Mamba-attention: A self-supervised framework for efficient remaining useful life prediction

Jihun Han, Yejin Kwon and Hyunsoo Yoon

Reliability Engineering and System Safety, 2026, vol. 265, issue PB

Abstract: Accurate remaining useful life (RUL) prediction provides significant economic, technical, and safety benefits by forecasting the lifespans of industrial equipment. Current state-of-the-art (SOTA) RUL prediction models predominantly rely on recurrent neural networks (RNNs) and attention-based models. While RNNs are adept at capturing temporal dependencies, they struggle with issues such as vanishing gradients and exhibit inefficiency in processing long sequences. Meanwhile, attention-based models improve the capture of global context but are limited by their high computational complexity. The proposed Mamba-Attention model addresses these limitations by integrating a selective state space model with attention mechanisms, achieving SOTA performance with faster training times on C-MAPSS dataset. Industrial equipment is frequently replaced prematurely, and obtaining labeled data is challenging due to cost and time constraints. To overcome these difficulties, this research introduces a novel framework that leverages unlabeled data via self-supervised learning approaches, including temporal loss, N-tuplet loss, and pseudo-label loss. By pre-training with self-supervised losses and fine-tuning on minimal labeled data in few-shot scenarios, the proposed approach remains effective in producing accurate RUL predictions, even under limited supervision, as demonstrated on the C-MAPSS and CALCE datasets. This reduces the reliance on costly labeled datasets and offers practical solutions for industrial applications.

Keywords: Remaining useful life; State space model; Mamba; Attention; Self-supervised learning (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:265:y:2026:i:pb:s0951832025006921

DOI: 10.1016/j.ress.2025.111492

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