A 2-D Long Short-Term Memory Fusion Networks for Bearing Remaining Useful Life Prediction

doi:10.1109/JSEN.2022.3202606

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 工业智能技术与系统

	A 2-D Long Short-Term Memory Fusion Networks for Bearing Remaining Useful Life Prediction
	Li, Yuan 1,2; Wang, Huanjie1,2 ; Li, Jingwei 1,2; Tan, Jie1,2
发表期刊	IEEE SENSORS JOURNAL
ISSN	1530-437X
	2022-11-15
卷号	22 期号:22 页码:21806-21815
通讯作者	Tan, Jie(jie.tan@ia.ac.cn)
摘要	Remaining useful life (RUL) prediction plays a significant role in prognostic and health management (PHM), and it can reduce the cost of unwanted failures and improve the reliability of industrial equipment and systems. In recent years, deep learning and sensor technology have boosted fault detection accuracy. This article proposes a two-stage prediction method based on 2-D long short-term memory (2D-LSTM) fusion networks with multisensor data for RUL prediction. This method first uses the Wilson Amplitude (WAMP) feature to automatically detect the fault occurrence time (FOT) and divide the bearing's degradation process into two stages: health and degradation state. Then a 2D-LSTM fusion network is employed to predict the RUL of bearings, including multiple subnetworks. In each subnetwork, deep temporal features of a single sensor's data are extracted by 2D-LSTM, which can capture both vertical and horizontal dependencies of data. Furthermore, an information fusion unit (IFU) is created to help the model incorporate features captured from each 2D-LSTM subnetwork. Experiments on two real-world bearing datasets show that our model's effectiveness is comparable to that of other existing methods. In addition, ablation studies are performed to verify the requirement and efficacy of each component of our proposed model.
关键词	Hidden Markov models Feature extraction Predictive models Sensors Mathematical models Data models Adaptation models 2-D long short-term memory (2D-LSTM) fault occurrence time (FOT) detection information fusion unit (IFU) remaining useful life (RUL) prediction
DOI	10.1109/JSEN.2022.3202606
关键词[WOS]	CONVOLUTIONAL NEURAL-NETWORK ; FAULT DIAGNOSTICS ; MODEL
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U1801263] ; National Natural Science Foundation of China[62003344]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Engineering ; Instruments & Instrumentation ; Physics
WOS类目	Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied
WOS记录号	WOS:000882008500048
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/51261
专题	中科院工业视觉智能装备工程实验室_工业智能技术与系统
通讯作者	Tan, Jie
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Li, Yuan,Wang, Huanjie,Li, Jingwei,et al. A 2-D Long Short-Term Memory Fusion Networks for Bearing Remaining Useful Life Prediction[J]. IEEE SENSORS JOURNAL,2022,22(22):21806-21815.
APA	Li, Yuan,Wang, Huanjie,Li, Jingwei,&Tan, Jie.(2022).A 2-D Long Short-Term Memory Fusion Networks for Bearing Remaining Useful Life Prediction.IEEE SENSORS JOURNAL,22(22),21806-21815.
MLA	Li, Yuan,et al."A 2-D Long Short-Term Memory Fusion Networks for Bearing Remaining Useful Life Prediction".IEEE SENSORS JOURNAL 22.22(2022):21806-21815.