Abstract
Anomaly detection and Remaining Useful Life (RUL) prediction are the most significant components of Prognostics and Health Management. A typical workflow is to extract features or construct health indicators by sensor fusion of the equipment and then perform anomaly detection and RUL prediction according to the health indicators. In this paper, we present a data-fusion based methodology for constructing two composite health indicators through integrating multiple run-To-failure sensor data towards to the anomaly detection and the RUL prediction, respectively. A novel optimization methodology termed as the Genetic Algorithm is proposed for constructing the composite health indicators, which are capable of presenting a better reflection of the health condition of systems. This optimization methodology makes the fusion of multiple sensor data no longer limited in linear fusion. For this algorithm, the property of a health indicator is the fitness and the different fusion methods to produce health indicators are individuals of a population. Our methodology was verified by applying to a degradation dataset of an aircraft gas turbine engine that was generated by the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS). The result shows that the extracted feature have better performance than using original data in the anomaly detection and the RUL prediction.
Originalsprog | Engelsk |
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Titel | Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 |
Redaktører | Dian Wang, Yong Zhou, Diego Cabrera, Chuan Li, Chunlin Zhang |
Antal sider | 7 |
Forlag | IEEE Signal Processing Society |
Publikationsdato | 11 mar. 2019 |
Sider | 570-576 |
Artikelnummer | 8664723 |
ISBN (Elektronisk) | 9781538660577 |
DOI | |
Status | Udgivet - 11 mar. 2019 |
Udgivet eksternt | Ja |
Begivenhed | 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 - Xi'an, Kina Varighed: 15 aug. 2018 → 17 aug. 2018 |
Konference
Konference | 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 |
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Land/Område | Kina |
By | Xi'an |
Periode | 15/08/2018 → 17/08/2018 |
Sponsor | Beijing Institute of Technology, Carleton University, Chongqing Technology and Business University, et al., IEEE, Northwestern Polytechnical University |
Navn | Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 |
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Bibliografisk note
Publisher Copyright:© 2018 IEEE.