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题名	A data-driven approach to RUL prediction of tools
作者	Li, Wei 1,5; Zhang, Liang-Chi2,3,4 ; Wu, Chu-Han 5; Wang, Yan 5; Cui, Zhen-Xiang 6; Niu, Chao 6
通讯作者	Zhang, Liang-Chi
发表日期	2023-10-01
DOI	10.1007/s40436-023-00464
发表期刊	ADVANCES IN MANUFACTURING 影响因子和分区
ISSN	2095-3127
EISSN	2195-3597
卷号	12 期号:1页码:6-18
摘要	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 networks (CNN). A pre-trained lightweight CNN-based network, WearNet, was re-trained to classify the wear states of workpiece surfaces with a high accuracy, then the classification results were passed into a BLSTM-based regression 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.
关键词	Remaining useful life (RUL) Bidirectional long short-term memory (BLSTM) Data-driven approach Metal forming
相关链接	[来源记录]
收录类别	SCI
语种	英语
学校署名	通讯
资助项目	This research was supported by the Baosteel Australia Research and Development Centre (BAJC) Portfolio (Grant No. BA17001), the ARC Hub for Computational Particle Technology (Grant No. IH140100035), the Chinese Guangdong Specific Discipline Project (Grant[BA17001] ; Baosteel Australia Research and Development Centre (BAJC) Portfolio[IH140100035] ; ARC Hub for Computational Particle Technology[2020ZDZX2006] ; Chinese Guangdong Specific Discipline Project[ZDSYS20200810171201007]
WOS研究方向	Engineering ; Materials Science
WOS类目	Engineering, Manufacturing ; Materials Science, Multidisciplinary
WOS记录号	WOS:001078567400001
出版者	SPRINGER
Scopus记录号	2-s2.0-85173715440
来源库	Web of Science
引用统计	被引频次[WOS]：8
成果类型	期刊论文
条目标识符	//www.snoollab.com/handle/2SGJ60CL/575864
专题	工学院_力学与航空航天工程系
作者单位	1.UCL, Dept Mech Engn, London WC1E 7JE, England 2.Southern Univ Sci & Technol, Shenzhen Key Lab Cross Scale Mfg Mech, Shenzhen 518055, Guangdong, Peoples R China 3.Southern Univ Sci & Technol, SUSTech Inst Mfg Innovat, Shenzhen 518055, Guangdong, Peoples R China 4.Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Guangdong, Peoples R China 5.Univ New South Wales, Sch Mech & Mfg Engn, Kensington, NSW 2052, Australia 6.Baoshan Iron & Steel Co Ltd, Shanghai 200941, Peoples R China
通讯作者单位	; 力学与航空航天工程系
推荐引用方式 GB/T 7714	Li, Wei,Zhang, Liang-Chi,Wu, Chu-Han,et al. A data-driven approach to RUL prediction of tools[J]. ADVANCES IN MANUFACTURING,2023,12(1):6-18.
APA	Li, Wei,Zhang, Liang-Chi,Wu, Chu-Han,Wang, Yan,Cui, Zhen-Xiang,&Niu, Chao.(2023).A data-driven approach to RUL prediction of tools.ADVANCES IN MANUFACTURING,12(1),6-18.
MLA	Li, Wei,et al."A data-driven approach to RUL prediction of tools".ADVANCES IN MANUFACTURING 12.1(2023):6-18.