, particularly in environments with sparse data, represents a notable contribution to the field of meta-learning for industrial fault diagnosis. GOPML shows promise in addressing the challenges of few-shot fault diagnosis in industrial systems, contributing to the growing body of research in this area by offering an approach that balances accuracy and generalization with limited data.