Siamese Network-Based Health Representation Learning and Robust Reference-Based Remaining Useful Life Prediction

Abstract

In many real-world prognostics and health management tasks, where the available training samples are insufficient, deep neural networks are highly vulnerable to overfitting. To address this problem, in this article, we propose a novel health representation learning method based on a Siamese network. This method prevents overfitting by utilizing a constraint by which the differences between samples in the embedding space of the Siamese network should follow the differences in the remaining useful life (RUL) values via the introduction of a multitask learning scheme. In addition, since the learned embedding space reflects the dynamics of degradation, each training sample can be used as a reference to estimate the RUL of a test sample. By combining the estimates for all training samples, the proposed method enables robust RUL prediction. Experimental results show that the proposed learning and estimation method contributes to improving not only RUL prediction performance but also robustness to data insufficiency.

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