References of "Borguet, Sébastien"
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See detailAircraft engine gas path diagnostic methods: public benchmarking results
Simon, Donald; Borguet, Sébastien ULg; Léonard, Olivier ULg et al

in Proceedings of ASME Turbo Expo 2013 (2013, June)

Recent technology reviews have identified the need for objective assessments of aircraft engine health management (EHM) technologies. To help address this issue, a publicly available gas path diagnostic ... [more ▼]

Recent technology reviews have identified the need for objective assessments of aircraft engine health management (EHM) technologies. To help address this issue, a publicly available gas path diagnostic benchmarking problem has been created and made publicly available. This software tool, referred to as the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES), has been constructed based on feedback provided by individuals within the aircraft EHM community. It provides a standard benchmarking problem enabling users to develop, evaluate and compare diagnostic methods. This paper will present an overview of ProDiMES along with a description of four gas path diagnostic methods developed and applied to the problem. These methods, which include analytical and empirical diagnostic techniques, will be described and associated blind test case metric results will be presented and compared. Lessons learned along with recommendations for improving the public benchmarking processes will also be presented and discussed. [less ▲]

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See detailA Methodology to Improve the Robustness of Gas Turbine Engine Performance Monitoring Against Sensor Faults
Dewallef, Pierre ULg; Borguet, Sébastien ULg

in Journal of Engineering for Gas Turbines and Power (2013), 135(5),

For turbine engine performance monitoring purposes, system identification techniques are often used to adapt a turbine engine simulation model to some measurements performed while the engine is in service ... [more ▼]

For turbine engine performance monitoring purposes, system identification techniques are often used to adapt a turbine engine simulation model to some measurements performed while the engine is in service. Doing so, the simulation model is adapted through a set of so-called health parameters whose values are intended to represent a faithful image of the actual health condition of the engine. For sake of low computational burden, the problem of random errors contaminating the measurements is often considered to be zero-mean, white and Gaussian random variables. However, when a sensor fault occurs, the measurement errors no longer satisfy the Gaussian assumption and the results given by the system identification rapidly become unreliable. The present contribution is dedicated to the development of a diagnosis tool based on a Kalman filter whose structure is slightly modified in order to accommodate sensor malfunctions. The benefit in terms of the diagnostic reliability of the resulting tool is illustrated on several sensor faults that may be encountered on a current turbofan layout. [less ▲]

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See detailVariations on the Kalman filter for enhanced performance monitoring of gas turbine engines
Borguet, Sébastien ULg

Doctoral thesis (2012)

Since their advent in the 1940's, gas turbines have been used in a wide range of land, sea and air applications due to their high power density and reliability. In today's competitive market, gas turbine ... [more ▼]

Since their advent in the 1940's, gas turbines have been used in a wide range of land, sea and air applications due to their high power density and reliability. In today's competitive market, gas turbine operators need to optimise the dispatch availability (it i.e., minimise operational issues such as aborted take-offs or in-flight shutdowns) as well as the direct operating costs of their assets. Besides improvements in the design and manufacture processes, proactive maintenance practices, based on the actual condition of the turbine, enable the achievement of these objectives. Generating dependable information about the health condition of the gas turbine is a requisite for a successful implementation of condition-based maintenance. In this thesis, we focus on the assessment of the performance of the thermodynamic cycle, also known as Module Performance Analysis. The purpose of module performance analysis is to detect, isolate and quantify changes in engine module performance, described by so-called health parameters, on the basis of measurements collected along the gas-path of the engine. Generally, the health parameters are correcting factors on the efficiency and the flow capacity of the modules while the measurements are inter-component temperatures, pressures, shaft speeds and fuel flow. Module performance analysis can be cast as an estimation problem that is characterised by a number of difficulties such as non-linearity of the system and noise and bias in the measurements. Moreover the number of health parameters usually exceeds the number of gas-path measurements, making the estimation problem underdetermined. This thesis starts with a survey of the state-of-the-art in module performance analysis. We then propose enhancements to a monitoring tool for steady-state data developed by Dr. P. Dewallef during his thesis at the Turbomachinery Group. Specifically, the improvements concern the fault detection and isolation tasks, respectively handled by a hypothesis testing and a sparse estimator. As a complement, we define metrics for the selection and analysis of sensor--health parameter suites based on the Information Theory. In a second step, we investigate the feasibility and the benefit that could be expected from the processing of data collected during transient operation of a gas turbine. We also discuss the impact of modelling errors on the estimation procedure and propose a solution that makes the health assessment robust with respect to modelling errors. The theoretical developments are evaluated on the basis of simulated test-cases through a series of metrics that gauge the estimation accuracy and the performance of the fault detection and isolation modules. [less ▲]

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See detailConstrained sparse estimation for improved fault isolation
Borguet, Sébastien ULg; Léonard, Olivier ULg

in Journal of Engineering for Gas Turbines & Power (2011), 133(12), 121602

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See detailA study on engine health monitoring in the frequency domain
Borguet, Sébastien ULg; Henriksson, Mattias; McKelvey, Tomas et al

in Journal of Engineering for Gas Turbines & Power (2011), 133(08), 081604-1--081604-8

Most of the techniques developed to date for module performance analysis rely on steady-state measurements from a single operating point to evaluate the level of deterioration of an engine. One of the ... [more ▼]

Most of the techniques developed to date for module performance analysis rely on steady-state measurements from a single operating point to evaluate the level of deterioration of an engine. One of the major difficulties associated with this estimation problem comes from its underdetermined nature. It results from the fact that the number of health parameters exceeds the number of available sensors. Among the panel of remedies to this issue, a few authors have investigated the potential of using data collected during a transient operation of the engine. A major outcome of these studies is an improvement in the assessed health condition. The present study proposes a framework that formalizes this observation for a given class of input signals. The analysis is performed in the frequency domain, following the lines of system identification theory. More specifically, the mean-squared estimation error is shown to drastically decrease when using transient input signals. This study is conducted with an engine model representative of a commercial turbofan. [less ▲]

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See detailConstrained sparse estimation for improved fault isolation
Borguet, Sébastien ULg; Léonard, Olivier ULg

in Proceedings of ASME Turbo Expo 2011 (2011)

Least-squares-based methods are very popular in the jet engine community for health monitoring purpose. Their isolation capability can be improved by using a prior knowledge on the health parameters that ... [more ▼]

Least-squares-based methods are very popular in the jet engine community for health monitoring purpose. Their isolation capability can be improved by using a prior knowledge on the health parameters that better matches the expected pattern of the solution i.e., a sparse one as accidental faults impact at most one or two component(s) simultaneously. On the other hand, complimentary information about the feasible values of the health parameters can be derived in the form of constraints. The present contribution investigates the effect of the addition of such constraints on the performance of the sparse estimation tool. Due to its quadratic programming formulation, the constraints are integrated in a straightforward manner. Results obtained on a variety of fault conditions simulated with a commercial turbofan model show that the inclusion of constraints further enhance the isolation capability of the sparse estimator. In particular, the constraints help resolve a confusion issue between high pressure compressor and variable stator vanes faults. [less ▲]

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See detailAssessment of an anomaly detector for jet engine health monitoring
Borguet, Sébastien ULg; Léonard, Olivier ULg

in International Journal of Rotating Machinery (2011), 2011

The goal of module performance analysis is to reliably assess the health of the main components of an aircraft engine. A predictive maintenance strategy can leverage this information to increase ... [more ▼]

The goal of module performance analysis is to reliably assess the health of the main components of an aircraft engine. A predictive maintenance strategy can leverage this information to increase operability and safety as well as to reduce costs. Degradation undergone by an engine can be divided into gradual deterioration and accidental events. Kalman filters have proven very efficient at tracking progressive deterioration but are poor performers in the face of abrupt events. Adaptive estimation is considered as an appropriate solution to this deficiency. This paper reports the evaluation of the detection capability of an adaptive diagnosis tool on the basis of simulated scenarios that may be encountered during the operation of a commercial turbofan engine. The diagnosis tool 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 abrupt events. [less ▲]

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See detailA study on engine health monitoring in the frequency domain
Borguet, Sébastien ULg; Henriksson, Mattias; Mc Kelvey, Tomas et al

in Proceedings of the ASME Turbo Expo 2010 (2010, June)

Most of the techniques developed to date for module performance analysis rely on steady-state measurements from a single operating point to evaluate the level of deterioration of an engine. One of the ... [more ▼]

Most of the techniques developed to date for module performance analysis rely on steady-state measurements from a single operating point to evaluate the level of deterioration of an engine. One of the major difficulties associated with this estimation problem comes from its underdetermined nature. It results from the fact that the number of health parameters exceeds the number of available sensors. Among the panel of remedies to this issue, a few authors have investigated the potential of using data collected during a transient operation of the engine. A major outcome of these studies is an Improvement in the assessed health condition. The present contribution proposes a framework that formalises this observation for a given class of input signals. The analysis is performed in the frequency domain, following the lines of system identification theory. More specifically, the meansquared estimation error is shown to drastically decrease when using transient input signals. The study is conducted with an engine model representative of a commercial turbofan. [less ▲]

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See detailA Sparse Estimation Approach to Fault Isolation
Borguet, Sébastien ULg; Léonard, Olivier ULg

in Journal of Engineering for Gas Turbines & Power (2010), 132(2), 021601-1021601-7

Least-squares-based methods are very popular in the jet engine community for health monitoring purposes. In most practical situations, the number of health parameters exceeds the number of measurements ... [more ▼]

Least-squares-based methods are very popular in the jet engine community for health monitoring purposes. In most practical situations, the number of health parameters exceeds the number of measurements, making the estimation problem underdetermined. To address this issue, regularization adds a penalty term on the deviations of the health parameters. Generally, this term imposes a quadratic penalization on these deviations. A side effect of this technique is a relatively poor isolation capability. The latter feature can be improved by recognizing that abrupt faults impact at most one or two component(s) simultaneously. This translates mathematically into the search for a sparse solution. The present contribution reports the development of a fault isolation tool favoring sparse solutions. It is very efficiently implemented in the form of a quadratic program. As a validation procedure, the resulting algorithm is applied to a variety of fault conditions simulated with a generic commercial turbofan model. [less ▲]

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See detailComparison of adaptive filters for gas turbine performance monitoring
Borguet, Sébastien ULg; Léonard, Olivier ULg

in Journal of Computational & Applied Mathematics (2010), 234(7), 2202-2212

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 ... [more ▼]

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 recognizing 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 assumes a smooth evolution of the engine condition. The aim of this contribution is to compare two adaptive diagnosis tools that combine a Kalman filter and a secondary system that monitors the residuals. This auxiliary component implements on one hand a covariance matching scheme and on the other hand a generalised likelihood ratio test to improve the behaviour of the diagnosis tool with respect to abrupt faults. [less ▲]

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See detailFrom Manual to Model-Based Control of a Small Jet Engine
Léonard, Olivier ULg; Denis, François; Thomas, Jean-Philippe ULg et al

in Proceedings of the 19th ISABE Conference (2009, September)

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See detailDetection of abrupt events within a jet engine health monitoring procedure
Borguet, Sébastien ULg; Léonard, Olivier ULg

in Proceedings of the 19th ISABE Conference (2009, September)

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See detailA Sparse Estimation Approach to Fault Isolation
Borguet, Sébastien ULg; Léonard, Olivier ULg

in Proceedings of the ASME Turbo Expo 2009 (2009, June)

Least-squares-based methods are very popular in the jet engine community for health monitoring purpose. In most practical situations, the number of health parameters exceeds the number of measurements ... [more ▼]

Least-squares-based methods are very popular in the jet engine community for health monitoring purpose. In most practical situations, the number of health parameters exceeds the number of measurements, making the estimation problem underdetermined. To address this issue, regularisation adds a penalty term on the deviations of the health parameters. Generally, this term imposes a quadratic penalisation on these deviations. A side-effect of this technique is a relatively poor isolation capability. The latter feature can be improved by recognizing that abrupt faults impact at most one or two component(s) simultaneously. This translates mathematically into the search for a sparse solution. The present contribution reports the development of a fault isolation tool favouring sparse solutions. It is very efficiently implemented in the form of a quadratic program. As a validation procedure, the resulting algorithm is applied to a variety of fault conditions simulated with a generic commercial turbofan model. [less ▲]

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See detailCoupling principal component analysis and Kalman filtering algorithms for on-line aircraft engine diagnostics
Borguet, Sébastien ULg; Léonard, Olivier ULg

in Control Engineering Practice (2009), 17(4), 494-502

Engine health monitoring has been an area of intensive research for more than three decades. Numerous methods have been developed with the goal of performing an accurate assessment of the engine condition ... [more ▼]

Engine health monitoring has been an area of intensive research for more than three decades. Numerous methods have been developed with the goal of performing an accurate assessment of the engine condition. It is generally accepted that a practical implementation of a monitoring tool will rely on a combination of several techniques. In this framework, the present contribution proposes an original approach for coupling two diagnostic tools in order to enhance the capability of an engine health monitoring system. One tool is based on a principal component analysis scheme and the other is based on a Kalman filter technique. The three methodologies are compared and the benefit of the combined tool is demonstrated on simulated fault cases which can be expected in a commercial turbofan layout. (C) 2008 Elsevier Ltd. All rights reserved. [less ▲]

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See detailA Generalized Likelihood Ratio Test for Adaptive Gas Turbine Performance Monitoring
Borguet, Sébastien ULg; Léonard, Olivier ULg

in Journal of Engineering for Gas Turbines & Power (2009), 131(1),

Kalman filters are widely used in the turbine engine community for health monitoring purposes. This algorithm has proven its capability to track gradual deterioration with good accuracy. On the other hand ... [more ▼]

Kalman filters are widely used in the turbine engine community for health monitoring purposes. This algorithm has proven its capability to track gradual deterioration with good accuracy. On the other hand, its response to rapid deterioration is a long delay in recognizing the fault and/or a spread of the estimated fault on several components. The main reason for 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 generalized likelihood ratio test in order to detect and estimate all 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. [DOI: 10.1115/1.2967493] [less ▲]

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See detailTen Years of Experience With a Small Jet Engine as a Support for Education
Léonard, Olivier ULg; Borguet, Sébastien ULg; Thomas, Jean-Philippe ULg

in Journal of Engineering for Gas Turbines & Power (2009), 131(1),

In 1997 the Turbomachinery Group of the University of Liege decided to acquire a small jet engine to illustrate the courses in propulsion and to provide the students with the opportunity to get some ... [more ▼]

In 1997 the Turbomachinery Group of the University of Liege decided to acquire a small jet engine to illustrate the courses in propulsion and to provide the students with the opportunity to get some experience on data measurement, acquisition, and interpretation. Among others, the SR-30 engine from Turbine Technology Ltd. Chetek, WI was chosen. It consists of a single spool, single flow engine with a centrifugal compressor, a reversed combustion chamber an axial turbine, and a fixed convergent nozzle. This engine was installed on a test bench allowing for manual control and providing fuel and oil to the engine. The original setup included measurements of intercomponent pressure and temperatures, exhaust gas temperature, and rotational speed. Since then both the engine and the test bench have been deeply modified These modifications were led by a triple objective: the improvement and the enrichment of the measurement chain, the widening of the engine operational domain, and, last but not the least, the wish to offer appealing hands-on projects to the students. All these modifications were performed at the University of Liege and were conducted by the students as part of their Master theses. Several performance models of the engine were developed to support data validation and engine condition diagnostic. This paper summarizes the developments conducted with and by the students, and presents the experience that was gained by using this engine as a support for education. [less ▲]

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See detailA Quadratic Programming Framework for Constrained and Robust Jet Engine
Borguet, Sébastien ULg; Léonard, Olivier ULg

in EUCASS Advances in Aerospace Sciences : Propulsion Physics (2009)

Kalman filters are largely used in the jet engine community for condition monitoring purpose. This algorithm gives a good estimate of the engine condition provided that the residuals between the model ... [more ▼]

Kalman filters are largely used in the jet engine community for condition monitoring purpose. This algorithm gives a good estimate of the engine condition provided that the residuals between the model prediction and the measurements are zero-mean, Gaussian random variables. In the case of sensor faults, this assumption does not hold anymore and consequently the diagnosis is spoiled. This contribution presents a recursive estimation algorithm based on a Quadratic Programming formulation which provides robustness against sensor faults and allows constraints on the health parameters to be specified. The improvements in estimation accuracy brought by this new algorithm are illustrated by a series of typical test-cases that may be encountered on current turbofan engines. [less ▲]

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See detailAdaptive estimation algorithm for aircraft engine performance monitoring
Léonard, Olivier ULg; Borguet, Sébastien ULg; Dewallef, Pierre ULg

in Journal of Propulsion and Power (2008), 24(4), 763-769

In the frame of turbine engine performance monitoring, system identification procedures are often used to adapt a simulation model of the engine to some observed data through a set of so-called health ... [more ▼]

In the frame of turbine engine performance monitoring, system identification procedures are often used to adapt a simulation model of the engine to some observed data through a set of so-called health parameters. Doing so, the values of these health parameters are intended to represent the actual health condition of the engine. The Kalman filter has been widely used to achieve the identification procedure in real-time onboard applications. However, to achieve a proper filtering of the measurement noise, the health parameters are often assumed to vary in time relatively slowly, preventing any abrupt accidental events from being tracked effectively. This contribution presents a procedure called adaptive filtering. Based on a covariance-matching method, it is intended to automatically release the health parameters once an accidental event is detected. This enables the Kalman filter to deal with both continuous and abrupt fault conditions. [less ▲]

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See detailA way to deal with model-plant mismatch for a reliable diagnosis in transient operation
Borguet, Sébastien ULg; Dewallef, Pierre ULg; Léonard, Olivier ULg

in Journal of Engineering for Gas Turbines & Power (2008), 130(3),

Least-squares health parameter identification techniques, such as the Kalman filter, have been extensively used to solve diagnosis problems. Indeed, such methods give a good estimate provided that the ... [more ▼]

Least-squares health parameter identification techniques, such as the Kalman filter, have been extensively used to solve diagnosis problems. Indeed, such methods give a good estimate provided that the discrepancies between the model prediction and the measurements are Zero-mean, white, Gaussian random variables. In a turbine engine diagnosis, however this assumption does not always hold due to the presence of biases in the model. This is especially true for a transient operation. As a result, the estimated parameters tend to diverge from their actual values, which strongly degrades the diagnosis. The purpose of this contribution is to present a Kalman filter diagnosis tool where the model biases are treated as an additional random measurement error. The new methodology is tested on simulated transient data representative of a current turbofan engine configuration. While relatively simple to implement, the newly developed diagnosis tool exhibits a much better accuracy than the original Kalman filter in the presence of model biases. [less ▲]

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See detailThe Fisher Information Matrix as a Relevant Tool for Sensor Selection in Engine Health Monitoring
Borguet, Sébastien ULg; Léonard, Olivier ULg

in International Journal of Rotating Machinery (2008), 2008

Engine health monitoring has been an area of intensive research for many years. Numerous methods have been developed with the goal of determining a faithful picture of the engine condition. On the other ... [more ▼]

Engine health monitoring has been an area of intensive research for many years. Numerous methods have been developed with the goal of determining a faithful picture of the engine condition. On the other hand, the issue of sensor selection allowing an efficient diagnosis has received less attention from the community. The present contribution revisits the problem of sensor selection for engine performance monitoring within the scope of information theory. To this end, a metric that integrates the essential elements of the sensor selection problem is defined from the Fisher information matrix. An example application consisting in a commercial turbofan engine illustrates the enhancement that can be expected from a wise selection of the sensor set. [less ▲]

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