DocumentCode :
14370
Title :
A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis
Author :
Kaibo Liu ; Gebraeel, N.Z. ; Jianjun Shi
Author_Institution :
H. Milton Stewart Sch. of Ind. & Syst. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Volume :
10
Issue :
3
fYear :
2013
fDate :
Jul-13
Firstpage :
652
Lastpage :
664
Abstract :
Prognostics involves the effective utilization of condition or performance-based sensor signals to accurately estimate the remaining lifetime of partially degraded systems and components. The rapid development of sensor technology, has led to the use of multiple sensors to monitor the condition of an engineering system. It is therefore important to develop methodologies capable of integrating data from multiple sensors with the goal of improving the accuracy of predicting remaining lifetime. Although numerous efforts have focused on developing feature-level and decision-level fusion methodologies for prognostics, little research has targeted the development of “data-level” fusion models. In this paper, we present a methodology for constructing a composite health index for characterizing the performance of a system through the fusion of multiple degradation-based sensor data. This methodology includes data selection, data processing, and data fusion steps that lead to an improved degradation-based prognostic model. Our goal is that the composite health index provides a much better characterization of the condition of a system compared to relying solely on data from an individual sensor. Our methodology was evaluated through a case study involving a degradation dataset of an aircraft gas turbine engine that was generated by the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS).
Keywords :
aerospace computing; aerospace engines; aerospace propulsion; aerospace simulation; condition monitoring; mechanical engineering computing; sensor fusion; C-MAPSS; aircraft gas turbine engine; commercial modular aero-propulsion system simulation; composite health index; composite health indices; data processing; data selection; data-level fusion model; decision-level fusion methodologies; degradation modeling; degradation-based sensor data; feature-level fusion methodologies; partially degraded systems; performance-based sensor signals; prognostic analysis; Data-level fusion; degradation modeling; prognostics; residual life distributions;
fLanguage :
English
Journal_Title :
Automation Science and Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1545-5955
Type :
jour
DOI :
10.1109/TASE.2013.2250282
Filename :
6496166
Link To Document :
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