Semantic Sensor Fusion for Fault Diagnosis in Aircraft Gas Turbine Engines

被引：0

作者：

Sarkar, Soumik ^{[1
]}

Singh, Dheeraj Sharan ^{[1
]}

Srivastav, Abhishek ^{[1
]}

Ray, Asok ^{[1
]}

机构：

[1] Penn State Univ, Dept Mech Engn, University Pk, PA 16802 USA

来源：

2011 AMERICAN CONTROL CONFERENCE | 2011年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Data-driven fault diagnosis of a complex system such as an aircraft gas turbine engine requires interpretation of multi-sensor information to assure enhanced performance. This paper proposes feature-level sensor information fusion in the framework of symbolic dynamic filtering. This hierarchical approach involves construction of composite patterns consisting of: (i) atomic patterns extracted from single sensor data and (ii) relational patterns that represent the cross-dependencies among different sensor data. The underlying theories are presented along with necessary assumptions and the proposed method is validated on the NASA C-MAPSS simulation model of aircraft gas turbine engines.

引用

页码：220 / 225

页数：6

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