A Bank of Kalman Filters and a Robust Kalman Filter Applied in Fault Diagnosis of Aircraft Engine Sensor/Actuator

2007 ◽  
Author(s):  
Wei Xue ◽  
Ying-qing Guo ◽  
Xiao-dong Zhang
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Kalman Filters ◽  
Aircraft Engine ◽  
Sensor Actuator ◽  
Robust Kalman Filter

Fault Diagnosis of a Sensor Network: A Distributed Filtering Approach

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Rajamani Doraiswami ◽  
Lahouari Cheded
Keyword(s):  
Fault Diagnosis ◽  
Sensor Network ◽  
Kalman Filters ◽  
Measurement Noise ◽  
Fluid System ◽  
Efficient Procedure ◽  
Sensor Actuator ◽  
Tank System ◽  
Reference Input ◽  
Filtering Approach

This paper proposes a model-based approach to develop a novel fault diagnosis scheme for a sensor network of a cascade, parallel and feedback combination of subsystems. The objective is to detect and isolate a fault in any of the subsystems and measurement sensors which are subject to disturbances and/or measurement noise. Our approach hinges on the use of a bank of Kalman filters (KF) to detect and isolate faults. Each KF is driven by either a pair (a) of consecutive sensor measurements or (b) of a reference input and a measurement. It is shown that the KF residual is a reliable indicator of a fault in subsystems and sensors located in the path between the pair of the KF's input. The simple and efficient procedure proposed here analyzes each of the associated paths and leads to both the detection and isolation of any fault that occurred in the paths analyzed. The scheme is successfully evaluated on several simulated examples and on a physical fluid system exemplified by a benchmarked laboratory-scale two-tank system to detect and isolate faults including sensor, actuator and leakage ones.

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 Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics

2004 ◽  
Vol 128 (2) ◽  
pp. 281-287 ◽  
Author(s):  
P. Dewallef ◽  
C. Romessis ◽  
O. Léonard ◽  
K. Mathioudakis
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Diagnostic Method ◽  
A Priori ◽  
Aircraft Engine ◽  
Bayesian Belief Network ◽  
A Priori Information ◽  
Time Step ◽  
Classification Techniques ◽  
Priori Information

A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Network (BBN) is presented. A soft-constrained Kalman filter uses a priori information derived by a BBN at each time step, to derive estimations of the unknown health parameters. The resulting algorithm has improved identification capability in comparison to the stand-alone Kalman filter. The paper focuses on a way of combining the information produced by the BBN with the Kalman filter. An extensive set of fault cases is used to test the method on a typical civil turbofan layout. The effectiveness of the method is thus demonstrated, and its advantages over individual constituent methods are presented.

scholarly journals Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics

2004 ◽  
Author(s):  
P. Dewallef ◽  
C. Romessis ◽  
O. Le´onard ◽  
K. Mathioudakis
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Diagnostic Method ◽  
A Priori ◽  
Aircraft Engine ◽  
Bayesian Belief Networks ◽  
Belief Networks ◽  
A Priori Information ◽  
Time Step ◽  
Priori Information

A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Networks (BBN) is presented. A soft-constrained Kalman filter uses a priori information derived by a BBN at each time step, to derive estimations of the unknown health parameters. The resulting algorithm has improved identification capability in comparison to the stand alone Kalman filter. The paper focuses on the way of combining the information produced by the BBN with the Kalman filter. An extensive set of fault cases is used to test the method on a typical civil turbofan layout. The effectiveness of the method is thus demonstrated and its advantages over individual constituent methods are shown.

scholarly journals Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics

2005 ◽  
Vol 127 (3) ◽  
pp. 497-504 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Aircraft Engine ◽  
Propulsion System ◽  
Fault Detection And Isolation ◽  
Actuator Faults ◽  
Commercial Aircraft ◽  
Nonlinear Simulation ◽  
Turbine Engine ◽  
Correct Hypothesis

In this paper, an approach for in-flight fault detection and isolation (FDI) of aircraft engine sensors based on a bank of Kalman filters is developed. This approach utilizes multiple Kalman filters, each of which is designed based on a specific fault hypothesis. When the propulsion system experiences a fault, only one Kalman filter with the correct hypothesis is able to maintain the nominal estimation performance. Based on this knowledge, the isolation of faults is achieved. Since the propulsion system may experience component and actuator faults as well, a sensor FDI system must be robust in terms of avoiding misclassifications of any anomalies. The proposed approach utilizes a bank of m+1 Kalman filters where m is the number of sensors being monitored. One Kalman filter is used for the detection of component and actuator faults while each of the other m filters detects a fault in a specific sensor. With this setup, the overall robustness of the sensor FDI system to anomalies is enhanced. Moreover, numerous component fault events can be accounted for by the FDI system. The sensor FDI system is applied to a nonlinear simulation of a commercial aircraft gas turbine engine, and its performance is evaluated at multiple power settings at a cruise operating point using various fault scenarios.

scholarly journals Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics

2004 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Aircraft Engine ◽  
Propulsion System ◽  
Fault Detection And Isolation ◽  
Actuator Faults ◽  
Commercial Aircraft ◽  
Engine Simulation ◽  
Correct Hypothesis ◽  
Multiple Power

In this paper, an approach for in-flight fault detection and isolation (FDI) of aircraft engine sensors based on a bank of Kalman filters is developed. This approach utilizes multiple Kalman filters, each of which is designed based on a specific fault hypothesis. When the propulsion system experiences a fault, only one Kalman filter with the correct hypothesis is able to maintain the nominal estimation performance. Based on this knowledge, the isolation of faults is achieved. Since the propulsion system may experience component and actuator faults as well, a sensor FDI system must be robust in terms of avoiding misclassifications of any anomalies. The proposed approach utilizes a bank of (m+1) Kalman filters where m is the number of sensors being monitored. One Kalman filter is used for the detection of component and actuator faults while each of the other m filters detects a fault in a specific sensor. With this setup, the overall robustness of the sensor FDI system to anomalies is enhanced. Moreover, numerous component fault events can be accounted for by the FDI system. The sensor FDI system is applied to a commercial aircraft engine simulation, and its performance is evaluated at multiple power settings at a cruise operating point using various fault scenarios.

Robust Kalman Filter Thrust Estimation in a Turbofan Engine

2006 ◽  
Author(s):  
Mattias Henriksson ◽  
Dan Ring
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Filter Design ◽  
The Other ◽  
Turbofan Engine ◽  
Turbine Engine ◽  
Engine Model ◽  
Lower Accuracy ◽  
Robust Kalman Filter ◽  
Linearization Point

This article will present that a robust Kalman filter design has a favorable property, when applied on thrust estimation on a low bypass turbofan gas turbine engine, compared to the regular Kalman filter design. This property is a larger operation range in parameters around the linearization point. On the other hand, the robust Kalman filter has marginally lower accuracy at the linearization point. This paper will present a method for describing the uncertainties in the engine model for use in the design of a robust Kalman filter. Both a regular Kalman filter and a robust Kalman filters are evaluated through simulations around a linearization point by using simulations of a nonlinear military turbofan engine.

Sign in / Sign up

Export Citation Format

Share Document