A Data Fusion-Based Methodology of Constructing Health Indicators for Anomaly Detection and Prognostics

Shaowei Chen, Pengfei Wen, Shuai Zhao, Dengshan Huang, Meng Wu, Yaming Zhang

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

6 Citationer (Scopus)

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.

OriginalsprogEngelsk
TitelProceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018
RedaktørerDian Wang, Yong Zhou, Diego Cabrera, Chuan Li, Chunlin Zhang
Antal sider7
ForlagIEEE Signal Processing Society
Publikationsdato11 mar. 2019
Sider570-576
Artikelnummer8664723
ISBN (Elektronisk)9781538660577
DOI
StatusUdgivet - 11 mar. 2019
Udgivet eksterntJa
Begivenhed2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 - Xi'an, Kina
Varighed: 15 aug. 201817 aug. 2018

Konference

Konference2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018
Land/OmrådeKina
ByXi'an
Periode15/08/201817/08/2018
SponsorBeijing Institute of Technology, Carleton University, Chongqing Technology and Business University, et al., IEEE, Northwestern Polytechnical University
NavnProceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018

Bibliografisk note

Publisher Copyright:
© 2018 IEEE.

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