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

Fault prediction in time series data is a vital machine learning task with extensive industrial applications, yet it faces challenges such as data scarcity and frequency mismatch. Meta-learning has emerged as a promising approach to address these issues, leveraging cross-task similarities and differences to effectively adapt to novel time series fault prediction tasks. It empowers deep learning models to rapidly adjust to new time series data with few or even no samples, capitalizing on the similarities and differences among time series data from various domains and scenarios to enhance generalization capabilities([35], [2]). Meta-learning enables a machine learning algorithm to'learn to learn', enhancing the universality and adaptability of knowledge. In the realm of time series fault prediction, the efficacy of meta-learning hinges on the nuanced calibration of several task-distribution-dependent factors, of which researchers identify four key aspects: data representation, meta-learner design, meta-learning algorithms, and pseudo meta-task division. It's noteworthy that the first three aspects require different adjustments based on the specific task distribution, whereas the division of pseudo meta-tasks is not dependent on task distribution [24]. Therefore, to enhance the adaptability of meta-learning in fault prediction, this paper primarily refines the division method of pseudo meta-tasks.