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
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;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
DOI :
10.1109/FSKD.2011.6019897