Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/616904
Title: Multiple Sensor Data Fusion for Degradation Modeling and Prognostics Under Multiple Operational Conditions
Authors: Hao Yan;Kaibo Liu;Xi Zhang;Jianjun Shi
subject: multiple operational conditions|prognostics|remaining life prediction|multiple sensors|Data fusion
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/147782
http://localhost/handle/Hannan/616904
ISSN: 0018-9529
1558-1721
volume: 65
issue: 3
Appears in Collections:2016

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Title: Multiple Sensor Data Fusion for Degradation Modeling and Prognostics Under Multiple Operational Conditions
Authors: Hao Yan;Kaibo Liu;Xi Zhang;Jianjun Shi
subject: multiple operational conditions|prognostics|remaining life prediction|multiple sensors|Data fusion
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/147782
http://localhost/handle/Hannan/616904
ISSN: 0018-9529
1558-1721
volume: 65
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7508495.pdf527.09 kBAdobe PDFThumbnail
Preview File
Title: Multiple Sensor Data Fusion for Degradation Modeling and Prognostics Under Multiple Operational Conditions
Authors: Hao Yan;Kaibo Liu;Xi Zhang;Jianjun Shi
subject: multiple operational conditions|prognostics|remaining life prediction|multiple sensors|Data fusion
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/147782
http://localhost/handle/Hannan/616904
ISSN: 0018-9529
1558-1721
volume: 65
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7508495.pdf527.09 kBAdobe PDFThumbnail
Preview File