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Multisensor contrast neural network for prediction of remaining useful life of rolling bearing under scarce labeled data
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Binkun LIU1,2,3,4, Zhenyi XU‡1,2,3, Yu KANG3,4, Yang CAO3,4, Yunbo ZHAO‡3,4. Multisensor contrast neural network for prediction of remaining useful life of rolling bearing under scarce labeled data[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Multisensor contrast neural network for prediction of remaining useful life of rolling bearing under scarce labeled data",
author="Binkun LIU1,2,3,4, Zhenyi XU‡1,2,3, Yu KANG3,4, Yang CAO3,4, Yunbo ZHAO‡3,4",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400753"
}
%0 Journal Article
%T Multisensor contrast neural network for prediction of remaining useful life of rolling bearing under scarce labeled data
%A Binkun LIU1
%A 2
%A 3
%A 4
%A Zhenyi XU‡1
%A 2
%A 3
%A Yu KANG3
%A 4
%A Yang CAO3
%A 4
%A Yunbo ZHAO‡3
%A 4
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400753
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T1 - Multisensor contrast neural network for prediction of remaining useful life of rolling bearing under scarce labeled data
A1 - Binkun LIU1
A1 - 2
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A1 - Zhenyi XU‡1
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A1 - 3
A1 - Yu KANG3
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A1 - Yang CAO3
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A1 - Yunbo ZHAO‡3
A1 - 4
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%@ 2095-9184
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2400753
Abstract: Predicting remaining useful life (RUL) of bearing under scarce labeled data is significant for intelligent manufacturing. Current approaches typically encounter the challenge that different degradation stages have similar behaviors in multisensor scenarios. Given that cross-sensor similarity improves the discrimination of degradation features, we propose a multisensor contrast method for RUL prediction under scarce RUL-labeled data, in which we exploit cross-sensor similarity to mine multisensor similar representations that indicate machine health condition from rich unlabeled sensor data in co-occurrence space. Specifically, we use Resnet18 to span the features of different sensors into a co-occurrence space. We then obtain multisensor similar representations of abundant unlabeled data through alternate contrast based on cross-sensor similarity in co-occurrence space, which indicate the machine degradation stage. Finally, we fine-tune these similar representations with attention to achieve RUL prediction with limited labeled sensor data. The proposed method is evaluated on a publicly available bearing dataset, and the results show that the mean absolute percentage error is reduced by at least 0.058, and the score is improved by at least 0.122 compared to the state-of-the-art methods.
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