Few-shot remaining useful life prediction based on meta-learning with deep sparse kernel network

计算机科学 机器学习 人工智能 推论 过度拟合 预言 核(代数) 数据挖掘 高斯过程 人工神经网络 高斯分布 数学 量子力学 组合数学 物理
作者
Jing Yang,Xiaomin Wang,Zhipeng Luo
出处
期刊:Information Sciences [Elsevier BV]
卷期号:653: 119795-119795 被引量:21
标识
DOI:10.1016/j.ins.2023.119795
摘要

Predicting remaining useful life (RUL) of machinery is of vital importance to prognostics and health management. Reliable and accurate RUL prediction not only can reduce maintenance costs and increase machine availability but also even prevent catastrophic consequences. In reality, RUL predictions usually require numerous certain kinds of machine degradation data. However, complex operating conditions and safety issues may often result in fragmented data records generated, with very few complete samples being usable. To overcome the challenge of RUL prediction with limited data, this paper proposes a novel MetaDESK model that is based on meta-learning with deep sparse kernel network. The general idea is to train a sparse kernel with a variational posterior in a data-driven fashion, and then transfer it to a new few-shot RUL task via meta-knowledge. Specifically, we first incorporate a Gaussian Process into the model-agnostic meta-learning (MAML) framework and use variational inference to estimate latent variables as kernel features, which allows us to sample from a non-Gaussian distribution of the posterior. Then, the KL-divergence of sparse approximation is added to the kernel features as a regularization term through inference to reduce the overfitting problem. Also, to exploit the dependencies of the tasks we integrate both their shared knowledge and task-specific information into a contextual reasoning process, which is implemented by a bidirectional long short-term memory network. To evaluate our proposed model, we conduct extensive experiments using publicly available degradation data, and the results verify the model's effectiveness.
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