SAM
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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sun, 11 Aug 2024 05:22:46 GMT2024-08-11T05:22:46ZA Spherical Cross-Channel Algorithm for Binaural Sound Localization
http://hdl.handle.net/10985/7400
A Spherical Cross-Channel Algorithm for Binaural Sound Localization
VINA, Carlos; ARGENTIERI, Sylvain; RÉBILLAT, Marc
This paper proposes a sound localization algorithm inspired by a cross-channel algorithm first studied by MacDonald et. al in 2008. The original algorithm assumes that the Head Related Transfer Functions (HRTFs) of the robotic head under study are precisely known, which is rarely the case in practice. Following the idea that any head is more or less spherical, the above assumption is relaxed by using HRTFs computed using a simple spherical head model with the same head radius as the robot head. In order to evaluate the proposed approach in realistic noisy conditions, an isotropic noise field is also computed and a precise definition of the Signal to Noise Ratio (SNR) in a binaural context is outlined. All these theoretical developments are finally assessed with simulated and experimental signals. Despite its simplicity, the proposed approach appears to be robust to noise and to provide reliable sound localization estimations in the frontal azimuthal plane.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/74002013-01-01T00:00:00ZVINA, CarlosARGENTIERI, SylvainRÉBILLAT, MarcThis paper proposes a sound localization algorithm inspired by a cross-channel algorithm first studied by MacDonald et. al in 2008. The original algorithm assumes that the Head Related Transfer Functions (HRTFs) of the robotic head under study are precisely known, which is rarely the case in practice. Following the idea that any head is more or less spherical, the above assumption is relaxed by using HRTFs computed using a simple spherical head model with the same head radius as the robot head. In order to evaluate the proposed approach in realistic noisy conditions, an isotropic noise field is also computed and a precise definition of the Signal to Noise Ratio (SNR) in a binaural context is outlined. All these theoretical developments are finally assessed with simulated and experimental signals. Despite its simplicity, the proposed approach appears to be robust to noise and to provide reliable sound localization estimations in the frontal azimuthal plane.Probabilistic Decision Trees using SVM for Multi-class Classification
http://hdl.handle.net/10985/7401
Probabilistic Decision Trees using SVM for Multi-class Classification
URIBE, Juan Sebastian; BOUAMAMA, Karima; PENGOV, Marco; MECHBAL, Nazih; RÉBILLAT, Marc
In the automotive repairing backdrop, retrieving from previously solved incident the database features that could support and speed up the diagnostic is of great usefulness. This decision helping process should give a fixed number of the more relevant diagnostic classified in a likelihood sense. It is a probabilistic multi-class classification problem. This paper describes an original classification technique, the Probabilistic Decision Tree (PDT) producing a posteriori probabilities in a multi-class context. It is based on a Binary Decision Tree (BDT) with Probabilistic Support Vector Machine classifier (PSVM). At each node of the tree, a bi-class SVM along with a sigmoid function are trained to give a probabilistic classification output. For each branch, the outputs of all the nodes composing the branch are combined to lead to a complete evaluation of the probability when reaching the final leaf (representing the class associated to the branch). To illustrate the effectiveness of PDTs, they are tested on benchmark datasets and results are compared with other existing approaches.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/74012013-01-01T00:00:00ZURIBE, Juan SebastianBOUAMAMA, KarimaPENGOV, MarcoMECHBAL, NazihRÉBILLAT, MarcIn the automotive repairing backdrop, retrieving from previously solved incident the database features that could support and speed up the diagnostic is of great usefulness. This decision helping process should give a fixed number of the more relevant diagnostic classified in a likelihood sense. It is a probabilistic multi-class classification problem. This paper describes an original classification technique, the Probabilistic Decision Tree (PDT) producing a posteriori probabilities in a multi-class context. It is based on a Binary Decision Tree (BDT) with Probabilistic Support Vector Machine classifier (PSVM). At each node of the tree, a bi-class SVM along with a sigmoid function are trained to give a probabilistic classification output. For each branch, the outputs of all the nodes composing the branch are combined to lead to a complete evaluation of the probability when reaching the final leaf (representing the class associated to the branch). To illustrate the effectiveness of PDTs, they are tested on benchmark datasets and results are compared with other existing approaches.Nonlinear structural damage detection based on cascade of Hammerstein models
http://hdl.handle.net/10985/8235
Nonlinear structural damage detection based on cascade of Hammerstein models
HAJRYA, Rafik; MECHBAL, Nazih; RÉBILLAT, Marc
Structural damages can result in nonlinear dynamical signatures that can significantly enhance their detection. An original nonlinear damage detection approach is proposed that is based on a cascade of Hammerstein models representation of the structure. This model is estimated by means of the Exponential Sine Sweep Method from only one measurement. On the basis of this estimated model, the linear and nonlinear parts of the output are estimated, and two damage indexes (DIs) are proposed. The first DI is built as the ratio of the energy contained in the nonlinear part of an output versus the energy contained in its linear part. The second DI is the angle between the subspaces obtained from the nonlinear parts of two set of outputs after a principal component analysis. The sensitivity of the proposed DIs to the presence of damages as well as their robustness to noise are assessed numerically on spring-mass-damper structures and experimentally on actual composite plates with surface-mounted PZT-elements. Results demonstrate the effectiveness of the proposed method to detect a damage in nonlinear structures and in the presence of noise.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/82352014-01-01T00:00:00ZHAJRYA, RafikMECHBAL, NazihRÉBILLAT, MarcStructural damages can result in nonlinear dynamical signatures that can significantly enhance their detection. An original nonlinear damage detection approach is proposed that is based on a cascade of Hammerstein models representation of the structure. This model is estimated by means of the Exponential Sine Sweep Method from only one measurement. On the basis of this estimated model, the linear and nonlinear parts of the output are estimated, and two damage indexes (DIs) are proposed. The first DI is built as the ratio of the energy contained in the nonlinear part of an output versus the energy contained in its linear part. The second DI is the angle between the subspaces obtained from the nonlinear parts of two set of outputs after a principal component analysis. The sensitivity of the proposed DIs to the presence of damages as well as their robustness to noise are assessed numerically on spring-mass-damper structures and experimentally on actual composite plates with surface-mounted PZT-elements. Results demonstrate the effectiveness of the proposed method to detect a damage in nonlinear structures and in the presence of noise.Wave damping and evanescence: how to combine the spatial and temporal visions of the same problem? 1
http://hdl.handle.net/10985/10854
Wave damping and evanescence: how to combine the spatial and temporal visions of the same problem? 1
BALMES, Etienne; ARLAUD, Elodie; RÉBILLAT, Marc
It is proposed to analyze the forced response of periodic structures using a 2D Fourier transform using continuous time and discrete space. The simple example of compression waves is used to show that this response can be used to define poles in the wavenumber domain corresponding to evanescent waves or poles in the frequency domain corresponding to damped periodic modes. Link with classical computational methods based on FEM models of cells was done for both the periodic solution and wave based approach (SAFE, WFE). Two examples are analyzed in more detail: a simple train track model exhibiting a band-gap and the more complex case of a honeycomb panel where cell wall bending occurs within the band of interest.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10985/108542016-01-01T00:00:00ZBALMES, EtienneARLAUD, ElodieRÉBILLAT, MarcIt is proposed to analyze the forced response of periodic structures using a 2D Fourier transform using continuous time and discrete space. The simple example of compression waves is used to show that this response can be used to define poles in the wavenumber domain corresponding to evanescent waves or poles in the frequency domain corresponding to damped periodic modes. Link with classical computational methods based on FEM models of cells was done for both the periodic solution and wave based approach (SAFE, WFE). Two examples are analyzed in more detail: a simple train track model exhibiting a band-gap and the more complex case of a honeycomb panel where cell wall bending occurs within the band of interest.Verification and Validation of Structural Health Monitoring Algorithms: A Maturation Procedure
http://hdl.handle.net/10985/10040
Verification and Validation of Structural Health Monitoring Algorithms: A Maturation Procedure
HMAD, Ouadie; FENDZI, Claude; MECHBAL, Nazih; RÉBILLAT, Marc
Structural Health Monitoring (SHM) system offers new approaches to interrogate the integrity of structures. However, their reliability has still to be demonstrated an quantified to enable confidence transition from R&D to field implementation. In general, SHM algorithms performances are illustrated by topography study but it is not sufficient in a reliability assessment context. In the sense, that there is no quantification of the performance. To address this key issue, a dedicated maturation procedure is proposed in this paper. It is strongly inspired from the six sigma procedure for processes improvement to gradually improve SHM algorithms in order to reach the required maturity level. This paper presents the application of this procedure to a damage SHM localization algorithm as case study. To address this issue, finite element models and experimentation on the monitored structure have been used. It is concluded with a need of a new specific SHM algorithm intrinsic maturity scale. These maturity scales can be defined with respect to the functions of the considered SHM algorithm and the type of the used data.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/100402015-01-01T00:00:00ZHMAD, OuadieFENDZI, ClaudeMECHBAL, NazihRÉBILLAT, MarcStructural Health Monitoring (SHM) system offers new approaches to interrogate the integrity of structures. However, their reliability has still to be demonstrated an quantified to enable confidence transition from R&D to field implementation. In general, SHM algorithms performances are illustrated by topography study but it is not sufficient in a reliability assessment context. In the sense, that there is no quantification of the performance. To address this key issue, a dedicated maturation procedure is proposed in this paper. It is strongly inspired from the six sigma procedure for processes improvement to gradually improve SHM algorithms in order to reach the required maturity level. This paper presents the application of this procedure to a damage SHM localization algorithm as case study. To address this issue, finite element models and experimentation on the monitored structure have been used. It is concluded with a need of a new specific SHM algorithm intrinsic maturity scale. These maturity scales can be defined with respect to the functions of the considered SHM algorithm and the type of the used data.Automatic Damage Quantification Using Signal Based And Nonlinear Model Based Damage Sensitive Features
http://hdl.handle.net/10985/12043
Automatic Damage Quantification Using Signal Based And Nonlinear Model Based Damage Sensitive Features
GHRIB, Meriem; VERMOT DES ROCHES, Guillaume; MECHBAL, Nazih; RÉBILLAT, Marc
Structural Health Monitoring (SHM) can be de ned as the process of acquiring and analyzing data from on-board sensors to evaluate the health of a structure. Classically, an SHM process can be performed in four steps: detection, localization, classi cation and quanti cation. This paper addresses damage quanti cation issue as a classi cation problem whereby each class corresponds to a certain damage extent. Starting from the assumption that damage causes a structure to exhibit nonlinear response, we investigate whether the use of nonlinear model based features increases classi cation performance. A support Vector Machine (SVM) is used to perform multi-class classi cation task. Two types of features are used as inputs to the SVM algorithm: Signal Based Features (SBF) and Nonlinear Model Based Features (NMBF). SBF are rooted in a direct use of response signals and do not consider any underlying model of the test structure. NMBF are computed based on parallel Hammerstein models which are identi ed with an Exponential Sine Sweep (ESS) signal. A study of the sensitivity of classi cation performance to the noise contained in output signals is also conducted. Dimension reduction of features vector using Principal Component Analysis (PCA) is carried out in order to nd out if it allows robustifying the quanti cation process suggested in this work. Simulation results on a cantilever beam with a bilinear torsion spring sti ness are considered for demonstration. Results show that by introducing NMBF, classi cation performance is improved. Furthermore, PCA allows for higher recognition rates while reducing features vector dimension. However, classi ers trained on NMBF or on principal components appear to be more sensitive to output noise than those trained on SBF.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/120432017-01-01T00:00:00ZGHRIB, MeriemVERMOT DES ROCHES, GuillaumeMECHBAL, NazihRÉBILLAT, MarcStructural Health Monitoring (SHM) can be de ned as the process of acquiring and analyzing data from on-board sensors to evaluate the health of a structure. Classically, an SHM process can be performed in four steps: detection, localization, classi cation and quanti cation. This paper addresses damage quanti cation issue as a classi cation problem whereby each class corresponds to a certain damage extent. Starting from the assumption that damage causes a structure to exhibit nonlinear response, we investigate whether the use of nonlinear model based features increases classi cation performance. A support Vector Machine (SVM) is used to perform multi-class classi cation task. Two types of features are used as inputs to the SVM algorithm: Signal Based Features (SBF) and Nonlinear Model Based Features (NMBF). SBF are rooted in a direct use of response signals and do not consider any underlying model of the test structure. NMBF are computed based on parallel Hammerstein models which are identi ed with an Exponential Sine Sweep (ESS) signal. A study of the sensitivity of classi cation performance to the noise contained in output signals is also conducted. Dimension reduction of features vector using Principal Component Analysis (PCA) is carried out in order to nd out if it allows robustifying the quanti cation process suggested in this work. Simulation results on a cantilever beam with a bilinear torsion spring sti ness are considered for demonstration. Results show that by introducing NMBF, classi cation performance is improved. Furthermore, PCA allows for higher recognition rates while reducing features vector dimension. However, classi ers trained on NMBF or on principal components appear to be more sensitive to output noise than those trained on SBF.Estimation of the temperature field on a composite fan cowl using the static capacity of surface-mounted piezoceramic transducers
http://hdl.handle.net/10985/12044
Estimation of the temperature field on a composite fan cowl using the static capacity of surface-mounted piezoceramic transducers
LIZE, Emmanuel; MECHBAL, Nazih; RÉBILLAT, Marc
The influence of temperature on SHM (Structural Health Monitoring) systems using guided waves is a major problem for their industrial deployment. One of the most used and cheapest SHM process developed in aeronautic context is based on piezoelectric transducers mounted on the monitored structure. Several methods are then used to assess the presence of damage. A popular one is based on tracking changes in the static capacity of the transducers: it is an efficient damage indicator in close area surrounding the device and is often used as a fault diagnosis of the transducer itself. However, monitoring decision are robustified with temperature sensors also mounted on the structure, adding wires and signal processing post-treatments. In this article, the static capacity is used to determine the temperature on each lead zirconate titanate transducer (PZT). By imparting this additional functionality to PZT devices, supplementary instrumentation is not necessary and an estimation of the entire temperature field of the structure is obtained. The original proposed approach is tested experimentally on a on a small composite plate and then on a large real part of an A380 composite nacelle. The results show that the temperature field on the structure can be estimated with a precision of ±5 °C using a linear regression between static capacity and temperature
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/120442017-01-01T00:00:00ZLIZE, EmmanuelMECHBAL, NazihRÉBILLAT, MarcThe influence of temperature on SHM (Structural Health Monitoring) systems using guided waves is a major problem for their industrial deployment. One of the most used and cheapest SHM process developed in aeronautic context is based on piezoelectric transducers mounted on the monitored structure. Several methods are then used to assess the presence of damage. A popular one is based on tracking changes in the static capacity of the transducers: it is an efficient damage indicator in close area surrounding the device and is often used as a fault diagnosis of the transducer itself. However, monitoring decision are robustified with temperature sensors also mounted on the structure, adding wires and signal processing post-treatments. In this article, the static capacity is used to determine the temperature on each lead zirconate titanate transducer (PZT). By imparting this additional functionality to PZT devices, supplementary instrumentation is not necessary and an estimation of the entire temperature field of the structure is obtained. The original proposed approach is tested experimentally on a on a small composite plate and then on a large real part of an A380 composite nacelle. The results show that the temperature field on the structure can be estimated with a precision of ±5 °C using a linear regression between static capacity and temperatureDamage indexes comparison for the structural health monitoring of a stiffened composite plate
http://hdl.handle.net/10985/12042
Damage indexes comparison for the structural health monitoring of a stiffened composite plate
MECHBAL, Nazih; RÉBILLAT, Marc
Stiffened composite structures are very appealing in aeronautic applications due to their unique stiffness to mass ratio. However, they are also prone to various and complex damage scenario (stiffener debonding, impact damage...) and to complex wave propagation phenomena due to the presence of the stiffener. Consequently, autonomous monitoring of such structure is still a real issue. The process of monitoring in real-time a structure is referred to structural health monitoring (SHM) and consists of several steps: damage detection, localization, classification, and quantification. The focus is put here on the damage detection step of SHM. To detect damages, stiffened composite structures are equipped with piezoelectric elements that act both as sensors and actuators. A database at the unknown (and possibly damaged state) is then compared to a healthy reference database. Several damage indexes (DIs) designed for detection are extracted from this comparison. The SHM process classically relies on four sequential steps: damage detection, localization, classification, and quantification. The most critical step of such process is the damage detection step since it is the first one and because performances of the following steps depend on it. A common method to design such a detector consists in relying on a statistical characterization of the damage indexes available in the healthy behavior of the structure. On the basis of this information, a decision threshold can then be computed in order to achieve a desired probability of false alarm (PFA). In this paper, the performances of these DIs with respect to damage detection in a stiffened composite plate are studied. Results show that DIs based on energy consideration perform better than the ones based on cross-correlation. Furthermore Fourier-transform based DIs appear to be insensitive to the presence of damage in such structure.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/120422017-01-01T00:00:00ZMECHBAL, NazihRÉBILLAT, MarcStiffened composite structures are very appealing in aeronautic applications due to their unique stiffness to mass ratio. However, they are also prone to various and complex damage scenario (stiffener debonding, impact damage...) and to complex wave propagation phenomena due to the presence of the stiffener. Consequently, autonomous monitoring of such structure is still a real issue. The process of monitoring in real-time a structure is referred to structural health monitoring (SHM) and consists of several steps: damage detection, localization, classification, and quantification. The focus is put here on the damage detection step of SHM. To detect damages, stiffened composite structures are equipped with piezoelectric elements that act both as sensors and actuators. A database at the unknown (and possibly damaged state) is then compared to a healthy reference database. Several damage indexes (DIs) designed for detection are extracted from this comparison. The SHM process classically relies on four sequential steps: damage detection, localization, classification, and quantification. The most critical step of such process is the damage detection step since it is the first one and because performances of the following steps depend on it. A common method to design such a detector consists in relying on a statistical characterization of the damage indexes available in the healthy behavior of the structure. On the basis of this information, a decision threshold can then be computed in order to achieve a desired probability of false alarm (PFA). In this paper, the performances of these DIs with respect to damage detection in a stiffened composite plate are studied. Results show that DIs based on energy consideration perform better than the ones based on cross-correlation. Furthermore Fourier-transform based DIs appear to be insensitive to the presence of damage in such structure.Parallel Hammerstein Models Identification using Sine Sweeps and the Welch Method
http://hdl.handle.net/10985/12377
Parallel Hammerstein Models Identification using Sine Sweeps and the Welch Method
BOUTILLON, Xavier; CORTEEL, Etienne; ROGGERONE, Vincent; RÉBILLAT, Marc
Linearity is a common assumption for many real life systems. But in many cases, the nonlinear behavior of systems cannot be ignored and has to be modeled and estimated. Among the various classes of nonlinear models present in the literature, Parallel Hammertein Models (PHM) are interesting as they are at the same time easy to understand as well as to estimate when using exponential sine sweeps (ESS) based methods. However, the classical EES- based estimation procedure for PHM relies on a very speci c input signal (ESS), which limits its use in practice. A method is proposed here based on the Welch method that allows for PHM estimation with arbitrary sine sweeps (ASS) which are a much broader class of input signals than ESS. Results show that for various ASS, the proposed method provides results that are in excellent agreement with the ones obtained with the classical ESS method.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/123772017-01-01T00:00:00ZBOUTILLON, XavierCORTEEL, EtienneROGGERONE, VincentRÉBILLAT, MarcLinearity is a common assumption for many real life systems. But in many cases, the nonlinear behavior of systems cannot be ignored and has to be modeled and estimated. Among the various classes of nonlinear models present in the literature, Parallel Hammertein Models (PHM) are interesting as they are at the same time easy to understand as well as to estimate when using exponential sine sweeps (ESS) based methods. However, the classical EES- based estimation procedure for PHM relies on a very speci c input signal (ESS), which limits its use in practice. A method is proposed here based on the Welch method that allows for PHM estimation with arbitrary sine sweeps (ASS) which are a much broader class of input signals than ESS. Results show that for various ASS, the proposed method provides results that are in excellent agreement with the ones obtained with the classical ESS method.Damage type classification based on structures nonlinear dynamical signature
http://hdl.handle.net/10985/10036
Damage type classification based on structures nonlinear dynamical signature
BAKIR, Myriam; MECHBAL, Nazih; RÉBILLAT, Marc
Structural damages result in nonlinear dynamical signatures that significantly help for their monitoring. A damage type classification approach is proposed here that is based on a parallel Hammerstein models representation of the structure estimated by means of the Exponential Sine Sweep Method. This estimation method has been here extended to take into account for input signal amplitude which was not the case before. On the basis of these estimated models, three amplitude dependent damage indexes are built: one that monitors the shift of the resonance frequency of the structure, another the ratio of nonlinear versus linear energy in the output signal, and a last one the ratio of the energy coming from odd nonlinearities to the energy coming from even nonlinearities in the output signal. The slopes of these amplitude-dependent DIs are then used as coordinates to place the damaged structure under study within a three-dimensional space. A single mass-spring-damper system is considered to illustrate the ability of this space to classify different types of damage. Four types of damage with different severities are simulated through different spring nonlinearities: bilinear stiffness, dead zone, saturation, and Coulomb friction. For all severities, the four types of damage are extremely well separated within the proposed three-dimensional space, thus highlighting its high potential for classification purposes.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/100362015-01-01T00:00:00ZBAKIR, MyriamMECHBAL, NazihRÉBILLAT, MarcStructural damages result in nonlinear dynamical signatures that significantly help for their monitoring. A damage type classification approach is proposed here that is based on a parallel Hammerstein models representation of the structure estimated by means of the Exponential Sine Sweep Method. This estimation method has been here extended to take into account for input signal amplitude which was not the case before. On the basis of these estimated models, three amplitude dependent damage indexes are built: one that monitors the shift of the resonance frequency of the structure, another the ratio of nonlinear versus linear energy in the output signal, and a last one the ratio of the energy coming from odd nonlinearities to the energy coming from even nonlinearities in the output signal. The slopes of these amplitude-dependent DIs are then used as coordinates to place the damaged structure under study within a three-dimensional space. A single mass-spring-damper system is considered to illustrate the ability of this space to classify different types of damage. Four types of damage with different severities are simulated through different spring nonlinearities: bilinear stiffness, dead zone, saturation, and Coulomb friction. For all severities, the four types of damage are extremely well separated within the proposed three-dimensional space, thus highlighting its high potential for classification purposes.