A sensor fault detection for aircraft using a single Kalman filter and hidden Markov models

2014 ◽  
Author(s):  
Konrad Rudin ◽  
Guillaume J. J. Ducard ◽  
Roland Y. Siegwart
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Sensor Fault ◽  
Sensor Fault Detection

Sensor fault detection, isolation and system reconfiguration based on extended Kalman filter for induction motor drives

2013 ◽  
Vol 7 (7) ◽  
pp. 607-617 ◽  
Author(s):  
Xinan Zhang ◽  
Gilbert Foo ◽  
Mahinda Don Vilathgamuwa ◽  
King Jet Tseng ◽  
Bikramjit Singh Bhangu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Motor Drives ◽  
Sensor Fault ◽  
Induction Motor Drives ◽  
System Reconfiguration ◽  
Sensor Fault Detection

Hybrid Kalman Filter Approach for Aircraft Engine In-Flight Diagnostics: Sensor Fault Detection Case

2006 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Linear Models ◽  
Detection System ◽  
Piecewise Linear ◽  
Diagnostic System ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Sensor Fault ◽  
Sensor Fault Detection

In this paper, a diagnostic system based on a uniquely structured Kalman filter is developed for its application to inflight fault detection of aircraft engine sensors. The Kalman filter is a hybrid of a nonlinear on-board engine model (OBEM) and piecewise linear models. The utilization of the nonlinear OBEM allows the reference health baseline of the diagnostic system to be updated, through a relatively simple process, to the health condition of degraded engines. Through this health baseline update, the diagnostic effectiveness of the in-flight sensor fault detection system is maintained as the health of the engine degrades over time. The performance of the sensor fault detection system is evaluated in a simulation environment at several operating conditions during the cruise phase of flight.

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