Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosis

TL;DR

This paper proposes a novel meta-learning approach for few-shot fault prognosis in time series data, using pseudo meta-tasks to improve feature extraction and robustness, leading to better prediction accuracy and generalization.

Contribution

It introduces a pseudo meta-task partitioning scheme and a differential algorithm to enhance feature utilization and robustness in few-shot time series fault prediction.

Findings

Significant improvement in prediction accuracy on multiple datasets.

Enhanced robustness across different datasets and conditions.

Better generalization in few-shot learning scenarios.

Abstract

Meta learning is a promising technique for solving few-shot fault prediction problems, which have attracted the attention of many researchers in recent years. Existing meta-learning methods for time series prediction, which predominantly rely on random and similarity matching-based task partitioning, face three major limitations: (1) feature exploitation inefficiency; (2) suboptimal task data allocation; and (3) limited robustness with small samples. To overcome these limitations, we introduce a novel 'pseudo meta-task' partitioning scheme that treats a continuous time period of a time series as a meta-task, composed of multiple successive short time periods. Employing continuous time series as pseudo meta-tasks allows our method to extract more comprehensive features and relationships from the data, resulting in more accurate predictions. Moreover, we introduce a differential algorithm to enhance the robustness of our method across different datasets. Through extensive experiments on several fault and time series prediction datasets, we demonstrate that our approach substantially enhances prediction performance and generalization capability under both few-shot and general conditions.