Multiple Sensor Data Fusion for Degradation Modeling and Prognostics   Under Multiple Operational Conditions

doi:10.1109/TR.2016.2575449

CORC > 北京大学 > 工学院

	Multiple Sensor Data Fusion for Degradation Modeling and Prognostics Under Multiple Operational Conditions
	Yan, Hao ; Liu, Kaibo ; Zhang, Xi ; Shi, Jianjun
刊名	IEEE TRANSACTIONS ON RELIABILITY
	2016
关键词	Data fusion multiple operational conditions multiple sensors prognostics remaining life prediction RESIDUAL-LIFE DISTRIBUTIONS PREDICTION TIME ENVIRONMENTS
DOI	10.1109/TR.2016.2575449
英文摘要	Due to the rapid advances in sensing and computing technology, multiple sensors have been widely used to simultaneously monitor the health status of an operation unit. This creates a data-rich environment, enabling an unprecedented opportunity to make better understanding and inference about the current and future behavior of the unit in real time. Depending on specific task requirements, a unit is often required to run under multiple operational conditions, each of which may affect the degradation path of the unit differently. Thus, two fundamental challenges remain to be solved for effective degradation modeling and prognostic analysis: 1) how to leverage the dependent information among multiple sensor signals to better understand the health condition of the unit; and 2) how to model the effects of multiple conditions on the degradation characteristics of the unit. To address these two issues, this paper develops a data fusion methodology that integrates the information from multiple sensors to construct a health index when the monitored unit runs under multiple operational conditions. Our goal is that the developed health index provides a much better characterization of the health condition of the degraded unit, and, thus, leads to a better prediction of the remaining lifetime. Unlike other existing approaches, the developed data fusion model combines the fusion procedure and the degradation modeling under different operational conditions in a unified manner. The effectiveness of the proposed method is demonstrated in a case study, which involves a degradation dataset of aircraft gas turbine engines collected from 21 sensors under six different operational conditions.; National Science Foundation [CMMI-1435809, 1233143]; NSFC [71201002, 71471005]; SCI(E); EI; ARTICLE; yanhao@gatech.edu; kliu8@wisc.edu; xi.zhang@pku.edu.cn; jianjun.shi@isye.gatech.edu; 3; 1416-1426; 65
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/491449]
专题	工学院
推荐引用方式 GB/T 7714	Yan, Hao,Liu, Kaibo,Zhang, Xi,et al. Multiple Sensor Data Fusion for Degradation Modeling and Prognostics Under Multiple Operational Conditions[J]. IEEE TRANSACTIONS ON RELIABILITY,2016.
APA	Yan, Hao,Liu, Kaibo,Zhang, Xi,&Shi, Jianjun.(2016).Multiple Sensor Data Fusion for Degradation Modeling and Prognostics Under Multiple Operational Conditions.IEEE TRANSACTIONS ON RELIABILITY.
MLA	Yan, Hao,et al."Multiple Sensor Data Fusion for Degradation Modeling and Prognostics Under Multiple Operational Conditions".IEEE TRANSACTIONS ON RELIABILITY (2016).