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Convolutional neural networks for automated damage recognition and damage type identification

Structural control and health monitoring, 2018-10, Vol.25 (10), p.e2230-n/a

2018

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Multi-unit nuclear power plant probabilistic risk assessment: A comprehensive survey

Reliability engineering & system safety, 2021-09, Vol.213, p.107782, Article 107782

2021

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Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Shock and vibration, 2017-01, Vol.2017 (2017), p.1-17

2017

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Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks

Sensors (Basel, Switzerland), 2021-09, Vol.21 (17), p.5888

2021

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Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management

Sensors (Basel, Switzerland), 2021-10, Vol.21 (20), p.6841

2021

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An improved multi-unit nuclear plant seismic probabilistic risk assessment approach

Reliability engineering & system safety, 2018-03, Vol.171, p.34-47

2018

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Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems

Reliability engineering & system safety, 2022-06, Vol.222, p.108433, Article 108433

2022

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Operation scheduling in a solar thermal system: A reinforcement learning-based framework

Applied energy, 2020-06, Vol.268, p.114943, Article 114943

2020

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Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework

Reliability engineering & system safety, 2022-08, Vol.224, p.108525, Article 108525

2022

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Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data

Shock and vibration, 2019, Vol.2019 (2019), p.1-27

2019

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A common cause failure model for components under age-related degradation

Reliability engineering & system safety, 2020-03, Vol.195, p.106699, Article 106699

2020

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A novel Capsule Neural Network based model for drowsiness detection using electroencephalography signals

Expert systems with applications, 2022-09, Vol.201, p.116977, Article 116977

2022

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A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics

Sensors (Basel, Switzerland), 2019-12, Vol.20 (1), p.176

2019

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Assessment of Deep Learning techniques for Prognosis of solar thermal systems

Renewable energy, 2020-01, Vol.145, p.2178-2191

2020

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Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis

Structural health monitoring, 2019-07, Vol.18 (4), p.1092-1128

2019

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SCF-Net: A sparse counterfactual generation network for interpretable fault diagnosis

Reliability engineering & system safety, 2024-10, Vol.250, p.110285, Article 110285

2024

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Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion

Sensors (Basel, Switzerland), 2020-10, Vol.20 (19), p.5708

2020

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FS-SCF network: Neural network interpretability based on counterfactual generation and feature selection for fault diagnosis

Expert systems with applications, 2024-03, Vol.237, p.121670, Article 121670

2024

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Multi-unit risk aggregation with consideration of uncertainty and bias in risk metrics

Reliability engineering & system safety, 2019-08, Vol.188, p.473-482

2019

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A novel deep capsule neural network for remaining useful life estimation

Proceedings of the Institution of Mechanical Engineers. Part O, Journal of risk and reliability, 2020-02, Vol.234 (1), p.151-167

2020

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Estimation of one-minute direct normal irradiance using a deep neural network for five climate zones

Renewable & sustainable energy reviews, 2023-09, Vol.183, p.113486, Article 113486

2023

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System-level prognostics and health management: A graph convolutional network–based framework

Proceedings of the Institution of Mechanical Engineers. Part O, Journal of risk and reliability, 2021-02, Vol.235 (1), p.120-135

2021

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Characterization of the modal response using Deep recurrent neural networks

Engineering structures, 2022-04, Vol.256, p.113915, Article 113915

2022

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Enhancing the estimation of direct normal irradiance for six climate zones through machine learning models

Renewable energy, 2024-09, Vol.231, p.120925, Article 120925

2024

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Fully Adaptive Particle Filtering Algorithm for Damage Diagnosis and Prognosis

Entropy (Basel, Switzerland), 2018-01, Vol.20 (2), p.100

2018

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Convolutional neural networks for automated damage recognition and damage type identification

Multi-unit nuclear power plant probabilistic risk assessment: A comprehensive survey

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks

Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management

An improved multi-unit nuclear plant seismic probabilistic risk assessment approach

Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems

Operation scheduling in a solar thermal system: A reinforcement learning-based framework

Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework

Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data

A common cause failure model for components under age-related degradation

A novel Capsule Neural Network based model for drowsiness detection using electroencephalography signals

A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics

Assessment of Deep Learning techniques for Prognosis of solar thermal systems

Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis

SCF-Net: A sparse counterfactual generation network for interpretable fault diagnosis

Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion

FS-SCF network: Neural network interpretability based on counterfactual generation and feature selection for fault diagnosis

Multi-unit risk aggregation with consideration of uncertainty and bias in risk metrics

A novel deep capsule neural network for remaining useful life estimation

Estimation of one-minute direct normal irradiance using a deep neural network for five climate zones

System-level prognostics and health management: A graph convolutional network–based framework

Characterization of the modal response using Deep recurrent neural networks

Enhancing the estimation of direct normal irradiance for six climate zones through machine learning models

Fully Adaptive Particle Filtering Algorithm for Damage Diagnosis and Prognosis

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