The flux weakening has become essential in Permanent Magnet Synchronous Motors (PMSMs) control to... more The flux weakening has become essential in Permanent Magnet Synchronous Motors (PMSMs) control to expand the speed range. So many works had been made in this topic last years, but, they are rare studies that take into account the magnetic saturation, and talk about optimization of flux weakening space. In this paper, an optimal flux weakening control scheme has been developed. The magnetic saturation is also considered. This method is based on two steps. The first one presents a specific identification scheme of PMSM. Second one shows how to build lookup tables for flux weakening control by using an optimization algorithm under constraints, based on obtained identification results. An example of illustration is discussed at each step of this study.
Procédé de surveillance d'un équipement de type actionneur électromécanique, l'équipement... more Procédé de surveillance d'un équipement de type actionneur électromécanique, l'équipement comportant un moteur électrique triphasé, le procédé de surveillances comprenant les étapes : - de réaliser des mesures de courants triphasés qui alimentent le moteur électrique triphasé ; - de projeter dans un repère de Park les mesures de courants triphasés (Iq) ; - de mettre en œuvre, sur le courant en quadrature, une méthode de décomposition modale empirique ensembliste (30) couplée à une méthode de séparation aveugle de source (40), pour obtenir des composantes sources (IC,..., IC10) ; - de sélectionner automatiquement parmi les composantes sources un premier ensemble de composantes sources (IC1,... IC9) sensibles à un premier défaut et un deuxième ensemble de composantes sources (IC9) sensibles à un deuxième défaut ; - de construire (60) un premier signal virtuel de défaut et un deuxième signal virtuel de défaut ; - d'extraire (70) du premier signal virtuel de défaut un premie...
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017
The automatic diagnosis of systems is essential in several industries such as aeronautics. This p... more The automatic diagnosis of systems is essential in several industries such as aeronautics. This paper introduces a method to diagnose systems with respect to the constraints of the aeronautics field: robustness and low computation costs. The proposed methodology is based on the combination of Support Vector Machine and Fuzzy Membership Functions (SVM-MBF). The distances, which are computed by the SVM, are fuzzified in order to give a degree of confidence in the classification. Besides, using SVM-MBF allows estimating the severity of a fault. The architecture of the proposed diagnosis system consists in putting in series one classifier to detect faults, with a set of classifiers, one per fault, to assess the severity. The method is applied to the diagnosis of inter-turn short-circuits of a Permanent Magnet Synchronous Machine (PMSM). The data come from measurements performed on a machine designed for aeronautics applications. The method is evaluated in terms of robustness and computation time by using cross validation. The results show the suitability of the methodology for aeronautics applications and to the path of onboard diagnosis algorithm.
2021 IEEE International Conference on Prognostics and Health Management (ICPHM), 2021
Electromechecanical actuators in the aerospace industry are gradually replacing hydraulic ones. I... more Electromechecanical actuators in the aerospace industry are gradually replacing hydraulic ones. In these circumstances, prognostics and health management are innovative frameworks to ensure better safety on board, especially in flight controls where jamming is dreaded. It allows the user to assess and predict system health in real–time. The first step is to collect temporal data from the monitored actuator and perform a data mining procedure to gain insight into its current health. Clustering encompasses several data-driven methods used to reveal patterns. However, getting a set of classes usually requires providing the algorithm with prior knowledge, such as the number of groups to seek. To avoid this drawback, we have developed a clustering algorithm using a deep neural network, as its core, to get the number of groups in data associated with their likelihood. Temporal sequences are reshaped into pictures to be fed into an artificially trained neural network: U-NET. The latter outputs segmented images from which one-dimensional information is extracted and filtered, without any need for parameter selection. A kernel density estimation finally transforms the signal into a candidate density. This new method provides a robust clustering result coupled with an empirical probability to label the times series. It lays the groundwork for future training of diagnosis and prognosis structures in the PHM framework.
Classification algorithms based on data mining tools show good performances for the automatic dia... more Classification algorithms based on data mining tools show good performances for the automatic diagnosis of systems. However, these performances degrade quickly when the database is not exhaustive. This happens, for example, when a new class appears. This class could correspond to a previous unknown fault or to an unknown combination of simultaneous faults. Described algorithm in this paper proposes a solution to this issue. It combines Support Vector Machine (SVM), fuzzy membership functions (mbf) and fuzzy information fusion. It results in the construction of a matrix of memberships to known classes U_class and a vector of membership to unknown classes U_others. Then, from these values, indicators of distance and ambiguity of the observations can be computed. These indicators allow setting a simple rejection rule with a threshold classifier. The algorithm is validated by using Cross-Validation (CV) on experimental data on an induction motor faults supplied by a voltage-source inverter. The results show the good performances of the proposed algorithms and its suitability for transportation systems like aircrafts.
This paper proposes an automated fault isolation and diagnostic chain for the health monitoring o... more This paper proposes an automated fault isolation and diagnostic chain for the health monitoring of a linear actuator composed of a roller screw driven by a permanent magnet synchronous motor. Four health conditions are considered and diagnosed: the healthy condition, a short circuit in the stator windings, a mechanical backlash in the roller screw, and the combination of both faults. In order to separate the fault signatures, empirical mode decomposition is applied to the motor current, followed by independent component analysis, automatic isolation of the fault signatures, and a classification step for the diagnosis. The novelty proposed consists of an automatic processing of the independent components to isolate the effects of the short-circuit from the effects of the backlash. This isolation step, in contrast to earlier works, requires no human intervention to select signals of interest, making it suitable to real-time onboard diagnostics. Furthermore, results show that independent component analysis occupies an important role in the diagnosis: its omission leads to a reduction in the diagnostic performance of the classifier as well as a reduction in measures of class separability.
2016 IEEE International Conference on Prognostics and Health Management (ICPHM), 2016
The condition based maintenance is an increasing challenge for flight control systems. In this pa... more The condition based maintenance is an increasing challenge for flight control systems. In this paper, a methodology for the diagnosis of a roller screw in an electromechanical actuator is proposed. As this component is critical, its diagnosis is essential to use it on aircrafts. The methodology is based on the extraction of features by identifying a model of the actuator. First, a specific waveform, made of increasing steps of speed, is run on the actuator. Then, the measurements are processed to reduce the noise and the bias of the different sensors. In order to accelerate the identification, an equivalent point is calculated for each step of the waveform. Then, the identification is realized and the identified parameters are gathered in a feature vector. Finally, a model including backlash and deformation of the stem is used to validate the approach and to generate a set of data. The aging is simulated by making assumptions on the evolution of parameters. Classification is made by using k-Nearest Neighbors (kNN). Performances of the algorithm on this application are evaluated in terms of precision and robustness.
In the next twenty years traffic aircraft will be doubled. Thus, avionic devices will become more... more In the next twenty years traffic aircraft will be doubled. Thus, avionic devices will become more and more electric and the aircrafts become lighter in order to save more fuel. Thus, the more electric aircraft will face a great challenge that of the predictive maintenance of its electrical equipments. A key component of these devices is the Permanent Magnet Synchronous Motor (PMSM). In this article we are interested in one of the most recurrent failure of electric motor, that of the inter-turn short circuit failure. The purpose of this study, therefore, is to develop an interturn short-circuit sensitive indicator. It’s based on a linear Kalman filter for a healthy model to estimate residual voltage drops in the rotor reference (d,q). The proposed study shows a high sensitive indicator to the inter-turn short-circuit fault even under external disturbances. As well, several features can result from it, especially the signal energy, spectral and statistical information, etc. These fea...
2007 IEEE International Fuzzy Systems Conference, 2007
ABSTRACT In this paper, stability conditions for a wide class of Takagi-Sugeno uncertain descript... more ABSTRACT In this paper, stability conditions for a wide class of Takagi-Sugeno uncertain descriptors are proposed. These are based on a quadratic Lyapunov candidate function. In order to solve the stability conditions with classical convex optimization algorithms, matrices transformations have been used to write these conditions in term of linear matrix inequality (LMI). A designed example illustrates the efficiency of the proposed approach.
2006 IEEE International Conference on Control Applications, 2006
This paper deals with the nonlinear control of a lower limbs isokinetic rehabilitation device bas... more This paper deals with the nonlinear control of a lower limbs isokinetic rehabilitation device based on a Takagi-Sugeno modeling. A parallel distributed compensation control law is used to stabilize the closed-loop system in the whole operational space. The human force applied to the device's arm is considered as an external disturbance to the system dynamics. To attenuate this disturbance, an
The flux weakening has become essential in Permanent Magnet Synchronous Motors (PMSMs) control to... more The flux weakening has become essential in Permanent Magnet Synchronous Motors (PMSMs) control to expand the speed range. So many works had been made in this topic last years, but, they are rare studies that take into account the magnetic saturation, and talk about optimization of flux weakening space. In this paper, an optimal flux weakening control scheme has been developed. The magnetic saturation is also considered. This method is based on two steps. The first one presents a specific identification scheme of PMSM. Second one shows how to build lookup tables for flux weakening control by using an optimization algorithm under constraints, based on obtained identification results. An example of illustration is discussed at each step of this study.
Procédé de surveillance d'un équipement de type actionneur électromécanique, l'équipement... more Procédé de surveillance d'un équipement de type actionneur électromécanique, l'équipement comportant un moteur électrique triphasé, le procédé de surveillances comprenant les étapes : - de réaliser des mesures de courants triphasés qui alimentent le moteur électrique triphasé ; - de projeter dans un repère de Park les mesures de courants triphasés (Iq) ; - de mettre en œuvre, sur le courant en quadrature, une méthode de décomposition modale empirique ensembliste (30) couplée à une méthode de séparation aveugle de source (40), pour obtenir des composantes sources (IC,..., IC10) ; - de sélectionner automatiquement parmi les composantes sources un premier ensemble de composantes sources (IC1,... IC9) sensibles à un premier défaut et un deuxième ensemble de composantes sources (IC9) sensibles à un deuxième défaut ; - de construire (60) un premier signal virtuel de défaut et un deuxième signal virtuel de défaut ; - d'extraire (70) du premier signal virtuel de défaut un premie...
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017
The automatic diagnosis of systems is essential in several industries such as aeronautics. This p... more The automatic diagnosis of systems is essential in several industries such as aeronautics. This paper introduces a method to diagnose systems with respect to the constraints of the aeronautics field: robustness and low computation costs. The proposed methodology is based on the combination of Support Vector Machine and Fuzzy Membership Functions (SVM-MBF). The distances, which are computed by the SVM, are fuzzified in order to give a degree of confidence in the classification. Besides, using SVM-MBF allows estimating the severity of a fault. The architecture of the proposed diagnosis system consists in putting in series one classifier to detect faults, with a set of classifiers, one per fault, to assess the severity. The method is applied to the diagnosis of inter-turn short-circuits of a Permanent Magnet Synchronous Machine (PMSM). The data come from measurements performed on a machine designed for aeronautics applications. The method is evaluated in terms of robustness and computation time by using cross validation. The results show the suitability of the methodology for aeronautics applications and to the path of onboard diagnosis algorithm.
2021 IEEE International Conference on Prognostics and Health Management (ICPHM), 2021
Electromechecanical actuators in the aerospace industry are gradually replacing hydraulic ones. I... more Electromechecanical actuators in the aerospace industry are gradually replacing hydraulic ones. In these circumstances, prognostics and health management are innovative frameworks to ensure better safety on board, especially in flight controls where jamming is dreaded. It allows the user to assess and predict system health in real–time. The first step is to collect temporal data from the monitored actuator and perform a data mining procedure to gain insight into its current health. Clustering encompasses several data-driven methods used to reveal patterns. However, getting a set of classes usually requires providing the algorithm with prior knowledge, such as the number of groups to seek. To avoid this drawback, we have developed a clustering algorithm using a deep neural network, as its core, to get the number of groups in data associated with their likelihood. Temporal sequences are reshaped into pictures to be fed into an artificially trained neural network: U-NET. The latter outputs segmented images from which one-dimensional information is extracted and filtered, without any need for parameter selection. A kernel density estimation finally transforms the signal into a candidate density. This new method provides a robust clustering result coupled with an empirical probability to label the times series. It lays the groundwork for future training of diagnosis and prognosis structures in the PHM framework.
Classification algorithms based on data mining tools show good performances for the automatic dia... more Classification algorithms based on data mining tools show good performances for the automatic diagnosis of systems. However, these performances degrade quickly when the database is not exhaustive. This happens, for example, when a new class appears. This class could correspond to a previous unknown fault or to an unknown combination of simultaneous faults. Described algorithm in this paper proposes a solution to this issue. It combines Support Vector Machine (SVM), fuzzy membership functions (mbf) and fuzzy information fusion. It results in the construction of a matrix of memberships to known classes U_class and a vector of membership to unknown classes U_others. Then, from these values, indicators of distance and ambiguity of the observations can be computed. These indicators allow setting a simple rejection rule with a threshold classifier. The algorithm is validated by using Cross-Validation (CV) on experimental data on an induction motor faults supplied by a voltage-source inverter. The results show the good performances of the proposed algorithms and its suitability for transportation systems like aircrafts.
This paper proposes an automated fault isolation and diagnostic chain for the health monitoring o... more This paper proposes an automated fault isolation and diagnostic chain for the health monitoring of a linear actuator composed of a roller screw driven by a permanent magnet synchronous motor. Four health conditions are considered and diagnosed: the healthy condition, a short circuit in the stator windings, a mechanical backlash in the roller screw, and the combination of both faults. In order to separate the fault signatures, empirical mode decomposition is applied to the motor current, followed by independent component analysis, automatic isolation of the fault signatures, and a classification step for the diagnosis. The novelty proposed consists of an automatic processing of the independent components to isolate the effects of the short-circuit from the effects of the backlash. This isolation step, in contrast to earlier works, requires no human intervention to select signals of interest, making it suitable to real-time onboard diagnostics. Furthermore, results show that independent component analysis occupies an important role in the diagnosis: its omission leads to a reduction in the diagnostic performance of the classifier as well as a reduction in measures of class separability.
2016 IEEE International Conference on Prognostics and Health Management (ICPHM), 2016
The condition based maintenance is an increasing challenge for flight control systems. In this pa... more The condition based maintenance is an increasing challenge for flight control systems. In this paper, a methodology for the diagnosis of a roller screw in an electromechanical actuator is proposed. As this component is critical, its diagnosis is essential to use it on aircrafts. The methodology is based on the extraction of features by identifying a model of the actuator. First, a specific waveform, made of increasing steps of speed, is run on the actuator. Then, the measurements are processed to reduce the noise and the bias of the different sensors. In order to accelerate the identification, an equivalent point is calculated for each step of the waveform. Then, the identification is realized and the identified parameters are gathered in a feature vector. Finally, a model including backlash and deformation of the stem is used to validate the approach and to generate a set of data. The aging is simulated by making assumptions on the evolution of parameters. Classification is made by using k-Nearest Neighbors (kNN). Performances of the algorithm on this application are evaluated in terms of precision and robustness.
In the next twenty years traffic aircraft will be doubled. Thus, avionic devices will become more... more In the next twenty years traffic aircraft will be doubled. Thus, avionic devices will become more and more electric and the aircrafts become lighter in order to save more fuel. Thus, the more electric aircraft will face a great challenge that of the predictive maintenance of its electrical equipments. A key component of these devices is the Permanent Magnet Synchronous Motor (PMSM). In this article we are interested in one of the most recurrent failure of electric motor, that of the inter-turn short circuit failure. The purpose of this study, therefore, is to develop an interturn short-circuit sensitive indicator. It’s based on a linear Kalman filter for a healthy model to estimate residual voltage drops in the rotor reference (d,q). The proposed study shows a high sensitive indicator to the inter-turn short-circuit fault even under external disturbances. As well, several features can result from it, especially the signal energy, spectral and statistical information, etc. These fea...
2007 IEEE International Fuzzy Systems Conference, 2007
ABSTRACT In this paper, stability conditions for a wide class of Takagi-Sugeno uncertain descript... more ABSTRACT In this paper, stability conditions for a wide class of Takagi-Sugeno uncertain descriptors are proposed. These are based on a quadratic Lyapunov candidate function. In order to solve the stability conditions with classical convex optimization algorithms, matrices transformations have been used to write these conditions in term of linear matrix inequality (LMI). A designed example illustrates the efficiency of the proposed approach.
2006 IEEE International Conference on Control Applications, 2006
This paper deals with the nonlinear control of a lower limbs isokinetic rehabilitation device bas... more This paper deals with the nonlinear control of a lower limbs isokinetic rehabilitation device based on a Takagi-Sugeno modeling. A parallel distributed compensation control law is used to stabilize the closed-loop system in the whole operational space. The human force applied to the device's arm is considered as an external disturbance to the system dynamics. To attenuate this disturbance, an
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