Multi-sensor Fault Diagnosis of Aircraft Engine Based on Kalman Filter Group

2016 ◽  
pp. 363-379 ◽  
Author(s):  
Jixiang Hu ◽  
Lingfei Xiao
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Aircraft Engine ◽  
Sensor Fault ◽  
Filter Group

Dynamic Threshold Method Based Aircraft Engine Sensor Fault Diagnosis

2008 ◽  
Author(s):  
Wenfei Li ◽  
Rama K. Yedavalli
Keyword(s):  
Fault Diagnosis ◽  
Robustness Analysis ◽  
Aircraft Engine ◽  
Upper And Lower Bounds ◽  
Dynamic Threshold ◽  
False Alarms ◽  
Parametric Uncertainties ◽  
State Variables ◽  
Sensor Fault ◽  
Threshold Method

It is challenging to have a good fault diagnostic scheme that can distinguish between model uncertainties and occurrence of faults, which helps in reducing false alarms and missed detections. In this paper, a dynamic threshold algorithm is developed for aircraft engine sensor fault diagnosis that accommodates parametric uncertainties. Using the robustness analysis of parametric uncertain systems, we generate upper-and-lower bound trajectories for the dynamic threshold. The extent of parametric uncertainties is assumed to be such that the perturbed eigenvalues reside in a set of distinct circular regions. Dedicated observer scheme is used for engine sensor fault diagnosis design. The residuals are errors between estimated state variables from a bank of Kalman filters. With this design approach, the residual crossing the upper-and-lower bounds of the dynamic threshold indicates the occurrence of fault. Application to an aircraft gas turbine engine Component Level Model clearly illustrates the improved performance of the proposed method.

Sensor fault diagnosis for fast steering mirror system based on Kalman filter

2015 ◽  
Author(s):  
Hongju Wang ◽  
Qiliang Bao ◽  
Haifeng Yang ◽  
Sunjie Tao
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Mirror System ◽  
Sensor Fault

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.

Application of Improved Extended Kalman Filter for Adaptive Model of Aircraft Engine

2014 ◽  
Vol 709 ◽  
pp. 180-185
Author(s):  
Gu Ting Zhou ◽  
San Mai Su
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Extended Kalman Filter ◽  
Performance Monitoring ◽  
Aircraft Engine ◽  
Filter Method ◽  
Adaptive Model ◽  
Nominal Model ◽  
Simulation Results ◽  
Kalman Filter Method

Adaptive model is the basis of engine fault diagnosis, performance monitoring, engine control, etc. This paper presents an improved kalman filter method which uses engine measurable parameters deviation to estimate the degradation parameters to correct the nominal model, and the acquisition and application of multiple kalman filter gain matrices in the whole flight envelope is analyzed. Simulation is carried out taking a civil engine as simulation object, the simulation results show that the method used in this paper to establish unmeasured parameters adaptive model can get the engine parameters better.

scholarly journals Simulation of Sensor Fault Diagnosis for Wind Turbine Generators DFIG and PMSM Using Kalman Filter

2014 ◽  
Vol 54 ◽  
pp. 494-505 ◽  
Author(s):  
R. Saravanakumar ◽  
M. Manimozhi ◽  
D.P. Kothari ◽  
M. Tejenosh
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Sensor Fault ◽  
Turbine Generators ◽  
Wind Turbine Generators

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

2006 ◽  
Vol 129 (3) ◽  
pp. 746-754 ◽  
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 in-flight 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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