mlsanityData to 14 Oct 2026

Improving Semi-Supervised Learning for Remaining Useful Lifetime Estimation Through Self-Supervision

arXiv 2108.08721 ↗First posted 19 Aug 2021cs.LGcited by 3

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Abstract (arXiv)

RUL estimation suffers from a server data imbalance where data from machines near their end of life is rare. Additionally, the data produced by a machine can only be labeled after the machine failed. Semi-Supervised Learning (SSL) can incorporate the unlabeled data produced by machines that did not yet fail. Previous work on SSL evaluated their approaches under unrealistic conditions where the data near failure was still available. Even so, only moderate improvements were made. This paper proposes a novel SSL approach based on self-supervised pre-training. The method can outperform two competing approaches from the literature and a supervised baseline under realistic conditions on the NASA C-MAPSS dataset.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 32 references that are in the map. First posted on arXiv 19 Aug 2021.

Builds on

13 of 32 references.

References · 13

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1 more
  1. 2017Semi-supervised Learning with Deep Generative Models for Asset Failure Predictioncited by 5

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.

  1. 2022Masked Self-Supervision for Remaining Useful Lifetime Prediction in Machine Toolscited by 4
  2. 2023Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problemcited by 3
  3. 2024The State of Lithium-Ion Battery Health Prognostics in the CPS Eracited by 0