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contributor authorShen, Xintian
contributor authorWang, Zi
contributor authorDing, Peng
contributor authorZhao, Xiaoli
contributor authorJia, Minping
date accessioned2025-04-21T10:26:34Z
date available2025-04-21T10:26:34Z
date copyright6/29/2024 12:00:00 AM
date issued2024
identifier issn2332-9017
identifier otherrisk_011_01_011101.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306207
description abstractAs critical components in industrial application scenarios, high-precision and high-confidence health assessment of rolling bearings attract more and more attention. Currently, predictive maintenance obtains outstanding achievements under the same object and working conditions. However, evaluation performances under variable working conditions and different specifications still need to be improved. This study zeroes in on the cross-domain prognostics of rotating machinery under oil and grease lubrication conditions. It proposes an unsupervised domain adaptation (DA) transform reconstruction GRU (UDATrGRU) prognostics framework, which captures the common degradation characteristics under different lubrication conditions through the designed second-order statistical quantity, facilitating the following high-precision predictions. To be specific, the vibration degradation features are first extracted through signal preprocessing and then input into UDATrGRU. The developed domain adaptation layer calculates high-dimensional projections between diverse data sets, and then corresponding degradation features are statistically aligned under the pressure of the designed quantity. Subsequently, time-series modeling and Bootstrap-based uncertainty estimations are carried out. Finally, lifecycle accelerated tests of the rolling bearing from PRONOSITA and ABLT-1A cross-validate the feasibility and effectiveness of the proposed machinery prognostics framework. The results are pretty promising: compared to existing methods, our UDATrGRU framework has achieved an improvement of at least 5.65% in R2 and a reduction of at least 21.5% in root mean squared error.
publisherThe American Society of Mechanical Engineers (ASME)
titleA Novel Unsupervised Domain Adaptation Transformation Reconstructed Gated Recurrent Unit Framework Considering Prediction Uncertainty for Machinery Prognostics Under Variable Lubrication Conditions
typeJournal Paper
journal volume11
journal issue1
journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
identifier doi10.1115/1.4065753
journal fristpage11101-1
journal lastpage11101-10
page10
treeASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2024:;volume( 011 ):;issue: 001
contenttypeFulltext


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