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    Uncertainty Quantification in the Prediction of Remaining Useful Life Considering Multiple Failure Modes

    Source: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2024:;volume( 011 ):;issue: 003::page 31202-1
    Author:
    Gandur, Nazir Laureano
    ,
    Ekwaro-Osire, Stephen
    DOI: 10.1115/1.4066722
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Despite the substantive literature on remaining useful life (RUL) prediction, less attention is paid to the influence of epistemic uncertainty and aleatory uncertainty in multiple failure behaviors in the accuracy of RUL. The research question in this study was: can uncertainties be quantified in predicting the RUL of systems with multiple failure modes? The first objective was to quantify the uncertainties in the prediction of RUL, considering known multiple failure modes. This objective used vibration data from accelerated degradation experiments of rolling element bearings. The second objective was to calculate the uncertainties in the prediction of RUL, considering the multiple failure modes as unknown. The experimental data used in this objective were from run-to-failure tests of Li-ion batteries. An analysis was performed on how the uncertainties affect the RUL prediction in systems with known multiple failure modes and systems where the multiple failure modes were unknown. A Bayesian neural network (BNN) was used to quantify epistemic and aleatory uncertainty while predicting RUL. The results of the qualitative uncertainties on RUL in systems with multiple failure modes were presented and discussed. Also, the study yielded an RUL uncertainty quantification model for multiple failure modes. The proposed framework's performance in the RUL prediction was demonstrated. Finally, the epistemic and aleatory uncertainties were quantified in the system's RUL. It was shown that systems that fail due to the same failure mode tend to have similar uncertainty values over time. The results in this paper may lead to the design of more reliable systems that exhibit multiple failure modes.
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      Uncertainty Quantification in the Prediction of Remaining Useful Life Considering Multiple Failure Modes

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    contributor authorGandur, Nazir Laureano
    contributor authorEkwaro-Osire, Stephen
    date accessioned2025-04-21T10:08:12Z
    date available2025-04-21T10:08:12Z
    date copyright11/4/2024 12:00:00 AM
    date issued2024
    identifier issn2332-9017
    identifier otherrisk_011_03_031202.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305572
    description abstractDespite the substantive literature on remaining useful life (RUL) prediction, less attention is paid to the influence of epistemic uncertainty and aleatory uncertainty in multiple failure behaviors in the accuracy of RUL. The research question in this study was: can uncertainties be quantified in predicting the RUL of systems with multiple failure modes? The first objective was to quantify the uncertainties in the prediction of RUL, considering known multiple failure modes. This objective used vibration data from accelerated degradation experiments of rolling element bearings. The second objective was to calculate the uncertainties in the prediction of RUL, considering the multiple failure modes as unknown. The experimental data used in this objective were from run-to-failure tests of Li-ion batteries. An analysis was performed on how the uncertainties affect the RUL prediction in systems with known multiple failure modes and systems where the multiple failure modes were unknown. A Bayesian neural network (BNN) was used to quantify epistemic and aleatory uncertainty while predicting RUL. The results of the qualitative uncertainties on RUL in systems with multiple failure modes were presented and discussed. Also, the study yielded an RUL uncertainty quantification model for multiple failure modes. The proposed framework's performance in the RUL prediction was demonstrated. Finally, the epistemic and aleatory uncertainties were quantified in the system's RUL. It was shown that systems that fail due to the same failure mode tend to have similar uncertainty values over time. The results in this paper may lead to the design of more reliable systems that exhibit multiple failure modes.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleUncertainty Quantification in the Prediction of Remaining Useful Life Considering Multiple Failure Modes
    typeJournal Paper
    journal volume11
    journal issue3
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
    identifier doi10.1115/1.4066722
    journal fristpage31202-1
    journal lastpage31202-16
    page16
    treeASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2024:;volume( 011 ):;issue: 003
    contenttypeFulltext
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