This paper deals with ultrasonic imaging in a nondestructive evaluation (NDE) context. In particular, we are focused on the inspection of coarse-grained steels having an heterogeneous composition that creates structural noise in the ultrasonic signals and images. The standard way to beamform the acquired ultrasonic data is by delay-and-sum (DAS). This method is fast but suffers from low signal-to-noise ratio (SNR) for coarse-grained steel inspection. In this paper, we propose to adapt a coherence-based beamformer called pDAS from the medical imaging community. pDAS beamforming is based on DAS structure but includes p-root and p-power before and after summations, respectively. It results in an enhancement of the coherent summation of signals that improves both resolution and contrast. Coherence-based beamformers are known to enhance information whose acoustic response correlates with geometrical information, that is why they decrease grating lobes and side lobes, specular echoes, reconstruction artifacts and noise due to multiple scattering. In this paper, the pDAS beamformer is proposed for two common acquisition schemes employed in NDE that are plane wave imaging (PWI) and full matrix capture (FMC). The beamformers have been efficiently implemented for parallel computing on graphics processing unit (GPU) in a context of real-time imaging and fast part scanning in NDE. First, experimental results are presented from an austenitic-ferritic sample from the power generation industry that contains side drilled holes (SDH) with diameter 0.4mm at several depths. pDAS (for p from 2 to 5) shows improvements in terms of SNR and resolution compared to standard DAS, both in PWI and FMC modalities. We also show that the computation cost of pDAS is equivalent to DAS. A real application on a sample containing a fatigue crack connected to the backwall is exposed. We show that pDAS beamformer can enhance crack response compared to grains, but it also decreases unwanted information such as backwall specular echoes and reconstruction artifacts.
{"title":"Nonlinear Beamforming Based on Amplitude Coherence Applied to Ultrasonic Imaging of Coarse-Grained Steels","authors":"E. Carcreff, N. Laroche, F. Varray, B. Nicolas","doi":"10.1115/1.4056898","DOIUrl":"https://doi.org/10.1115/1.4056898","url":null,"abstract":"\u0000 This paper deals with ultrasonic imaging in a nondestructive evaluation (NDE) context. In particular, we are focused on the inspection of coarse-grained steels having an heterogeneous composition that creates structural noise in the ultrasonic signals and images. The standard way to beamform the acquired ultrasonic data is by delay-and-sum (DAS). This method is fast but suffers from low signal-to-noise ratio (SNR) for coarse-grained steel inspection. In this paper, we propose to adapt a coherence-based beamformer called pDAS from the medical imaging community. pDAS beamforming is based on DAS structure but includes p-root and p-power before and after summations, respectively. It results in an enhancement of the coherent summation of signals that improves both resolution and contrast. Coherence-based beamformers are known to enhance information whose acoustic response correlates with geometrical information, that is why they decrease grating lobes and side lobes, specular echoes, reconstruction artifacts and noise due to multiple scattering. In this paper, the pDAS beamformer is proposed for two common acquisition schemes employed in NDE that are plane wave imaging (PWI) and full matrix capture (FMC). The beamformers have been efficiently implemented for parallel computing on graphics processing unit (GPU) in a context of real-time imaging and fast part scanning in NDE. First, experimental results are presented from an austenitic-ferritic sample from the power generation industry that contains side drilled holes (SDH) with diameter 0.4mm at several depths. pDAS (for p from 2 to 5) shows improvements in terms of SNR and resolution compared to standard DAS, both in PWI and FMC modalities. We also show that the computation cost of pDAS is equivalent to DAS. A real application on a sample containing a fatigue crack connected to the backwall is exposed. We show that pDAS beamformer can enhance crack response compared to grains, but it also decreases unwanted information such as backwall specular echoes and reconstruction artifacts.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"6 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77597753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Capriotti, Luis Waldo Escalona Galvis, A. Spada
As structures increase in complexity, in the use of high-performing materials and designs, their health assessment becomes increasingly challenging. Ultrasonic guided waves (UGWs) have shown to be very promising in the inspection of large (i.e. aerospace components) attenuating (i.e. composite materials) structures and have been successfully employed for damage detection in a variety of fields. The intrinsic complex nature of UGWs, due to their dispersive behavior, combined with the structural complexity of the applications, though, makes the interpretation of UGW inspections very challenging. Numerical simulations of UGW propagation become crucial to this end and have been addressed with fully numerical, semi-analytical and hybrid approaches. The capability of predicting UGW scattering can inform experimental testing in optimizing the sensitivity of UGW inspections to specific waveguides and defects, and in interpreting the acquired data for the non-destructive identification and quantification of damages. In this work, an improved computational tool for UGW scattering predictions is presented. The approach relies on the Global-Local method and leverages the efficiency of the semi-analytical finite element (SAFE) method and the parallelized implementation of the coupled solution. 2D applications of the Global-Local approach for UGW scattering predictions in composite structures over a wide range of frequencies will be presented, together with the demonstration of the improved computational performance. The computational efficiency promises feasible and reliable UGWs predictions in multi-layered complex assemblies and different damage scenarios, and enables virtual UGWs inspections and future integration in NDE testing.
{"title":"Improved Global-Local Method for Ultrasonic Guided Wave Scattering Predictions in Composite Waveguides and Defects","authors":"M. Capriotti, Luis Waldo Escalona Galvis, A. Spada","doi":"10.1115/1.4056897","DOIUrl":"https://doi.org/10.1115/1.4056897","url":null,"abstract":"\u0000 As structures increase in complexity, in the use of high-performing materials and designs, their health assessment becomes increasingly challenging. Ultrasonic guided waves (UGWs) have shown to be very promising in the inspection of large (i.e. aerospace components) attenuating (i.e. composite materials) structures and have been successfully employed for damage detection in a variety of fields. The intrinsic complex nature of UGWs, due to their dispersive behavior, combined with the structural complexity of the applications, though, makes the interpretation of UGW inspections very challenging. Numerical simulations of UGW propagation become crucial to this end and have been addressed with fully numerical, semi-analytical and hybrid approaches. The capability of predicting UGW scattering can inform experimental testing in optimizing the sensitivity of UGW inspections to specific waveguides and defects, and in interpreting the acquired data for the non-destructive identification and quantification of damages. In this work, an improved computational tool for UGW scattering predictions is presented. The approach relies on the Global-Local method and leverages the efficiency of the semi-analytical finite element (SAFE) method and the parallelized implementation of the coupled solution. 2D applications of the Global-Local approach for UGW scattering predictions in composite structures over a wide range of frequencies will be presented, together with the demonstration of the improved computational performance. The computational efficiency promises feasible and reliable UGWs predictions in multi-layered complex assemblies and different damage scenarios, and enables virtual UGWs inspections and future integration in NDE testing.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"80 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83325355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Volker, J. Vrolijk, Egon Merks-Swolfs, Dennis van der Burg, Maurits van der Heiden, Q. Martina
Composite materials are becoming more popular in the aerospace industry, because of their physical properties. In quality assurance and in-service inspection, there is a need for fast, non-contact, high quality, non-destructive inspection techniques. The most common approach is to perform the inspection using water-coupled high frequency transducers. Full wavefield techniques are promising to replace the conventional inspection approach. However, these are currently performed by a laser vibrometer setup, which has drawbacks. As an alternative, a low-cost MEMS sensor array and dedicated processing scheme are presented enabling fast inspection of large samples. This inspection approach uses a piezoelectric actuator to excite the composite or metallic part with Lamb waves. An array of MEMS sensors records the energy that radiates into the surrounding air. A dedicated processing scheme will translate the measured wavefield into a thickness map of the inspected part. For composite parts, material's anisotropy needs to be taken into account for accurate thickness mapping. In principle all relevant defects show up as local thickness reductions. The results in this paper are obtained with a MEMS-sensor array of 128 elements capable of detecting ultrasound up to 250 kHz at a typical stand-off distance of 100 mm. Defects up to 6 mm in diameter could be detected in thick panels, and defects as small as 2.5 mm could be detected in thin panels. A full-size fuselage experiment shows that the method is also suited for fast inspection of large inspection areas.
{"title":"Non-contact MEMS-Sensor array Inspection of Composites and Metallic Parts Using Lamb Waves","authors":"A. Volker, J. Vrolijk, Egon Merks-Swolfs, Dennis van der Burg, Maurits van der Heiden, Q. Martina","doi":"10.1115/1.4056896","DOIUrl":"https://doi.org/10.1115/1.4056896","url":null,"abstract":"\u0000 Composite materials are becoming more popular in the aerospace industry, because of their physical properties. In quality assurance and in-service inspection, there is a need for fast, non-contact, high quality, non-destructive inspection techniques. The most common approach is to perform the inspection using water-coupled high frequency transducers. Full wavefield techniques are promising to replace the conventional inspection approach. However, these are currently performed by a laser vibrometer setup, which has drawbacks. As an alternative, a low-cost MEMS sensor array and dedicated processing scheme are presented enabling fast inspection of large samples. This inspection approach uses a piezoelectric actuator to excite the composite or metallic part with Lamb waves. An array of MEMS sensors records the energy that radiates into the surrounding air. A dedicated processing scheme will translate the measured wavefield into a thickness map of the inspected part. For composite parts, material's anisotropy needs to be taken into account for accurate thickness mapping. In principle all relevant defects show up as local thickness reductions. The results in this paper are obtained with a MEMS-sensor array of 128 elements capable of detecting ultrasound up to 250 kHz at a typical stand-off distance of 100 mm. Defects up to 6 mm in diameter could be detected in thick panels, and defects as small as 2.5 mm could be detected in thin panels. A full-size fuselage experiment shows that the method is also suited for fast inspection of large inspection areas.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"21 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77908018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A framework for quantifying the uncertainty propagating through the signal energy-based acoustic source localization approach in an orthotropic plate under an uncertainty in the properties of the plate material is presented. Seven mechanical properties of an orthotropic plate material, namely, density and six elastic constants, are considered as lognormally distributed and mutually independent random variables with a fixed coefficient of variation for all seven random variables. Their means are considered such that the ‘mean’ plate exhibits a strong anisotropy. Using Latin Hypercube Sampling, several design points in lognormal spaces of these random variables are selected. For each design point, an acoustic event is simulated in the corresponding plate using finite element analyses. The signal energy-based approach is applied to localize the acoustic source for each design point. The localization error for each design point is taken as the ‘response’, and a regression kriging metamodel is constructed through these response values at the design points. Monte Carlo points are selected in lognormal spaces of the random variables, and the response values at these Monte Carlo points are estimated using the regression kriging metamodel. The distribution parameters of the so obtained response values are computed. Finally, a global sensitivity analysis of the random variables is carried out by computing the Sobol' indices.
{"title":"On Propagation of Material Property Uncertainty through Signal Energy-Based Acoustic Source Localization in an Orthotropic Plate","authors":"Novonil Sen","doi":"10.1115/1.4056733","DOIUrl":"https://doi.org/10.1115/1.4056733","url":null,"abstract":"\u0000 A framework for quantifying the uncertainty propagating through the signal energy-based acoustic source localization approach in an orthotropic plate under an uncertainty in the properties of the plate material is presented. Seven mechanical properties of an orthotropic plate material, namely, density and six elastic constants, are considered as lognormally distributed and mutually independent random variables with a fixed coefficient of variation for all seven random variables. Their means are considered such that the ‘mean’ plate exhibits a strong anisotropy. Using Latin Hypercube Sampling, several design points in lognormal spaces of these random variables are selected. For each design point, an acoustic event is simulated in the corresponding plate using finite element analyses. The signal energy-based approach is applied to localize the acoustic source for each design point. The localization error for each design point is taken as the ‘response’, and a regression kriging metamodel is constructed through these response values at the design points. Monte Carlo points are selected in lognormal spaces of the random variables, and the response values at these Monte Carlo points are estimated using the regression kriging metamodel. The distribution parameters of the so obtained response values are computed. Finally, a global sensitivity analysis of the random variables is carried out by computing the Sobol' indices.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"137 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86482864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a spindle condition monitoring methodology using a low-power smart vibration sensor and a near real-time deep neural network classifier. The most frequent spindle failures, such as imbalance, ingression, and evidence of a crash with the workpiece, are analyzed in this study. Experiments were designed to induce various failure events to monitor the spindle behavior using conventional vibration, current and temperature sensors, and an intelligent vibration sensor. The smart sensor is a device with internal signal processing identifying eight dominant frequencies and the amplitude/power distributions. It requires low power and generates narrow bandwidth messages that can be communicated wirelessly. A Fog device and a test plan are designed to monitor and store a dataset needed to train a Deep Neural Network (DNN) classifier. The Fog device generates temperature, current, and vibration signals from sensors connected to the spindle and sends them to data storage in the cloud. The signals were analyzed using both conventional vibration analysis and AI-based classifiers. The data from the smart sensor are used to train an optimized DNN, and the spindle defect classification performance is measured. With 960 data points per failure mode and training data taken over 960 minutes of operation, the optimized DNNs can classify the spindle states with an accuracy of 98%. The study shows real-time spindle condition classification feasibility over long periods using inexpensive and low-power smart vibration sensors.
{"title":"Spindle Condition Monitoring with a Smart Vibration Sensor and an Optimized Deep Neural Network","authors":"Lo-Eng Oh, Emil Pitz, K. Pochiraju","doi":"10.1115/1.4056616","DOIUrl":"https://doi.org/10.1115/1.4056616","url":null,"abstract":"\u0000 This paper presents a spindle condition monitoring methodology using a low-power smart vibration sensor and a near real-time deep neural network classifier. The most frequent spindle failures, such as imbalance, ingression, and evidence of a crash with the workpiece, are analyzed in this study. Experiments were designed to induce various failure events to monitor the spindle behavior using conventional vibration, current and temperature sensors, and an intelligent vibration sensor. The smart sensor is a device with internal signal processing identifying eight dominant frequencies and the amplitude/power distributions. It requires low power and generates narrow bandwidth messages that can be communicated wirelessly. A Fog device and a test plan are designed to monitor and store a dataset needed to train a Deep Neural Network (DNN) classifier. The Fog device generates temperature, current, and vibration signals from sensors connected to the spindle and sends them to data storage in the cloud. The signals were analyzed using both conventional vibration analysis and AI-based classifiers. The data from the smart sensor are used to train an optimized DNN, and the spindle defect classification performance is measured. With 960 data points per failure mode and training data taken over 960 minutes of operation, the optimized DNNs can classify the spindle states with an accuracy of 98%. The study shows real-time spindle condition classification feasibility over long periods using inexpensive and low-power smart vibration sensors.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"32 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89818146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The presence and location of knots within cut lumber substantially controls the physical properties and commercial value of the material. Thus, there is great practical interest in developing ways of choosing the cutting pattern for a log in a sawmill to optimize the arrangement of knots in the resulting cut lumber. X-rays can image the interior of a log to detect the arrangement of the knots; however, traditional radiography measurements are two-dimensional in character and cannot provide the needed depth information. Conversely, computed tomography (CT) can provide the required spatial details but is challenging in practice because of its complexity and cost. The research here aims to overcome these concerns by employing a novel ‘oblique’ scanning technique that uses radiography to determine knot orientations with both reasonable accuracy and low cost. Image processing and detection algorithms were developed to locate and orientate the knots automatically within the scanned logs. Detection metrics of Precision and Recall were used to analyze the performance of the detection algorithm. Results indicate that the oblique scanning method is a viable way to detect and orientate knots within logs with both reasonable accuracy and low cost compared to existing methods. In initial tests, an average circumferential angle accuracy within 15 degrees was achieved, with the detection algorithm being able to detect between 60% to 80% of the knots present within the log.
{"title":"Log Grading and Knot Identification by Oblique X-ray Scanning","authors":"Conan S. Omori, G. Schajer","doi":"10.1115/1.4056342","DOIUrl":"https://doi.org/10.1115/1.4056342","url":null,"abstract":"\u0000 The presence and location of knots within cut lumber substantially controls the physical properties and commercial value of the material. Thus, there is great practical interest in developing ways of choosing the cutting pattern for a log in a sawmill to optimize the arrangement of knots in the resulting cut lumber. X-rays can image the interior of a log to detect the arrangement of the knots; however, traditional radiography measurements are two-dimensional in character and cannot provide the needed depth information. Conversely, computed tomography (CT) can provide the required spatial details but is challenging in practice because of its complexity and cost. The research here aims to overcome these concerns by employing a novel ‘oblique’ scanning technique that uses radiography to determine knot orientations with both reasonable accuracy and low cost. Image processing and detection algorithms were developed to locate and orientate the knots automatically within the scanned logs. Detection metrics of Precision and Recall were used to analyze the performance of the detection algorithm. Results indicate that the oblique scanning method is a viable way to detect and orientate knots within logs with both reasonable accuracy and low cost compared to existing methods. In initial tests, an average circumferential angle accuracy within 15 degrees was achieved, with the detection algorithm being able to detect between 60% to 80% of the knots present within the log.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"7 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85501051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Steam turbine blades are the important components in power system shaft lines subjected to severe temperatures, leading to low/high cycle fatigue failures. The transient conditions occurring during startup and shutdown events generate alternative stresses causing the fracture at the blade roots. Present work deals with the effect of localized damage on the vibration characteristics and damage identification study in the last stage LP (low pressure) steam turbine blade. Initially, free vibration studies and transient analysis of the last row LP blade section are conducted using the finite element model. A crack near the root region is modeled by a torsional spring, whose stiffness is expressed in terms of crack depth ratio. Effects of crack depth ratio and location near the roots on the natural frequencies and transient response amplitudes are studied in detail. The relationship between the damage parameters and blade frequencies is established through the back propagation neural network model.
{"title":"Localized damage identification in the last stage low-pressure steam turbine blade using dynamic parameter measurements","authors":"K. Shetkar, Jithendra Srinivas","doi":"10.1115/1.4056312","DOIUrl":"https://doi.org/10.1115/1.4056312","url":null,"abstract":"\u0000 Steam turbine blades are the important components in power system shaft lines subjected to severe temperatures, leading to low/high cycle fatigue failures. The transient conditions occurring during startup and shutdown events generate alternative stresses causing the fracture at the blade roots. Present work deals with the effect of localized damage on the vibration characteristics and damage identification study in the last stage LP (low pressure) steam turbine blade. Initially, free vibration studies and transient analysis of the last row LP blade section are conducted using the finite element model. A crack near the root region is modeled by a torsional spring, whose stiffness is expressed in terms of crack depth ratio. Effects of crack depth ratio and location near the roots on the natural frequencies and transient response amplitudes are studied in detail. The relationship between the damage parameters and blade frequencies is established through the back propagation neural network model.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"50 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90039537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A reliable assessment of the actual condition of wind turbines is essential for their continuous operation, or if an extension of their service life is considered. To be able to make valid statements about the condition of the welded structure, an inspection concept for the reliable detection of fatigue cracks and damage on welds of the offshore tower and foundation structures is essential. In the present study, five different eddy current differential sensors were evaluated on cyclically fatigued DY butt welds under tensile load with a focus on the signal-to-noise ratio (SNR). Based on previous evaluations, two sensors were selected for semi-automatic weld testing. It is shown that due to the weld crossing necessary for the detection of fatigue cracks, air coils arranged parallel to the test surface have the highest SNR. This must be contrasted with the potential for crack depth determination. In this context, coils with different arrangement with respect to the test surface were analyzed. It is shown that groove depths can be differentiated based on the imaginary part of the measurement signal for groove depths of up to 8 mm, and actual fatigue cracks in welds with a crack depth of 0.5 mm were detected.
{"title":"Detection and characterization of fatigue cracks in butt welds of offshore structures using the eddy current method","authors":"René Gansel, H. Maier, S. Barton","doi":"10.1115/1.4056313","DOIUrl":"https://doi.org/10.1115/1.4056313","url":null,"abstract":"\u0000 A reliable assessment of the actual condition of wind turbines is essential for their continuous operation, or if an extension of their service life is considered. To be able to make valid statements about the condition of the welded structure, an inspection concept for the reliable detection of fatigue cracks and damage on welds of the offshore tower and foundation structures is essential. In the present study, five different eddy current differential sensors were evaluated on cyclically fatigued DY butt welds under tensile load with a focus on the signal-to-noise ratio (SNR). Based on previous evaluations, two sensors were selected for semi-automatic weld testing. It is shown that due to the weld crossing necessary for the detection of fatigue cracks, air coils arranged parallel to the test surface have the highest SNR. This must be contrasted with the potential for crack depth determination. In this context, coils with different arrangement with respect to the test surface were analyzed. It is shown that groove depths can be differentiated based on the imaginary part of the measurement signal for groove depths of up to 8 mm, and actual fatigue cracks in welds with a crack depth of 0.5 mm were detected.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"19 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89805228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bharath Basti Shenoy, Zi Li, L. Udpa, S. Udpa, Y. Deng, T. Seuaciuc-Osório
Stainless steel is used in many applications because of its excellent mechanical properties at elevated temperatures. Material fatigue is a major problem in steel structures and can cause catastrophic damage resulting in significant economic consequences. Conventional nondestructive evaluation techniques can detect macro defects, but do not perform well when it comes to material degradation due to fatigue, which happens at a microstructure level. It is well known that stress applied on a material will have an impact on the microstructure and produces a change in the magnetic properties of the material. Hence magnetic nondestructive evaluation techniques that are sensitive to changes in magnetic properties play a major role in the early-stage fatigue detection, i.e., before the macro crack initiates. This paper introduces the Magnetic barkhausen noise technique to garner information about fatigue state of the material under test. K-medoids clustering algorithm and genetic optimization algorithm are used to classify the stainless-samples into fatigue categories. Initial results prove that the martensitic grade stainless-steel samples in different stages of fatigue can be classified into broad fatigue categories, i.e., low fatigue, mid fatigue and high fatigue based on the remaining useful life of the sample.
{"title":"Magnetic Barkhausen Noise Technique for Fatigue Detection and Classification in Martensitic Stainless-Steel","authors":"Bharath Basti Shenoy, Zi Li, L. Udpa, S. Udpa, Y. Deng, T. Seuaciuc-Osório","doi":"10.1115/1.4055992","DOIUrl":"https://doi.org/10.1115/1.4055992","url":null,"abstract":"\u0000 Stainless steel is used in many applications because of its excellent mechanical properties at elevated temperatures. Material fatigue is a major problem in steel structures and can cause catastrophic damage resulting in significant economic consequences. Conventional nondestructive evaluation techniques can detect macro defects, but do not perform well when it comes to material degradation due to fatigue, which happens at a microstructure level. It is well known that stress applied on a material will have an impact on the microstructure and produces a change in the magnetic properties of the material. Hence magnetic nondestructive evaluation techniques that are sensitive to changes in magnetic properties play a major role in the early-stage fatigue detection, i.e., before the macro crack initiates. This paper introduces the Magnetic barkhausen noise technique to garner information about fatigue state of the material under test. K-medoids clustering algorithm and genetic optimization algorithm are used to classify the stainless-samples into fatigue categories. Initial results prove that the martensitic grade stainless-steel samples in different stages of fatigue can be classified into broad fatigue categories, i.e., low fatigue, mid fatigue and high fatigue based on the remaining useful life of the sample.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"6 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88307793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The existence of a wide reinforced concrete building stock, which is reaching the end of its service life, has focused the attention on its vulnerability assessment. The first step of this analysis is the estimation of the concrete compressive strength. In this field, many codes allow to supplement the classical destructive tests with non-destructive ones, because of their versality and of the possibility of reducing the time spent for tests. It is worth noting that the spread of these tests is strictly connected to the accuracy of the conversion models, which correlate the non-destructive measurements to the concrete strength. The present paper deals with this issue by examining the results of the experimental investigations, which made use of destructive and Ultrasonic Pulse Velocity tests, on three r.c. buildings in Bari (Italy). The data are analyzed and rearranged to define several testing conditions, for each one of which a conversion model is calibrated. Moreover, the variability of the parameters of the identified models are discussed with respect to the number of data considered in the calibration process, to the chosen model, and to the accuracy of the assessed strength.
{"title":"ON THE VARIABILITY OF CONVERSION MODELS FOR CONCRETE STRENGTH ASSESSMENT BASED ON PULSE VELOCITY MEASUREMENTS","authors":"M. Diaferio","doi":"10.1115/1.4055979","DOIUrl":"https://doi.org/10.1115/1.4055979","url":null,"abstract":"The existence of a wide reinforced concrete building stock, which is reaching the end of its service life, has focused the attention on its vulnerability assessment. The first step of this analysis is the estimation of the concrete compressive strength. In this field, many codes allow to supplement the classical destructive tests with non-destructive ones, because of their versality and of the possibility of reducing the time spent for tests. It is worth noting that the spread of these tests is strictly connected to the accuracy of the conversion models, which correlate the non-destructive measurements to the concrete strength. The present paper deals with this issue by examining the results of the experimental investigations, which made use of destructive and Ultrasonic Pulse Velocity tests, on three r.c. buildings in Bari (Italy). The data are analyzed and rearranged to define several testing conditions, for each one of which a conversion model is calibrated. Moreover, the variability of the parameters of the identified models are discussed with respect to the number of data considered in the calibration process, to the chosen model, and to the accuracy of the assessed strength.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"65 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84787852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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