Self-Supervised Representation Learning for Remaining Useful Life Prediction: A Taxonomy and Triplet-Loss Case Study
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Abstract
Remaining useful life (RUL) prediction is fundamentally constrained by the scarcity of labeled run-to-failure data, motivating growing use of self-supervised learning (SSL) to exploit abundant unlabeled condition monitoring data. However, the literature lacks a systematic comparison of the loss functions underlying these SSL methods, as existing surveys organize the field by architecture rather than by the training objective itself. This paper reviews self-supervised loss functions for RUL prediction, spanning temporal-order, contrastive, metric-learning, multi-view, and meta-learning objectives, summarizing each family's assumptions, requirements, and limitations. To ground this review empirically, we present a framework pre-training a Transformer encoder with a triplet margin loss and evaluating it under few-shot fine-tuning on the C-MAPSS turbofan benchmark. Across 2 to 50 labeled fine-tuning sequences, the pre-trained model consistently outperforms an identically structured supervised baseline, with the gap narrowing as labeled data becomes abundant, confirming that SSL pre-training is most valuable precisely in the label-scarce regime.
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RUL Prediction, Self-Supervised Learning, Prognostics, Triplet, Representation Learning
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