Self-Supervised Learning for data scarcity in a fatigue damage prognostic problem

Akrim, Anass; Gogu, Christian; Vingerhoeds, Rob A.; Salaün, Michel

doi:10.1016/j.engappai.2023.105837

Cited by 18 publications

(5 citation statements)

References 64 publications

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“…The models are then able to produce state-of-the-art results on various downstream tasks by simply training a single layer on top of the pre-trained network for the specific task. Attempts at usage of the paradigm for different time series condition-based tasks are as reported in References 36,46,47. In Reference 36, the pretext task aims to reconstruct data upon masking of some portions using an auto-encoder, before eventual usage for remaining useful life prediction of a machine tool. In References 47,48, contrastive approaches are used for the pretext task whereby similar data samples are grouped closer together whereas diverse ones further apart with the aid of a similarity metric for distance measurement.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…The models are then able to produce state‐of‐the‐art results on various downstream tasks by simply training a single layer on top of the pre‐trained network for the specific task. Attempts at usage of the paradigm for different time series condition‐based tasks are as reported in References 36,46,47. In Reference 36, the pretext task aims to reconstruct data upon masking of some portions using an auto‐encoder, before eventual usage for remaining useful life prediction of a machine tool.…”

Section: Introductionmentioning

confidence: 99%

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Generative and self‐supervised ensemble modeling for multivariate tool wear monitoring

Joanes Agung',

James,

Samuel

et al. 2023

Engineering Reports

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Development of an effective tool wear monitor requires maximum utilization of information from associated data, especially in machine learning based modeling. However, vastly varied annotated training data is required, which is not only expensive but impractical to obtain. In the present work, a contiguous approach of artificial data generation followed up by self‐supervised pre‐training before supervised model fine tuning and final stacked generalized ensembling, has been adopted to develop an effective tool wear monitor in a low data regime. Cross‐validated results of proposed methodology adoption in tool wear prediction on an experimental data set of few labeled samples attained an averaged MAE of 0.035, RMSE of 0.045 and MAPE of on the best case ensemble, which was comparatively superior to a purely supervised‐only trained deep model on the same data set, with an overall accuracy enhancement of over . The proposed approach provides an effective experimental data augmentation technique while simultaneously minimizing aleatoric uncertainty and allowing for utilization of information from often ignored static cutting parameters.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generative and self‐supervised ensemble modeling for multivariate tool wear monitoring

Joanes Agung',

James,

Samuel

et al. 2023

Engineering Reports

View full text Add to dashboard Cite

show abstract

“…The crucial rationale for these feasible models includes their significant classification potential in circumstances involving therapeutic diagnosis, maintenance, and production line prognostics. As these two processes formulate a prominent activity in medicine and engineering, the adoption of ML and DL models could contribute to numerous advantages and productivity records [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts

Abdulrazzaq,

Ramaha,

Hameed

et al. 2024

Mathematics

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Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in different disciplines. However, the literature does not fully address SSL’s practicalities and workabilities necessary for industrial engineering and medicine. Accordingly, this thorough review is administered to identify these prominent possibilities for prediction, focusing on industrial and medical fields. This extensive survey, with its pivotal outcomes, could support industrial engineers and medical personnel in efficiently predicting machinery faults and patients’ ailments without referring to traditional numerical models that require massive computational budgets, time, storage, and effort for data annotation. Additionally, the review’s numerous addressed ideas could encourage industry and healthcare actors to take SSL principles into an agile application to achieve precise maintenance prognostics and illness diagnosis with remarkable levels of accuracy and feasibility, simulating functional human thinking and cognition without compromising prediction efficacy.

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“…Deep learning can reach an accurate data representation. However, it is not applicable in the few-shot learning case [26]. Meta-learning is proposed to improve the performance of deep learning with fewer training samples.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of a Planetary Gearbox Based on Meta Representation Learning and Adaptive Fractional Generalized Pareto Motion

Zheng,

Deng,

Song

et al. 2023

Fractal Fract

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The remaining useful life (RUL) prediction of wind turbine planetary gearboxes is crucial for the reliable operation of new energy power systems. However, the interpretability of the current RUL prediction models is not satisfactory. To this end, a multi-stage RUL prediction model is proposed in this work, with an interpretable metric-based feature selection algorithm. In the proposed model, the advantages of neural networks and long-range-dependent stochastic processes are combined. In the offline training stage, a general representation of the degradation trend is learned with the meta-long short-term memory neural network (meta-LSTM) model. The inevitable measurement error in the sensor reading is modelled by white Gaussian noise. During the online RUL prediction stage, fractional generalized Pareto motion (fGPm) with an adaptive diffusion is employed to model the stochasticity of the planetary gearbox degradation. In the case study, real planetary gearbox degradation data are used for the model validation.

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Self-Supervised Learning for data scarcity in a fatigue damage prognostic problem

Cited by 18 publications

References 64 publications

Generative and self‐supervised ensemble modeling for multivariate tool wear monitoring

Generative and self‐supervised ensemble modeling for multivariate tool wear monitoring

Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts

Remaining Useful Life Prediction of a Planetary Gearbox Based on Meta Representation Learning and Adaptive Fractional Generalized Pareto Motion

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