The Experts below are selected from a list of 88755 Experts worldwide ranked by ideXlab platform
Belkacem Ouldbouamama - One of the best experts on this subject based on the ideXlab platform.
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robust fault detection with interval valued uncertainties in bond Graph Framework
Control Engineering Practice, 2018Co-Authors: Mayank Shekhar Jha, G Dauphintanguy, Belkacem OuldbouamamaAbstract:Abstract This paper describes a novel formalism for modelling uncertain system parameters and measurements, as interval models in a Bond Graph (BG) modelling Framework. The main scientific interest remains in integrating the benefits of BG modelling technique and properties of Interval Analysis (IA), for efficient diagnosis of uncertain systems. Structural properties of Bond Graphs in Linear Fractional transformation (BG-LFT) are exploited to model interval-valued uncertainties over a BG model in order to form an uncertain BG. The inherent causal properties are exploited to generate interval-valued fault indicators. Then, various properties of IA are used to generate point valued residual and interval-valued thresholds. The latter must contain the point valued residuals under nominal system functioning. A systematic procedure is proposed for passive-type fault detection method which is robust to uncertain system parameters and measurements. The viability of the method is shown through experimental study of a steam generator system. The limitations associated with existing fault detection method based on BG-LFT are alleviated by the proposed approach. Moreover, it is shown that proposed approach generalizes the BG-LFT method. This work forms the initial step towards integrating interval analysis based capabilities in BG Framework for fault detection and health monitoring of uncertain systems.
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particle filter based integrated health monitoring in bond Graph Framework
2017Co-Authors: G Dauphintanguy, Belkacem OuldbouamamaAbstract:This chapter presents a holistic method to addresses the issue of health monitoring of system parameters in Bond Graph (BG). The advantages of BGs are integrated with Bayesian estimation techniques for efficient diagnostics and prog-nostics of faults. In particular, BG in Linear fractional transformations (LFT) are used for modelling the global uncertain system and sequential Monte Carlo method based Particle filters (PF) are used for estimation of state of health (SOH) and subsequent prediction of the remaining useful life (RUL). In this work, the method is described with respect to a single system parameter which is chosen as prognos-tic candidate. The prognostic candidate undergoes progressive degradation and its degradation model is assumed to be known a priori. The system operates in control feedback loop. The detection of degradation initiation is achieved using BG LFT based robust fault detection technique. The latter forms an efficient diagnostic module. PFs are exploited for efficient Bayesian inference of SOH of the prog-nostic candidate. Moreover, prognostics is achieved by assessment of RUL in probabilistic domain. The issue of prognostics is formulated as joint state-parameter estimation problem, a hybrid prognostic approach, wherein the fault model is constructed by considering the statistical degradation model of the prog-nostic candidate. The observation equation is constructed from nominal part of the BG-LFT derived Analytical Redundancy Relations (ARR). Various uncertainties which arise because of noise on ARR based measurements, degradation process, environmental conditions etc. are effectively managed by PF. This allows the production of effective predictions of the RUL of the prognostic candidate with suitable confidence bounds. The method is applied over a mechatronic system in real time and performance is assessed using suitable metrics.
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particle filter based hybrid prognostics of proton exchange membrane fuel cell in bond Graph Framework
Computers & Chemical Engineering, 2016Co-Authors: Mathieu Bressel, Belkacem Ouldbouamama, G DauphintanguyAbstract:Abstract This paper presents a holistic solution towards prognostics of industrial Proton Exchange Membrane Fuel Cell. It involves an efficient multi-energetic model suited for diagnostics and prognostics, developed using some specific properties of Bond Graph (BG) theory. The benefits of Particle Filters (PF) are integrated with the BG model derived fault indicators named Analytical Redundancy Relations, for prognostics of the Electrical-Electrochemical part. The hybrid prognostics involves statistical degradation model obtained using real degradation tests. Prognostics problem is formulated as the joint state-parameter estimation problem in PF Framework where estimations of state of health (SOH) is obtained in probabilistic domain. This in turn is used for prediction of Remaining Useful Life (RUL) under constant current as well as dynamic current solicitations. The SOH estimation and RUL prediction is obtained with very high accuracy and precise confidence bounds. Moreover, a comparative analysis with Extended Kalman Filter demonstrates the usefulness of PF.
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particle filter based hybrid prognostics for health monitoring of uncertain systems in bond Graph Framework
Mechanical Systems and Signal Processing, 2016Co-Authors: Mayank Shekhar Jha, G Dauphintanguy, Belkacem OuldbouamamaAbstract:The paper’s main objective is to address the problem of health monitoring of system parameters in Bond Graph (BG) modeling Framework, by exploiting its structural and causal properties. The system in feedback control loop is considered uncertain globally. Parametric uncertainty is modeled in interval form. The system parameter is undergoing degradation (prognostic candidate) and its degradation model is assumed to be known a priori. The detection of degradation commencement is done in a passive manner which involves interval valued robust adaptive thresholds over the nominal part of the uncertain BG-derived interval valued analytical redundancy relations (I-ARRs). The latter forms an efficient diagnostic module. The prognostics problem is cast as joint state-parameter estimation problem, a hybrid prognostic approach, wherein the fault model is constructed by considering the statistical degradation model of the system parameter (prognostic candidate). The observation equation is constructed from nominal part of the I-ARR. Using particle filter (PF) algorithms; the estimation of state of health (state of prognostic candidate) and associated hidden time-varying degradation progression parameters is achieved in probabilistic terms. A simplified variance adaptation scheme is proposed. Associated uncertainties which arise out of noisy measurements, parametric degradation process, environmental conditions etc. are effectively managed by PF. This allows the production of effective predictions of the remaining useful life of the prognostic candidate with suitable confidence bounds. The effectiveness of the novel methodology is demonstrated through simulations and experiments on a mechatronic system.
G Dauphintanguy - One of the best experts on this subject based on the ideXlab platform.
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robust fault detection with interval valued uncertainties in bond Graph Framework
Control Engineering Practice, 2018Co-Authors: Mayank Shekhar Jha, G Dauphintanguy, Belkacem OuldbouamamaAbstract:Abstract This paper describes a novel formalism for modelling uncertain system parameters and measurements, as interval models in a Bond Graph (BG) modelling Framework. The main scientific interest remains in integrating the benefits of BG modelling technique and properties of Interval Analysis (IA), for efficient diagnosis of uncertain systems. Structural properties of Bond Graphs in Linear Fractional transformation (BG-LFT) are exploited to model interval-valued uncertainties over a BG model in order to form an uncertain BG. The inherent causal properties are exploited to generate interval-valued fault indicators. Then, various properties of IA are used to generate point valued residual and interval-valued thresholds. The latter must contain the point valued residuals under nominal system functioning. A systematic procedure is proposed for passive-type fault detection method which is robust to uncertain system parameters and measurements. The viability of the method is shown through experimental study of a steam generator system. The limitations associated with existing fault detection method based on BG-LFT are alleviated by the proposed approach. Moreover, it is shown that proposed approach generalizes the BG-LFT method. This work forms the initial step towards integrating interval analysis based capabilities in BG Framework for fault detection and health monitoring of uncertain systems.
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particle filter based integrated health monitoring in bond Graph Framework
2017Co-Authors: G Dauphintanguy, Belkacem OuldbouamamaAbstract:This chapter presents a holistic method to addresses the issue of health monitoring of system parameters in Bond Graph (BG). The advantages of BGs are integrated with Bayesian estimation techniques for efficient diagnostics and prog-nostics of faults. In particular, BG in Linear fractional transformations (LFT) are used for modelling the global uncertain system and sequential Monte Carlo method based Particle filters (PF) are used for estimation of state of health (SOH) and subsequent prediction of the remaining useful life (RUL). In this work, the method is described with respect to a single system parameter which is chosen as prognos-tic candidate. The prognostic candidate undergoes progressive degradation and its degradation model is assumed to be known a priori. The system operates in control feedback loop. The detection of degradation initiation is achieved using BG LFT based robust fault detection technique. The latter forms an efficient diagnostic module. PFs are exploited for efficient Bayesian inference of SOH of the prog-nostic candidate. Moreover, prognostics is achieved by assessment of RUL in probabilistic domain. The issue of prognostics is formulated as joint state-parameter estimation problem, a hybrid prognostic approach, wherein the fault model is constructed by considering the statistical degradation model of the prog-nostic candidate. The observation equation is constructed from nominal part of the BG-LFT derived Analytical Redundancy Relations (ARR). Various uncertainties which arise because of noise on ARR based measurements, degradation process, environmental conditions etc. are effectively managed by PF. This allows the production of effective predictions of the RUL of the prognostic candidate with suitable confidence bounds. The method is applied over a mechatronic system in real time and performance is assessed using suitable metrics.
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particle filter based hybrid prognostics of proton exchange membrane fuel cell in bond Graph Framework
Computers & Chemical Engineering, 2016Co-Authors: Mathieu Bressel, Belkacem Ouldbouamama, G DauphintanguyAbstract:Abstract This paper presents a holistic solution towards prognostics of industrial Proton Exchange Membrane Fuel Cell. It involves an efficient multi-energetic model suited for diagnostics and prognostics, developed using some specific properties of Bond Graph (BG) theory. The benefits of Particle Filters (PF) are integrated with the BG model derived fault indicators named Analytical Redundancy Relations, for prognostics of the Electrical-Electrochemical part. The hybrid prognostics involves statistical degradation model obtained using real degradation tests. Prognostics problem is formulated as the joint state-parameter estimation problem in PF Framework where estimations of state of health (SOH) is obtained in probabilistic domain. This in turn is used for prediction of Remaining Useful Life (RUL) under constant current as well as dynamic current solicitations. The SOH estimation and RUL prediction is obtained with very high accuracy and precise confidence bounds. Moreover, a comparative analysis with Extended Kalman Filter demonstrates the usefulness of PF.
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particle filter based hybrid prognostics for health monitoring of uncertain systems in bond Graph Framework
Mechanical Systems and Signal Processing, 2016Co-Authors: Mayank Shekhar Jha, G Dauphintanguy, Belkacem OuldbouamamaAbstract:The paper’s main objective is to address the problem of health monitoring of system parameters in Bond Graph (BG) modeling Framework, by exploiting its structural and causal properties. The system in feedback control loop is considered uncertain globally. Parametric uncertainty is modeled in interval form. The system parameter is undergoing degradation (prognostic candidate) and its degradation model is assumed to be known a priori. The detection of degradation commencement is done in a passive manner which involves interval valued robust adaptive thresholds over the nominal part of the uncertain BG-derived interval valued analytical redundancy relations (I-ARRs). The latter forms an efficient diagnostic module. The prognostics problem is cast as joint state-parameter estimation problem, a hybrid prognostic approach, wherein the fault model is constructed by considering the statistical degradation model of the system parameter (prognostic candidate). The observation equation is constructed from nominal part of the I-ARR. Using particle filter (PF) algorithms; the estimation of state of health (state of prognostic candidate) and associated hidden time-varying degradation progression parameters is achieved in probabilistic terms. A simplified variance adaptation scheme is proposed. Associated uncertainties which arise out of noisy measurements, parametric degradation process, environmental conditions etc. are effectively managed by PF. This allows the production of effective predictions of the remaining useful life of the prognostic candidate with suitable confidence bounds. The effectiveness of the novel methodology is demonstrated through simulations and experiments on a mechatronic system.
Mayank Shekhar Jha - One of the best experts on this subject based on the ideXlab platform.
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robust fault detection with interval valued uncertainties in bond Graph Framework
Control Engineering Practice, 2018Co-Authors: Mayank Shekhar Jha, G Dauphintanguy, Belkacem OuldbouamamaAbstract:Abstract This paper describes a novel formalism for modelling uncertain system parameters and measurements, as interval models in a Bond Graph (BG) modelling Framework. The main scientific interest remains in integrating the benefits of BG modelling technique and properties of Interval Analysis (IA), for efficient diagnosis of uncertain systems. Structural properties of Bond Graphs in Linear Fractional transformation (BG-LFT) are exploited to model interval-valued uncertainties over a BG model in order to form an uncertain BG. The inherent causal properties are exploited to generate interval-valued fault indicators. Then, various properties of IA are used to generate point valued residual and interval-valued thresholds. The latter must contain the point valued residuals under nominal system functioning. A systematic procedure is proposed for passive-type fault detection method which is robust to uncertain system parameters and measurements. The viability of the method is shown through experimental study of a steam generator system. The limitations associated with existing fault detection method based on BG-LFT are alleviated by the proposed approach. Moreover, it is shown that proposed approach generalizes the BG-LFT method. This work forms the initial step towards integrating interval analysis based capabilities in BG Framework for fault detection and health monitoring of uncertain systems.
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particle filter based hybrid prognostics for health monitoring of uncertain systems in bond Graph Framework
Mechanical Systems and Signal Processing, 2016Co-Authors: Mayank Shekhar Jha, G Dauphintanguy, Belkacem OuldbouamamaAbstract:The paper’s main objective is to address the problem of health monitoring of system parameters in Bond Graph (BG) modeling Framework, by exploiting its structural and causal properties. The system in feedback control loop is considered uncertain globally. Parametric uncertainty is modeled in interval form. The system parameter is undergoing degradation (prognostic candidate) and its degradation model is assumed to be known a priori. The detection of degradation commencement is done in a passive manner which involves interval valued robust adaptive thresholds over the nominal part of the uncertain BG-derived interval valued analytical redundancy relations (I-ARRs). The latter forms an efficient diagnostic module. The prognostics problem is cast as joint state-parameter estimation problem, a hybrid prognostic approach, wherein the fault model is constructed by considering the statistical degradation model of the system parameter (prognostic candidate). The observation equation is constructed from nominal part of the I-ARR. Using particle filter (PF) algorithms; the estimation of state of health (state of prognostic candidate) and associated hidden time-varying degradation progression parameters is achieved in probabilistic terms. A simplified variance adaptation scheme is proposed. Associated uncertainties which arise out of noisy measurements, parametric degradation process, environmental conditions etc. are effectively managed by PF. This allows the production of effective predictions of the remaining useful life of the prognostic candidate with suitable confidence bounds. The effectiveness of the novel methodology is demonstrated through simulations and experiments on a mechatronic system.
Mathieu Bressel - One of the best experts on this subject based on the ideXlab platform.
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particle filter based hybrid prognostics of proton exchange membrane fuel cell in bond Graph Framework
Computers & Chemical Engineering, 2016Co-Authors: Mathieu Bressel, Belkacem Ouldbouamama, G DauphintanguyAbstract:Abstract This paper presents a holistic solution towards prognostics of industrial Proton Exchange Membrane Fuel Cell. It involves an efficient multi-energetic model suited for diagnostics and prognostics, developed using some specific properties of Bond Graph (BG) theory. The benefits of Particle Filters (PF) are integrated with the BG model derived fault indicators named Analytical Redundancy Relations, for prognostics of the Electrical-Electrochemical part. The hybrid prognostics involves statistical degradation model obtained using real degradation tests. Prognostics problem is formulated as the joint state-parameter estimation problem in PF Framework where estimations of state of health (SOH) is obtained in probabilistic domain. This in turn is used for prediction of Remaining Useful Life (RUL) under constant current as well as dynamic current solicitations. The SOH estimation and RUL prediction is obtained with very high accuracy and precise confidence bounds. Moreover, a comparative analysis with Extended Kalman Filter demonstrates the usefulness of PF.
Marlis Ontivero Ortega - One of the best experts on this subject based on the ideXlab platform.
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Automated Discrimination of Brain Pathological State Attending to Complex Structural Brain Network Properties: The Shiverer Mutant Mouse Case
2013Co-Authors: Yasser Iturria-medina, Ro Pérez Fernández, Pedro Valdés Hernández, Lorna García Pentón, Erick J. Canales-rodríguez, Lester Melie-garcia, Agustin Lage Castellanos, Marlis Ontivero OrtegaAbstract:Neuroimaging classification procedures between normal and pathological subjects are sparse and highly dependent of an expert’s clinical criterion. Here, we aimed to investigate whether possible brain structural network differences in the shiverer mouse mutant, a relevant animal model of myelin related diseases, can reflect intrinsic individual brain properties that allow the automatic discrimination between the shiverer and normal subjects. Common structural networks properties between shiverer (C3Fe.SWV Mbp shi /Mbp shi, n = 6) and background control (C3HeB.FeJ, n = 6) mice are estimated and compared by means of three diffusion weighted MRI (DW-MRI) fiber tractoGraphy algorithms and a Graph Framework. Firstly, we found that brain networks of control group are significantly more clustered, modularized, efficient and optimized than those of the shiverer group, which presented significantly increased characteristic path length. These results are in line with previous structural/functional complex brain networks analysis that have revealed topologic differences and brain network randomization associated to specific states of human brain pathology. In addition, by means of network measures spatial representations and discrimination analysis, we show that it is possible to classify with high accuracy to which group each subject belongs, providing also a probability value of being a normal or shiverer subject as an individual anatomica
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Automated Discrimination of Brain Pathological State Attending to Complex Structural Brain Network Properties: The Shiverer Mutant Mouse Case
PLoS ONE, 2011Co-Authors: Yasser Iturria-medina, Pedro Valdés Hernández, Lorna García Pentón, Erick J. Canales-rodríguez, Lester Melie-garcia, Agustin Lage Castellanos, Alejandro Pérez Fernández, Marlis Ontivero OrtegaAbstract:Neuroimaging classification procedures between normal and pathological subjects are sparse and highly dependent of an expert's clinical criterion. Here, we aimed to investigate whether possible brain structural network differences in the shiverer mouse mutant, a relevant animal model of myelin related diseases, can reflect intrinsic individual brain properties that allow the automatic discrimination between the shiverer and normal subjects. Common structural networks properties between shiverer (C3Fe.SWV Mbp(shi)/Mbp(shi), n = 6) and background control (C3HeB.FeJ, n = 6) mice are estimated and compared by means of three diffusion weighted MRI (DW-MRI) fiber tractoGraphy algorithms and a Graph Framework. Firstly, we found that brain networks of control group are significantly more clustered, modularized, efficient and optimized than those of the shiverer group, which presented significantly increased characteristic path length. These results are in line with previous structural/functional complex brain networks analysis that have revealed topologic differences and brain network randomization associated to specific states of human brain pathology. In addition, by means of network measures spatial representations and discrimination analysis, we show that it is possible to classify with high accuracy to which group each subject belongs, providing also a probability value of being a normal or shiverer subject as an individual anatomical classifier. The obtained correct predictions (e.g., around 91.6-100%) and clear spatial subdivisions between control and shiverer mice, suggest that there might exist specific network subspaces corresponding to specific brain disorders, supporting also the point of view that complex brain network analyses constitutes promising tools in the future creation of interpretable imaging biomarkers.
