The Experts below are selected from a list of 915 Experts worldwide ranked by ideXlab platform
Demba Diallo - One of the best experts on this subject based on the ideXlab platform.
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jensen shannon divergence for non destructive Incipient Crack detection and estimation
IEEE Access, 2020Co-Authors: Xiaoxia Zhang, Claude Delpha, Demba DialloAbstract:Nowadays industrial process needs more and more accurate nondestructive procedures for material Crack detection and diagnosis. Early detection in that cases are very challenging issues: more the Cracks are Incipient and higher is the difficulty for its detection and estimation. Indeed, these Incipient Cracks which cause non-obvious changes in sensor measurements needs to be properly detected and estimated. For conductive materials measurement based on impedance maps obtained from Eddy Current Testing (ECT) are used but the presence of environmental noise can mask the Crack information and induce missed detection and false estimation. In this paper, we highlight the limitation of classical techniques and address this problem using a methodology based on wavelet transform and Jensen-Shannon divergence in the framework of Noisy Independent Component Analysis (NICA). For our work, the impedance maps are considered as a mixture information. Then, source signals containing the fault features are obtained by the application of the Independent Component Analysis regarding the noise. A wavelet decomposition is then used and operates as a noise reduction operation. Jensen-Shannon (JSD) divergence is then proposed for the Crack detection. Thanks to a theoretical derivation, the fault severity estimation is obtained. The performances are evaluated and the superiority validated regarding other techniques already used in the literature. The performances limits are evaluated for noise varying environments and the optimal diagnosis is obtained for several Incipient Cracks.
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Nondestructive Incipient Crack Detection based on Wavelet and Jensen-Shannon Divergence in the NICA framework
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, 2019Co-Authors: Xiaoxia Zhang, Claude Delpha, Demba DialloAbstract:The nondestructive Crack detection is an important issue in industrial engineering. However, the detection of Incipient Cracks that can cause non obvious changes in the conductive material impedance map is difficult. In our paper, we propose a new method based on wavelet and Jensen-Shannon divergence in the framework of Noisy Independent Component Analysis (NICA) to address this problem. The source signals with fault features are obtained by the application of the Independent Component Analysis regarding the noise. Then, the wavelet decomposition is considered as the denoising method to partially reduce the noise influence. The Jensen-Shannon divergence(JSD) which has been proved as an efficient Incipient fault detection algorithm in previous works is used here for Incipient Crack detection. The detection performances of the proposed method is compared with the ones obtained with the Kullback-Leibler divergence often proposed in the literature.
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statistical approach for nondestructive Incipient Crack detection and characterization using kullback leibler divergence
IEEE Transactions on Reliability, 2016Co-Authors: Jinane Harmouche, Claude Delpha, Demba Diallo, Yann Le BihanAbstract:This paper is a contribution to the detection and characterisation of small Cracks using Eddy Current Testing in the Non Destructive Evaluation framework. Small Cracks are considered as Incipient faults defined as gradual faults whose signature is weak and concealed by the noise. They are characterized by high signal to noise ratio and low fault to noise ratio. The detection and diagnosis of such faults is still an open challenge. For complex systems, model-based Incipient fault detection and diagnosis (FDD) methods usually fail because of the inaccuracy of the model to describe all the phenomena and their interactions. Data-driven methods using statistical features are very promising as long as historical data are available. However in the case of Incipient faults, there is not a significant variation of a single feature. The fault signature lies in the global variation of the signal properties. The proposed method relies on the Kullback-Leibler Divergence (KLD) as a nonparametric fault indicator. It measures the slight dissimilarities between the probability density functions of the current signal compared to the faultless or healthy one. Through experimental results, the KLD exhibits a higher sensitivity than the usual statistical features for the detection of small Cracks (with dimensions in the order of 0.1 mm) realized in a nickel-based superalloy plate. Moreover, the detection is done with zero missed detection probability. Furthermore, the fault severity is assessed through the characteristics of the Crack (surface, length, and depth). In the principal component analysis framework, the analysis of four statistical features (KLD, mean, variance, and maximum) dependency to the excitation frequency allows to discriminating among the Cracks.
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Statistical Approach for Nondestructive Incipient Crack Detection and Characterization Using Kullback-Leibler Divergence
IEEE Transactions on Reliability, 2016Co-Authors: Jinane Harmouche, Claude Delpha, Demba Diallo, Yann Le BihanAbstract:This paper is a contribution to the detection and characterisation of small Cracks using Eddy Current Testing in the Non Destructive Evaluation framework. Small Cracks are considered as Incipient faults defined as gradual faults whose signature is weak and concealed by the noise. They are characterized by high signal to noise ratio and low fault to noise ratio. The detection and diagnosis of such faults is still an open challenge. For complex systems, model-based Incipient fault detection and diagnosis (FDD) methods usually fail because of the inaccuracy of the model to describe all the phenomena and their interactions. Data-driven methods using statistical features are very promising as long as historical data are available. However in the case of Incipient faults, there is not a significant variation of a single feature. The fault signature lies in the global variation of the signal properties. The proposed method relies on the Kullback-Leibler Divergence (KLD) as a nonparametric fault indicator. It measures the slight dissimilarities between the probability density functions of the current signal compared to the faultless or healthy one. Through experimental results, the KLD exhibits a higher sensitivity than the usual statistical features for the detection of small Cracks (with dimensions in the order of 0.1 mm) realized in a nickel-based superalloy plate. Moreover, the detection is done with zero missed detection probability. Furthermore, the fault severity is assessed through the characteristics of the Crack (surface, length, and depth). In the principal component analysis framework, the analysis of four statistical features (KLD, mean, variance, and maximum) dependency to the excitation frequency allows to discriminating among the Cracks.
Claude Delpha - One of the best experts on this subject based on the ideXlab platform.
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jensen shannon divergence for non destructive Incipient Crack detection and estimation
IEEE Access, 2020Co-Authors: Xiaoxia Zhang, Claude Delpha, Demba DialloAbstract:Nowadays industrial process needs more and more accurate nondestructive procedures for material Crack detection and diagnosis. Early detection in that cases are very challenging issues: more the Cracks are Incipient and higher is the difficulty for its detection and estimation. Indeed, these Incipient Cracks which cause non-obvious changes in sensor measurements needs to be properly detected and estimated. For conductive materials measurement based on impedance maps obtained from Eddy Current Testing (ECT) are used but the presence of environmental noise can mask the Crack information and induce missed detection and false estimation. In this paper, we highlight the limitation of classical techniques and address this problem using a methodology based on wavelet transform and Jensen-Shannon divergence in the framework of Noisy Independent Component Analysis (NICA). For our work, the impedance maps are considered as a mixture information. Then, source signals containing the fault features are obtained by the application of the Independent Component Analysis regarding the noise. A wavelet decomposition is then used and operates as a noise reduction operation. Jensen-Shannon (JSD) divergence is then proposed for the Crack detection. Thanks to a theoretical derivation, the fault severity estimation is obtained. The performances are evaluated and the superiority validated regarding other techniques already used in the literature. The performances limits are evaluated for noise varying environments and the optimal diagnosis is obtained for several Incipient Cracks.
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Nondestructive Incipient Crack Detection based on Wavelet and Jensen-Shannon Divergence in the NICA framework
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, 2019Co-Authors: Xiaoxia Zhang, Claude Delpha, Demba DialloAbstract:The nondestructive Crack detection is an important issue in industrial engineering. However, the detection of Incipient Cracks that can cause non obvious changes in the conductive material impedance map is difficult. In our paper, we propose a new method based on wavelet and Jensen-Shannon divergence in the framework of Noisy Independent Component Analysis (NICA) to address this problem. The source signals with fault features are obtained by the application of the Independent Component Analysis regarding the noise. Then, the wavelet decomposition is considered as the denoising method to partially reduce the noise influence. The Jensen-Shannon divergence(JSD) which has been proved as an efficient Incipient fault detection algorithm in previous works is used here for Incipient Crack detection. The detection performances of the proposed method is compared with the ones obtained with the Kullback-Leibler divergence often proposed in the literature.
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statistical approach for nondestructive Incipient Crack detection and characterization using kullback leibler divergence
IEEE Transactions on Reliability, 2016Co-Authors: Jinane Harmouche, Claude Delpha, Demba Diallo, Yann Le BihanAbstract:This paper is a contribution to the detection and characterisation of small Cracks using Eddy Current Testing in the Non Destructive Evaluation framework. Small Cracks are considered as Incipient faults defined as gradual faults whose signature is weak and concealed by the noise. They are characterized by high signal to noise ratio and low fault to noise ratio. The detection and diagnosis of such faults is still an open challenge. For complex systems, model-based Incipient fault detection and diagnosis (FDD) methods usually fail because of the inaccuracy of the model to describe all the phenomena and their interactions. Data-driven methods using statistical features are very promising as long as historical data are available. However in the case of Incipient faults, there is not a significant variation of a single feature. The fault signature lies in the global variation of the signal properties. The proposed method relies on the Kullback-Leibler Divergence (KLD) as a nonparametric fault indicator. It measures the slight dissimilarities between the probability density functions of the current signal compared to the faultless or healthy one. Through experimental results, the KLD exhibits a higher sensitivity than the usual statistical features for the detection of small Cracks (with dimensions in the order of 0.1 mm) realized in a nickel-based superalloy plate. Moreover, the detection is done with zero missed detection probability. Furthermore, the fault severity is assessed through the characteristics of the Crack (surface, length, and depth). In the principal component analysis framework, the analysis of four statistical features (KLD, mean, variance, and maximum) dependency to the excitation frequency allows to discriminating among the Cracks.
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Statistical Approach for Nondestructive Incipient Crack Detection and Characterization Using Kullback-Leibler Divergence
IEEE Transactions on Reliability, 2016Co-Authors: Jinane Harmouche, Claude Delpha, Demba Diallo, Yann Le BihanAbstract:This paper is a contribution to the detection and characterisation of small Cracks using Eddy Current Testing in the Non Destructive Evaluation framework. Small Cracks are considered as Incipient faults defined as gradual faults whose signature is weak and concealed by the noise. They are characterized by high signal to noise ratio and low fault to noise ratio. The detection and diagnosis of such faults is still an open challenge. For complex systems, model-based Incipient fault detection and diagnosis (FDD) methods usually fail because of the inaccuracy of the model to describe all the phenomena and their interactions. Data-driven methods using statistical features are very promising as long as historical data are available. However in the case of Incipient faults, there is not a significant variation of a single feature. The fault signature lies in the global variation of the signal properties. The proposed method relies on the Kullback-Leibler Divergence (KLD) as a nonparametric fault indicator. It measures the slight dissimilarities between the probability density functions of the current signal compared to the faultless or healthy one. Through experimental results, the KLD exhibits a higher sensitivity than the usual statistical features for the detection of small Cracks (with dimensions in the order of 0.1 mm) realized in a nickel-based superalloy plate. Moreover, the detection is done with zero missed detection probability. Furthermore, the fault severity is assessed through the characteristics of the Crack (surface, length, and depth). In the principal component analysis framework, the analysis of four statistical features (KLD, mean, variance, and maximum) dependency to the excitation frequency allows to discriminating among the Cracks.
Yann Le Bihan - One of the best experts on this subject based on the ideXlab platform.
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statistical approach for nondestructive Incipient Crack detection and characterization using kullback leibler divergence
IEEE Transactions on Reliability, 2016Co-Authors: Jinane Harmouche, Claude Delpha, Demba Diallo, Yann Le BihanAbstract:This paper is a contribution to the detection and characterisation of small Cracks using Eddy Current Testing in the Non Destructive Evaluation framework. Small Cracks are considered as Incipient faults defined as gradual faults whose signature is weak and concealed by the noise. They are characterized by high signal to noise ratio and low fault to noise ratio. The detection and diagnosis of such faults is still an open challenge. For complex systems, model-based Incipient fault detection and diagnosis (FDD) methods usually fail because of the inaccuracy of the model to describe all the phenomena and their interactions. Data-driven methods using statistical features are very promising as long as historical data are available. However in the case of Incipient faults, there is not a significant variation of a single feature. The fault signature lies in the global variation of the signal properties. The proposed method relies on the Kullback-Leibler Divergence (KLD) as a nonparametric fault indicator. It measures the slight dissimilarities between the probability density functions of the current signal compared to the faultless or healthy one. Through experimental results, the KLD exhibits a higher sensitivity than the usual statistical features for the detection of small Cracks (with dimensions in the order of 0.1 mm) realized in a nickel-based superalloy plate. Moreover, the detection is done with zero missed detection probability. Furthermore, the fault severity is assessed through the characteristics of the Crack (surface, length, and depth). In the principal component analysis framework, the analysis of four statistical features (KLD, mean, variance, and maximum) dependency to the excitation frequency allows to discriminating among the Cracks.
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Statistical Approach for Nondestructive Incipient Crack Detection and Characterization Using Kullback-Leibler Divergence
IEEE Transactions on Reliability, 2016Co-Authors: Jinane Harmouche, Claude Delpha, Demba Diallo, Yann Le BihanAbstract:This paper is a contribution to the detection and characterisation of small Cracks using Eddy Current Testing in the Non Destructive Evaluation framework. Small Cracks are considered as Incipient faults defined as gradual faults whose signature is weak and concealed by the noise. They are characterized by high signal to noise ratio and low fault to noise ratio. The detection and diagnosis of such faults is still an open challenge. For complex systems, model-based Incipient fault detection and diagnosis (FDD) methods usually fail because of the inaccuracy of the model to describe all the phenomena and their interactions. Data-driven methods using statistical features are very promising as long as historical data are available. However in the case of Incipient faults, there is not a significant variation of a single feature. The fault signature lies in the global variation of the signal properties. The proposed method relies on the Kullback-Leibler Divergence (KLD) as a nonparametric fault indicator. It measures the slight dissimilarities between the probability density functions of the current signal compared to the faultless or healthy one. Through experimental results, the KLD exhibits a higher sensitivity than the usual statistical features for the detection of small Cracks (with dimensions in the order of 0.1 mm) realized in a nickel-based superalloy plate. Moreover, the detection is done with zero missed detection probability. Furthermore, the fault severity is assessed through the characteristics of the Crack (surface, length, and depth). In the principal component analysis framework, the analysis of four statistical features (KLD, mean, variance, and maximum) dependency to the excitation frequency allows to discriminating among the Cracks.
Raphael Pesci - One of the best experts on this subject based on the ideXlab platform.
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local behavior of an aisi 304 stainless steel submitted to in situ biaxial loading in sem
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2017Co-Authors: Celia Caer, Raphael PesciAbstract:Abstract The microstructural response of a coarse grained AISI 304 stainless steel submitted to biaxial tensile loading was investigated using SEM and X-ray diffraction. The specimen geometry was designed to allow for biaxial stress state and Incipient Crack in the center of the active part under biaxial tensile loading. This complex loading was performed step by step by a micromachine fitting into a SEM chamber. At each loading step FSD pictures and EBSD measurements were carried out to study the microstructural evolution of the alloy, namely grain rotations and misorientations, stress-induced martensite formation and Crack propagation. According to their initial orientation, grains are found to behave differently under loading. Approximately 60% of grains are shown to reorient to the [110] Z orientation under biaxial tensile loading, whereas the 40% left undergo high plastic deformation. EBSD and XRD measurements respectively performed under loading and on the post mortem specimen highlighted the formation of about 4% of martensite.
M G Alexander - One of the best experts on this subject based on the ideXlab platform.
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chloride induced corrosion of steel in Cracked concrete part ii corrosion rate prediction models
Cement and Concrete Research, 2016Co-Authors: Mike Otieno, H Beushausen, M G AlexanderAbstract:Chloride-induced corrosion rate (i sub corr) prediction models for reinforced concrete (RC) structures in the marine tidal zone that incorporate the influence of Crack width (w sub cr), cover (c) and concrete quality are proposed. Parallel corrosion experiments were carried out for 2¼ years by exposing one half of 210 beam specimens (120 × 130 × 375 mm long) to accelerated laboratory corrosion (cyclic wetting and drying) while the other half underwent natural corrosion in the tidal zone. Experimental variables were w sub cr (0, Incipient Crack, 0.4, 0.7 mm), c (20, 40 mm), binder type (PC, PC/GGBS, PC/FA) and w/b ratio (0.40, 0.55). The two proposed models (one each for accelerated and natural i sub corr) can aid not only in quantifying the propagation phase, but also provide a novel way to select c, w sub cr and concrete quality.
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chloride induced corrosion of steel in Cracked concrete part i experimental studies under accelerated and natural marine environments
Cement and Concrete Research, 2016Co-Authors: Mike Otieno, H Beushausen, M G AlexanderAbstract:Parallel corrosion experiments were carried out for 2¼ years by exposing one half of 210 beam specimens (120 × 130 × 375 mm long) to accelerated laboratory corrosion (cyclic wetting and drying) while the other half underwent natural corrosion in a marine tidal zone. Experimental variables were Crack width w sub cr (0, Incipient Crack, 0.4, 0.7 mm), cover c (20, 40 mm), binder type (PC, PC/GGBS, PC/FA) and w/b ratio (0.40, 0.55). Results show that corrosion rate (i sub corr) was affected by the experimental variables in the following manner: i sub corr increased with increase in Crack width, and decreased with increase in concrete quality and cover depth. The results also show that the corrosion performance of concretes in the field under natural corrosion cannot be inferred from its performance in the laboratory under accelerated corrosion. Other factors such as corrosion process should be taken into account.
