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 …
An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications
T Zhou, L Zhang, T Han, EL Droguett… - … and System Safety, 2023 - econpapers.repec.org
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 …
[PDF][PDF] An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applications
T Zhou, EL Droguett, A Mosleh, FTS Chan - researchgate.net
There has been a growing interest in deep learning-based prognostic and health
management (PHM) for building end-to-end maintenance decision support systems …
management (PHM) for building end-to-end maintenance decision support systems …
An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applications
T Zhou, E Lopez Droguett, A Mosleh… - arXiv e …, 2021 - ui.adsabs.harvard.edu
There has been a growing interest in deep learning-based prognostic and health
management (PHM) for building end-to-end maintenance decision support systems …
management (PHM) for building end-to-end maintenance decision support systems …
An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applications
T Zhou, EL Droguett, A Mosleh, FTS Chan - arXiv preprint arXiv …, 2021 - arxiv.org
There has been a growing interest in deep learning-based prognostic and health
management (PHM) for building end-to-end maintenance decision support systems …
management (PHM) for building end-to-end maintenance decision support systems …
An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications
T Zhou, L Zhang, T Han, EL Droguett… - … and System Safety, 2023 - ideas.repec.org
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 …