首页|A data-driven approach to RUL prediction of tools

A data-driven approach to RUL prediction of tools

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An effective and reliable prediction of the remaining useful life(RUL)of a tool is important to a metal forming process because it can significantly reduce unexpected maintenance,avoid machine shutdowns and increase system stability.This study proposes a new data-driven approach to the RUL prediction for metal forming processes under multiple contact sliding conditions.The data-driven approach took advantage of bidirectional long short-term memory(BLSTM)and convolutional neural net-works(CNN).A pre-trained lightweight CNN-based net-work,WearNet,was re-trained to classify the wear states of workpiece surfaces with a high accuracy,then the clas-sification results were passed into a BLSTM-based regres-sion model as inputs for RUL estimation.The experimental results demonstrated that this approach was able to predict the RUL values with a small error(below 5％)and a low root mean square error(RMSE)(around 1.5),which was more superior and robust than the other state-of-the-art methods.

Remaining useful life(RUL)Bidirectional long short-term memory(BLSTM)Data-driven approachMetal forming

Wei Li、Liang-Chi Zhang、Chu-Han Wu、Yan Wang、Zhen-Xiang Cui、Chao Niu

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Department of Mechanical Engineering,University College London,London WC1E 7JE,UK

School of Mechanical and Manufacturing Engineering,The University of New South Wales,Kensington,NSW 2052,Australia

Shenzhen Key Laboratory of Cross-Scale Manufacturing Mechanics,Southern University of Science and Technology,Shenzhen 518055,Guangdong,People's Republic of China

SUSTech Institute for Manufacturing Innovation,Southern University of Science and Technology,Shenzhen 518055,Guangdong,People's Republic of China

Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen 518055,Guangdong,People's Republic of China

Baoshan Iron & Steel Co.,Ltd.,Shanghai 200941,People's Republic of China

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Baosteel Australia Research and Development Centre(BAJC)PortfolioARC Hub for Computational Particle TechnologyChinese Guangdong Specific Discipline ProjectShenzhen Key Laboratory Project of Crossscale Manufacturing Mechanics

BA17001IH1401000352020ZDZX2006ZDSYS20200810171201007

2024

10.1007/s40436-023-00464-y

先进制造进展(英文版)

先进制造进展(英文版)

ISSN：
年,卷(期)：2024.12(1)
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