Anomaly Detection Based on Multi-Detector Fusion Used in Turbine

被引:0
作者
Hui-Xin He [1 ]
Ning Li [2 ]
Geng-Feng Zheng [3 ]
Xu-Zhou Lin [1 ]
Da-Ren Yu [1 ]
机构
[1] School of Astronautics,Harbin Institute of Technology
[2] National Institutes for Food and Drug Control
[3] Fujian Special Equipment Ispection and Research Institute
关键词
fusion; industry data; anomaly detection;
D O I
暂无
中图分类号
TP311.13 []; V263.6 [故障分析及排除];
学科分类号
082503 ; 1201 ;
摘要
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.
引用
收藏
页码:113 / 117
页数:5
相关论文
共 3 条
  • [1] Anomaly detection[J] . Varun Chandola,Arindam Banerjee,Vipin Kumar.ACM Computing Surveys (CSUR) . 2009 (3)
  • [2] A Survey of Outlier Detection Methodologies[J] . Victoria J. Hodge,Jim Austin.Artificial Intelligence Review . 2004 (2)
  • [3] Data mining:concepts and techniques .2 Han J,Kamber M,et al. Morgan Kaufmann Pub . 2011