Anomaly Detection Based on Multi-Detector Fusion Used in Turbine
Hui-Xin He1, Ning Li2, Geng-Feng Zheng3, Xu-Zhou Lin1, Da-Ren Yu1
(1.School of Astronautics, Harbin Institute of Technology,Harbin 150001, China;2.National Institutes for Food and Drug Control, Beijing 100050, China;3.Fujian Special Equipment Ispection and Research Institute, Fuzhou 350008, China);1.School of Astronautics, Harbin Institute of Technology,Harbin 150001, China;2.National Institutes for Food and Drug Control, Beijing 100050, China;3.Fujian Special Equipment Ispection and Research Institute, Fuzhou 350008, China
Abstract:
In order to improve the gas turbine engine health monitoring capability, using multiple detector fusion method in the monitoring system of gas turbine data monitor. Multi detector frame fusion includes point bias anomaly detector, contextual bias anomaly detector and collective bias anomaly detector, common to analyze the new arrival data, and the possible abnormal state to vote and weighted statistics as a result output. The experimental results show the method can effectively detect the mutation phenomenon, relatively slow changes and abnormal behavior discordant to the conditions. The framework applied to the gas turbine engine can effectively enhance the health diagnosis ability, will be highly applied for real industry.
Key words: fusion industry data anomaly detection
DOI：10.11916/j.issn.1005-9113.2013.01.021
Clc Number:TP277
Fund:
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Anomaly Detection Based on Multi-Detector Fusion Used in Turbine
本站小编 哈尔滨工业大学/2019-10-24
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