Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problem

• URL: http://arxiv.org/abs/2301.08441v1
• Date: Fri, 20 Jan 2023 06:45:32 GMT
• Title: Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problem
• Authors: Anass Akrim, Christian Gogu, Rob Vingerhoeds, Michel Sala\"un
• Abstract summary: Self-Supervised Learning is a sub-category of unsupervised learning approaches. This paper investigates whether pre-training DL models in a self-supervised way on unlabelled sensors data can be useful for Remaining Useful Life (RUL) estimation. Results show that the self-supervised pre-trained models are able to significantly outperform the non-pre-trained models in downstream RUL prediction task.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing availability of data for Prognostics and Health Management (PHM), Deep Learning (DL) techniques are now the subject of considerable attention for this application, often achieving more accurate Remaining Useful Life (RUL) predictions. However, one of the major challenges for DL techniques resides in the difficulty of obtaining large amounts of labelled data on industrial systems. To overcome this lack of labelled data, an emerging learning technique is considered in our work: Self-Supervised Learning, a sub-category of unsupervised learning approaches. This paper aims to investigate whether pre-training DL models in a self-supervised way on unlabelled sensors data can be useful for RUL estimation with only Few-Shots Learning, i.e. with scarce labelled data. In this research, a fatigue damage prognostics problem is addressed, through the estimation of the RUL of aluminum alloy panels (typical of aerospace structures) subject to fatigue cracks from strain gauge data. Synthetic datasets composed of strain data are used allowing to extensively investigate the influence of the dataset size on the predictive performance. Results show that the self-supervised pre-trained models are able to significantly outperform the non-pre-trained models in downstream RUL prediction task, and with less computational expense, showing promising results in prognostic tasks when only limited labelled data is available.

Related papers

• Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning [28.35038726318893]
  Machine learning models trained on vast amounts of real or synthetic data often achieve outstanding predictive performance across various domains. To address privacy concerns, machine unlearning has been proposed to erase specific data samples from models. We introduce the Unlearning Usability Attack to distill data distribution information into a small set of benign data.
  arXiv  Detail & Related papers  (2024-07-06T15:42:28Z)
• Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models [89.88010750772413]
  Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs) Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
  arXiv  Detail & Related papers  (2024-06-18T08:38:59Z)
• VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability Estimation [0.0]
  This work introduces VIRL, a Volume-Informed Representation Learning approach to pre-train a 3D geometric encoder. The model pre-trained by VIRL shows substantial enhancements on demonstrating improved generalizability with limited data.
  arXiv  Detail & Related papers  (2024-06-18T05:30:26Z)
• Probing Language Models for Pre-training Data Detection [11.37731401086372]
  We propose to utilize the probing technique for pre-training data detection by examining the model's internal activations. Our method is simple and effective and leads to more trustworthy pre-training data detection.
  arXiv  Detail & Related papers  (2024-06-03T13:58:04Z)
• Low-rank finetuning for LLMs: A fairness perspective [54.13240282850982]
  Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models. This paper investigates the effectiveness of these methods in capturing the shift of fine-tuning datasets from the initial pre-trained data distribution. We show that low-rank fine-tuning inadvertently preserves undesirable biases and toxic behaviors.
  arXiv  Detail & Related papers  (2024-05-28T20:43:53Z)
• Machine Learning Based Missing Values Imputation in Categorical Datasets [2.5611256859404983]
  This research looked into the use of machine learning algorithms to fill in the gaps in categorical datasets. The emphasis was on ensemble models constructed using the Error Correction Output Codes framework. Deep learning for missing data imputation has obstacles despite these encouraging results, including the requirement for large amounts of labeled data.
  arXiv  Detail & Related papers  (2023-06-10T03:29:48Z)
• To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis [50.31589712761807]
  Large language models (LLMs) are notoriously token-hungry during pre-training, and high-quality text data on the web is approaching its scaling limit for LLMs. We investigate the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting. Second, we examine the key factors contributing to multi-epoch degradation, finding that significant factors include dataset size, model parameters, and training objectives.
  arXiv  Detail & Related papers  (2023-05-22T17:02:15Z)
• Temporal Output Discrepancy for Loss Estimation-based Active Learning [65.93767110342502]
  We present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. Our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2022-12-20T19:29:37Z)
• Improving the Adversarial Robustness of NLP Models by Information Bottleneck [112.44039792098579]
  Non-robust features can be easily manipulated by adversaries to fool NLP models. In this study, we explore the feasibility of capturing task-specific robust features, while eliminating the non-robust ones by using the information bottleneck theory. We show that the models trained with our information bottleneck-based method are able to achieve a significant improvement in robust accuracy.
  arXiv  Detail & Related papers  (2022-06-11T12:12:20Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.