Enhancing prognostics for sparse labeled data using advanced contrastive self-supervised learning with downstream integration

Author:

Deng WeikunORCID,Nguyen Khanh T.P.,Gogu Christian,Medjaher Kamal,Morio JérômeORCID

Publisher

Elsevier BV

Reference53 articles.

1. Investigating merits of deep self-supervised learning on a fatigue RUL prognostics application;Aguiar,2024

2. Self-Supervised Learning for data scarcity in a fatigue damage prognostic problem;Akrim;Eng. Appl. Artif. Intell.,2023

3. Reverse engineering self-supervised learning;Ben-Shaul,2023

4. Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials of the aerospace industry;Ben-Yelun;Eng. Struct.,2023

5. A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings;Cao;Reliab. Eng. Syst. Saf.,2021

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