Uncertainty-aware deep learning for reliable health monitoring in safety-critical energy systems
In recent years, significant advancements in deep learning technology have facilitated the
development of intelligent health monitoring approaches for energy systems. However …
development of intelligent health monitoring approaches for energy systems. However …
Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems
A challenging problem in risk and reliability analysis of Complex Engineering Systems
(CES) is performing and updating risk and reliability assessments on the whole system with …
(CES) is performing and updating risk and reliability assessments on the whole system with …
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
B Rezaeianjouybari, Y Shang - Measurement, 2020 - Elsevier
Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …
various engineering fields, such as aerospace, nuclear energy, and water declination …
An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications
Deep learning-based models, while highly effective for prognostics and health management,
fail to reliably detect the data unknown in the training stage, referred to as out-of-distribution …
fail to reliably detect the data unknown in the training stage, referred to as out-of-distribution …
Physics-informed deep learning: A promising technique for system reliability assessment
Deep learning-based models for system prognostics and health management have received
significant attention in the reliability and safety fields. However, limited progress has been …
significant attention in the reliability and safety fields. However, limited progress has been …
Fusing physics-based and deep learning models for prognostics
Physics-based and data-driven models for remaining useful lifetime (RUL) prediction
typically suffer from two major challenges that limit their applicability to complex real-world …
typically suffer from two major challenges that limit their applicability to complex real-world …
Deep learning schemes for event identification and signal reconstruction in nuclear power plants with sensor faults
TH Lin, TC Wang, SC Wu - Annals of Nuclear Energy, 2021 - Elsevier
An initiating event (IE) is an event that may lead to core damage in a nuclear power plant
(NPP), and being able to identify an IE is crucial in determining what actions to take. This …
(NPP), and being able to identify an IE is crucial in determining what actions to take. This …
A hybrid deep learning framework for intelligent predictive maintenance of cyber-physical systems
M Shcherbakov, C Sai - ACM Transactions on Cyber-Physical Systems …, 2022 - dl.acm.org
The proliferation of cyber-physical systems (CPSs) and the advancement of the Internet of
Things (IoT) technologies have led to explosive digitization of the industrial sector. It offers …
Things (IoT) technologies have led to explosive digitization of the industrial sector. It offers …
Neural-based time series forecasting of loss of coolant accidents in nuclear power plants
In the last few years, deep learning in neural networks demonstrated impressive successes
in the areas of computer vision, speech and image recognition, text generation, and many …
in the areas of computer vision, speech and image recognition, text generation, and many …
Bayesian deep-learning-based health prognostics toward prognostics uncertainty
Deep-learning-based health prognostics is receiving ever-increasing attention. Most existing
methods leverage advanced neural networks for prognostics performance improvement …
methods leverage advanced neural networks for prognostics performance improvement …