Estimating and Leveraging Uncertainties in Deep Learning for Remaining Useful Life Prediction in Mechanical Systems

Cornelius, Jackson; Brockner, Blake; Hong, Sampyo; Wang, Yi; Pant, Kapil; Ball, John

doi:10.1109/icphm49022.2020.9187063

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…In order to estimate the epistemic uncertainty of the proposed DNNs candidate models, model ensembling and bootstrap sampling [18,63] are utilized in our approach for capturing the uncertainty inherent in the RUL prediction. This requires creating a base training set from which all samples for training and validation are included as well as a different testing subset of data for estimating generalization performance following the bootstrap technique.…”

Section: Model Uncertainty Quantificationmentioning

confidence: 99%

“…Data-driven techniques, particularly those based on artificial intelligence (AI) such as deep learning (DL) [14][15][16], have gained increasing attraction in the manufacturing industry as the industrial Internet of Things (IoT) [17] and Big Data (BD) have grown in popularity. While considerable research has been conducted on the use of deep learning techniques relative to machine health monitoring, very few studies have focused on applying deep learning to the prediction of RUL with associated uncertainties [1,12,18]. Precise RUL prediction can considerably increase industrial components or systems' reliability and operational safety [19], prevent fatal failures, and lower maintenance costs [20].…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

et al. 2021

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Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method's applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively.

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Section: Model Uncertainty Quantificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

et al. 2021

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“…Therefore, a piecewise model was used instead of a linear model to construct a degradation curve. The piecewise model was originally presented in [ 48 ], and it has been proven to be an effective method to improve the prediction performance of the model [ 49 , 50 ]. Specifically, the previous stage of the degradation curve is set to a constant and then begins to degenerate linearly.…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer

Wang

Song

2021

Computational Intelligence and Neuroscience

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The remaining useful life estimation is a key technology in prognostics and health management (PHM) systems for a new generation of aircraft engines. With the increase in massive monitoring data, it brings new opportunities to improve the prediction from the perspective of deep learning. Therefore, we propose a novel joint deep learning architecture that is composed of two main parts: the transformer encoder, which uses scaled dot-product attention to extract dependencies across distances in time series, and the temporal convolution neural network (TCNN), which is constructed to fix the insensitivity of the self-attention mechanism to local features. Both parts are jointly trained within a regression module, which implies that the proposed approach differs from traditional ensemble learning models. It is applied on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset from the Prognostics Center of Excellence at NASA Ames, and satisfactory results are obtained, especially under complex working conditions.

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A Set-Based Uncertainty Quantification of Evolving Fuzzy Models for Data-Driven Prognostics

Boutrous

Bessa

Nejjari

et al. 2022

Intelligent and Safe Computer Systems in Control and Diagnostics

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Estimating and Leveraging Uncertainties in Deep Learning for Remaining Useful Life Prediction in Mechanical Systems

Cited by 8 publications

References 17 publications

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer

A Set-Based Uncertainty Quantification of Evolving Fuzzy Models for Data-Driven Prognostics

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