DocumentCode
553221
Title
Fault diagnosis for sensors of aero-engine based on improved least squares support vector regression
Author
Shujing Duan ; Qiuhong Li ; Yongping Zhao
Author_Institution
Coll. of Energy & Power Eng., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
Volume
3
fYear
2011
fDate
26-28 July 2011
Firstpage
1962
Lastpage
1966
Abstract
In this paper, an intelligent online sensor faults diagnosis system of aero-engine is designed based on improved least squares support vector machine. In order to distinguish sensor offset fault and drift fault, the method, called threshold discriminance, is adopted. For common sensor faults, including the single or multiple sensor offset faults and drift faults, the sensor faults diagnosis system is able to accomplish the fault detection, isolation and signal reconstruction well. And the simulation experiments of aero-engine sensor faults show the effectiveness and validity of the proposed system.
Keywords
aerospace engines; computerised instrumentation; fault diagnosis; intelligent sensors; least squares approximations; regression analysis; signal reconstruction; support vector machines; aeroengine sensor faults; fault detection; fault isolation; improved least square support vector regression; intelligent online sensor fault diagnosis system; sensor drift fault; sensor offset fault; signal reconstruction; threshold discriminance; Control systems; Fault diagnosis; Intelligent sensors; Redundancy; Sensor systems; Support vector machines; aero-engine; improved least squares support vector regression; one or two-sensor faults; sensor fault diagnosis;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-61284-180-9
Type
conf
DOI
10.1109/FSKD.2011.6019897
Filename
6019897
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