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

TL;DR

This paper introduces a self-supervised semi-supervised learning method for remaining useful lifetime estimation that outperforms existing approaches under realistic data conditions, addressing data imbalance issues.

Contribution

The paper proposes a novel self-supervised pre-training approach for semi-supervised RUL estimation, improving performance over prior methods and baselines.

Findings

Outperforms competing SSL approaches on NASA C-MAPSS dataset

Effective in realistic data scenarios with limited failure data

Enhances RUL prediction accuracy

Abstract

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.