| Related citation: | Hui-Xin He,Ning Li,Geng-Feng Zheng,Xu-Zhou Lin,Da-Ren Yu.Anomaly Detection Based on Multi-Detector Fusion Used in Turbine[J].Journal of Harbin Institute Of Technology(New Series),2013,20(1):113-117.DOI:10.11916/j.issn.1005-9113.2013.01.021. |
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| Author Name | Affiliation | | Hui-Xin He | School of Astronautics, Harbin Institute of Technology,Harbin 150001, China | | Ning Li | National Institutes for Food and Drug Control, Beijing 100050, China | | Geng-Feng Zheng | Fujian Special Equipment Ispection and Research Institute, Fuzhou 350008, China | | Xu-Zhou Lin | School of Astronautics, Harbin Institute of Technology,Harbin 150001, China | | Da-Ren Yu | School of Astronautics, Harbin Institute of Technology,Harbin 150001, China |
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| 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: |
