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.

scholarly journals Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

2003 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Kalman Filters ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Fault Detection And Isolation ◽  
Actuator Fault ◽  
Estimation Errors ◽  
Turbine Engine ◽  
Engine Simulation ◽  
Correct Hypothesis ◽  
The One

In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.

A Generalised Likelihood Ratio Test for Adaptive Gas Turbine Health Monitoring

2008 ◽  
Author(s):  
S. Borguet ◽  
O. Le´onard
Keyword(s):  
Kalman Filter ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Health Monitoring ◽  
Aircraft Engine ◽  
Ratio Test ◽  
Secondary System ◽  
Commercial Aircraft ◽  
Turbine Engine ◽  
Auxiliary Component

Kalman filters are widely used in the turbine engine community for health monitoring purpose. This algorithm has proven its capability to track gradual deterioration with a good accuracy. On the other hand, its response to rapid deterioration is either a long delay in recognising the fault, and/or a spread of the estimated fault on several components. The main reason of this deficiency lies in the transition model of the parameters that is blended in the Kalman filter and assumes a smooth evolution of the engine condition. This contribution reports the development of an adaptive diagnosis tool that combines a Kalman filter and a secondary system that monitors the residuals. This auxiliary component implements a Generalised Likelihood Ratio Test in order to detect and estimate an abrupt fault. The enhancement in terms of accuracy and reactivity brought by this adaptive Kalman filter is highlighted for a variety of simulated fault cases that may be encountered on a commercial aircraft engine.

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 A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

2010 ◽  
Author(s):  
Liang Tang ◽  
Xiaodong Zhang ◽  
Jonathan A. DeCastro ◽  
Luis Farfan-Ramos ◽  
Donald L. Simon
Keyword(s):  
Health Management ◽  
System Development ◽  
Aircraft Engine ◽  
Fault Detection And Isolation ◽  
Specific Response ◽  
Sensor Faults ◽  
Actuator Faults ◽  
Unified Framework ◽  
Adaptive Estimator ◽  
Component Faults

A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented.

Model-Based Detection of Leaks and Blockages in Pipes

2000 ◽  
Author(s):  
Anju Narendra ◽  
John Down ◽  
Kirk Mathews ◽  
Ravi Rajamani ◽  
Sal Leone ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Kalman Filters ◽  
Experimental Validation ◽  
Fault Detection And Isolation ◽  
Piping System ◽  
Test Rig ◽  
Failed State ◽  
Model Based

This paper deals with a model-based strategy for detecting leaks and blockages in a network of pipes. The fault detection and isolation (FDI) system uses a Kalman filter and a model of the piping system to decide whether the system is operating in a normal or failed state, and to distinguish the type of fault. While the idea of using Kalman filters is quite old, its application in the present case is novel, as is the formulation of the FDI system that uses only one model. The theory is backed by experimental validation on a test rig.

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.

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.

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