X-ray computed tomography (XCT) is a widely adopted nondestructive technique for characterizing internal porosity in additive manufactured (AM) components. However, the accuracy and precision of porosity characterization using XCT can be affected by factors, such as XCT system configuration and post-processing methodologies. This study investigates the influence of these variables on porosity characterization by comparing results obtained from four different XCT systems and two distinct analysis workflows applied to a single metallic AM sample. A benchmark is also established for the XCT performance by using a high-resolution reference dataset generated through mechanical polishing serial sectioning (MPSS). Porosity metrics, including volume fraction, pore count, size distribution, and equivalent spherical diameter (ESD), were computed for large pores ( $ge 84~mu $ m) within the XCT and MPSS datasets. By comparing these metrics across XCT systems and workflows, this research aims to demonstrate the variability introduced by different XCT configurations and analysis procedures, providing insights into the potential limitations and uncertainty considerations needed while carrying out XCT-based porosity characterization of AM components.
X 射线计算机断层扫描（XCT）是一种广泛采用的无损技术，用于表征增材制造（AM）部件的内部孔隙率。然而，使用 XCT 表征孔隙率的准确性和精确度会受到一些因素的影响，如 XCT 系统配置和后处理方法。本研究通过比较四种不同的 XCT 系统和两种应用于单一金属 AM 样品的不同分析工作流程所获得的结果，研究了这些变量对孔隙率表征的影响。此外，还使用通过机械抛光序列切片（MPSS）生成的高分辨率参考数据集为 XCT 性能建立了基准。对 XCT 和 MPSS 数据集中的大孔隙（84~mu $ m）计算了孔隙度指标，包括体积分数、孔隙数、尺寸分布和等效球直径（ESD）。通过比较不同 XCT 系统和工作流程的这些指标，本研究旨在展示不同 XCT 配置和分析程序带来的可变性，从而深入了解在对 AM 组件进行基于 XCT 的孔隙率表征时需要考虑的潜在限制和不确定性。
{"title":"Comparative Analysis of Internal Porosity in AM Ti64 Using X-Ray Computed Tomography and Mechanical Polishing Serial Sectioning","authors":"Bryce Jolley;Christine Knott;Daniel Sparkman;Michael Uchic","doi":"10.1109/OJIM.2024.3477569","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3477569","url":null,"abstract":"X-ray computed tomography (XCT) is a widely adopted nondestructive technique for characterizing internal porosity in additive manufactured (AM) components. However, the accuracy and precision of porosity characterization using XCT can be affected by factors, such as XCT system configuration and post-processing methodologies. This study investigates the influence of these variables on porosity characterization by comparing results obtained from four different XCT systems and two distinct analysis workflows applied to a single metallic AM sample. A benchmark is also established for the XCT performance by using a high-resolution reference dataset generated through mechanical polishing serial sectioning (MPSS). Porosity metrics, including volume fraction, pore count, size distribution, and equivalent spherical diameter (ESD), were computed for large pores (\u0000<inline-formula> <tex-math>$ge 84~mu $ </tex-math></inline-formula>\u0000m) within the XCT and MPSS datasets. By comparing these metrics across XCT systems and workflows, this research aims to demonstrate the variability introduced by different XCT configurations and analysis procedures, providing insights into the potential limitations and uncertainty considerations needed while carrying out XCT-based porosity characterization of AM components.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.1109/OJIM.2024.3459989
Tyler J. Lesthaeghe;Stephen D. Holland
In the field of nondestructive evaluation (NDE), we sometimes need an intricate system of multiple actuators and sensors to measure and assess the material condition or structural integrity of a specimen. Complicated systems are especially necessary for more advanced techniques that involve multiple phenomena or modeling in a geometric context. In the research laboratory, we rarely understand the intricacies of the measurement up front, and we need the agility to reconfigure our measurement system as needs evolve. Software is the glue that ties our measurement systems together. The traditional approach of ad hoc software quickly becomes unsustainable in the modern environment. We propose an alternative approach that addresses the need for agility in the modern NDE laboratory: a reconfigurable, modular software architecture that is built from the ground up to accommodate conflicting requirements in the areas of data management, automation, parallelism, geometry and robotics, and version control. We describe a new pair of open-source tools, Dataguzzler-Python and SpatialNDE2, that facilitate instrumentation control, data acquisition, and processing for the NDE laboratory. The tools make up a framework that provides the following: multiplexed automatic and manual control of instrumentation, a versioned database to store the acquired data, parallel acquisition and live high performance/GPU computation, the ability to acquire and store data in geometric context, and the ability to visualize and interact with the acquired data. This article discusses their design, implementation, and initial experiences in using them in the NDE laboratory.
{"title":"Dataguzzler-Python and SpatialNDE2: Crucial Software Infrastructure for Reconfigurable NDE Data Acquisition With Spatial Context","authors":"Tyler J. Lesthaeghe;Stephen D. Holland","doi":"10.1109/OJIM.2024.3459989","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3459989","url":null,"abstract":"In the field of nondestructive evaluation (NDE), we sometimes need an intricate system of multiple actuators and sensors to measure and assess the material condition or structural integrity of a specimen. Complicated systems are especially necessary for more advanced techniques that involve multiple phenomena or modeling in a geometric context. In the research laboratory, we rarely understand the intricacies of the measurement up front, and we need the agility to reconfigure our measurement system as needs evolve. Software is the glue that ties our measurement systems together. The traditional approach of ad hoc software quickly becomes unsustainable in the modern environment. We propose an alternative approach that addresses the need for agility in the modern NDE laboratory: a reconfigurable, modular software architecture that is built from the ground up to accommodate conflicting requirements in the areas of data management, automation, parallelism, geometry and robotics, and version control. We describe a new pair of open-source tools, Dataguzzler-Python and SpatialNDE2, that facilitate instrumentation control, data acquisition, and processing for the NDE laboratory. The tools make up a framework that provides the following: multiplexed automatic and manual control of instrumentation, a versioned database to store the acquired data, parallel acquisition and live high performance/GPU computation, the ability to acquire and store data in geometric context, and the ability to visualize and interact with the acquired data. This article discusses their design, implementation, and initial experiences in using them in the NDE laboratory.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/OJIM.2024.3449936
Oliver L. Geißendörfer;Christoph Holst
Analyzing objects concerning their periodic behavior is mostly performed with inertial measurement units (IMUs) or global navigation satellite system (GNSS) sensors fixed to its surface. For connecting observations, sensors have to be assigned to the same reference frame in space and time as a prerequisite. Using light detection and ranging (LiDAR) observations enables contactless, time-synchronized, and spatially connected data points within a single sensor. Therefore, common signal properties are further analyzed in the spectrum to find connections and similarities between observations. Since observations are spatially continuous we can discretize them and traditionally process them. However, the time domain offers a diversity of ways to simultaneously estimate frequencies and continuously model properties at different spatial locations. Within this work, we exploit the potential of processing LiDAR data in the time domain to make use of the sensor’s contactless observations and its sampling rate in space and time. Consecutive points and their spatial neighborhoods are used to implement temporal as well as spatiotemporal connections to directly model oscillations in 2-D space. Moreover, we compute an uncertainty of estimated variables to qualify our solution. Consequently, our approach offers the opportunity to describe as well as evaluate movements and vibrations of spatially connected areas.
{"title":"Combining LiDAR and Time-Domain Frequency Analysis for Enhanced Spatial Understanding of Vibration Responses","authors":"Oliver L. Geißendörfer;Christoph Holst","doi":"10.1109/OJIM.2024.3449936","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3449936","url":null,"abstract":"Analyzing objects concerning their periodic behavior is mostly performed with inertial measurement units (IMUs) or global navigation satellite system (GNSS) sensors fixed to its surface. For connecting observations, sensors have to be assigned to the same reference frame in space and time as a prerequisite. Using light detection and ranging (LiDAR) observations enables contactless, time-synchronized, and spatially connected data points within a single sensor. Therefore, common signal properties are further analyzed in the spectrum to find connections and similarities between observations. Since observations are spatially continuous we can discretize them and traditionally process them. However, the time domain offers a diversity of ways to simultaneously estimate frequencies and continuously model properties at different spatial locations. Within this work, we exploit the potential of processing LiDAR data in the time domain to make use of the sensor’s contactless observations and its sampling rate in space and time. Consecutive points and their spatial neighborhoods are used to implement temporal as well as spatiotemporal connections to directly model oscillations in 2-D space. Moreover, we compute an uncertainty of estimated variables to qualify our solution. Consequently, our approach offers the opportunity to describe as well as evaluate movements and vibrations of spatially connected areas.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-24DOI: 10.1109/OJIM.2024.3418466
Xinyi Du;Chi Xu;Lin Li;Xinchun Li
Industrial control systems are facing ever-increasing security challenges due to the large-scale access of heterogeneous devices in the open Internet environment. Existing anomaly detection methods are mainly based on the priori knowledge of industrial control protocols (ICPs) whose protocol specifications, communication mechanism, and data format are already known. However, when these knowledge are blank, namely, unknown ICPs, existing methods become powerless to detect the anomaly data. To tackle this challenge, we propose a multigranularity feature automatic marking-based deep learning method to classify unknown ICPs for anomaly detection. First, to obtain the feature sequences without priori knowledge assisting, we propose a multigranularity feature extraction algorithm to extract both byte and half-byte information by fully utilizing the intensive key information in the header field of the application layer. Then, to label the feature sequences for deep learning, we propose a feature automatic marking algorithm that utilizes the inconsistency feature sequences to dynamically update the feature sequence set. With the labeled feature sequences, we employ deep learning with 1-D convolutional neural network and gated recurrent unit to classify the unknown ICPs and realize anomaly detection. Extensive experiments on two public datasets show that both the accuracy and precision of the proposed method reach above 98.4%, which is better than the three benchmark methods.
{"title":"Multigranularity Feature Automatic Marking-Based Deep Learning for Anomaly Detection of Industrial Control Systems","authors":"Xinyi Du;Chi Xu;Lin Li;Xinchun Li","doi":"10.1109/OJIM.2024.3418466","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3418466","url":null,"abstract":"Industrial control systems are facing ever-increasing security challenges due to the large-scale access of heterogeneous devices in the open Internet environment. Existing anomaly detection methods are mainly based on the priori knowledge of industrial control protocols (ICPs) whose protocol specifications, communication mechanism, and data format are already known. However, when these knowledge are blank, namely, unknown ICPs, existing methods become powerless to detect the anomaly data. To tackle this challenge, we propose a multigranularity feature automatic marking-based deep learning method to classify unknown ICPs for anomaly detection. First, to obtain the feature sequences without priori knowledge assisting, we propose a multigranularity feature extraction algorithm to extract both byte and half-byte information by fully utilizing the intensive key information in the header field of the application layer. Then, to label the feature sequences for deep learning, we propose a feature automatic marking algorithm that utilizes the inconsistency feature sequences to dynamically update the feature sequence set. With the labeled feature sequences, we employ deep learning with 1-D convolutional neural network and gated recurrent unit to classify the unknown ICPs and realize anomaly detection. Extensive experiments on two public datasets show that both the accuracy and precision of the proposed method reach above 98.4%, which is better than the three benchmark methods.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10570378","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.1109/OJIM.2024.3412205
Marco Salucci;Lorenzo Poli;Giorgio Gottardi;Giacomo Oliveri;Luca Tosi;Andrea Massa
This article deals with the nondestructive testing and evaluation (NDT/NDE) of dielectric structures through a sparseness-promoting probabilistic microwave imaging (MI) method. Prior information on both the unperturbed scenario and the class of imaged targets is profitably exploited to formulate the inverse scattering problem (ISP) at hand within a differential contrast source inversion (CSI) framework. The imaging process is then efficiently completed by applying a customized Bayesian compressive sensing (BCS) inversion strategy. Selected numerical and experimental results are provided to assess the effectiveness of the proposed imaging method also in comparison with competitive state-of-the-art alternatives.
{"title":"Microwave NDT/NDE Through Differential Bayesian Compressive Sensing","authors":"Marco Salucci;Lorenzo Poli;Giorgio Gottardi;Giacomo Oliveri;Luca Tosi;Andrea Massa","doi":"10.1109/OJIM.2024.3412205","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3412205","url":null,"abstract":"This article deals with the nondestructive testing and evaluation (NDT/NDE) of dielectric structures through a sparseness-promoting probabilistic microwave imaging (MI) method. Prior information on both the unperturbed scenario and the class of imaged targets is profitably exploited to formulate the inverse scattering problem (ISP) at hand within a differential contrast source inversion (CSI) framework. The imaging process is then efficiently completed by applying a customized Bayesian compressive sensing (BCS) inversion strategy. Selected numerical and experimental results are provided to assess the effectiveness of the proposed imaging method also in comparison with competitive state-of-the-art alternatives.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552809","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.1109/OJIM.2024.3412219
Abhishek Thakur;P. Rajalakshmi
Autonomous navigation has become a topic of immense interest in robotics in recent years. Light detection and ranging (LiDAR) can perceive the environment in 3-D by creating the point cloud data that can be used in constructing a 3-D or high-definition (HD) map. Localization can be performed on the 3-D map created using a LiDAR sensor in real-time by matching the current point cloud data on the prebuilt map, which is useful in the GPS-denied areas. GPS data is inaccurate in indoor or obstructed environments, and achieving centimeter-level accuracy requires a costly real-time kinematic (RTK) connection in GPS. However, LiDAR produces bulky data with hundreds of thousands of points in a frame, making it computationally expensive to process. The localization algorithm must be very fast to ensure the smooth driving of autonomous vehicles. To make the localization faster, the point cloud is downsampled and filtered before matching, and subsequently, the Newton optimization is applied using the normal distribution transform to accelerate the convergence of the point cloud data on the map, achieving localization at 6 ms per frame, which is 16 times less than the data acquisition rate of LiDAR at 10 Hz (100ms per frame). The performance of optimized localization is also evaluated on the Kitti odometry benchmark dataset. With the same localization accuracy, the localization process is made five times faster. LiDAR map-based autonomous driving on an electric vehicle is tested in the TiHAN testbed at the IIT Hyderabad campus in real-time. The complete system runs on the robot operating system (ROS). The code will be released at https://github.com/abhishekt711/Localization-Nav .
{"title":"LiDAR-Based Optimized Normal Distribution Transform Localization on 3-D Map for Autonomous Navigation","authors":"Abhishek Thakur;P. Rajalakshmi","doi":"10.1109/OJIM.2024.3412219","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3412219","url":null,"abstract":"Autonomous navigation has become a topic of immense interest in robotics in recent years. Light detection and ranging (LiDAR) can perceive the environment in 3-D by creating the point cloud data that can be used in constructing a 3-D or high-definition (HD) map. Localization can be performed on the 3-D map created using a LiDAR sensor in real-time by matching the current point cloud data on the prebuilt map, which is useful in the GPS-denied areas. GPS data is inaccurate in indoor or obstructed environments, and achieving centimeter-level accuracy requires a costly real-time kinematic (RTK) connection in GPS. However, LiDAR produces bulky data with hundreds of thousands of points in a frame, making it computationally expensive to process. The localization algorithm must be very fast to ensure the smooth driving of autonomous vehicles. To make the localization faster, the point cloud is downsampled and filtered before matching, and subsequently, the Newton optimization is applied using the normal distribution transform to accelerate the convergence of the point cloud data on the map, achieving localization at 6 ms per frame, which is 16 times less than the data acquisition rate of LiDAR at 10 Hz (100ms per frame). The performance of optimized localization is also evaluated on the Kitti odometry benchmark dataset. With the same localization accuracy, the localization process is made five times faster. LiDAR map-based autonomous driving on an electric vehicle is tested in the TiHAN testbed at the IIT Hyderabad campus in real-time. The complete system runs on the robot operating system (ROS). The code will be released at \u0000<uri>https://github.com/abhishekt711/Localization-Nav</uri>\u0000.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552773","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-07DOI: 10.1109/OJIM.2024.3403319
{"title":"OJIM 2023 Reviewer List","authors":"","doi":"10.1109/OJIM.2024.3403319","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3403319","url":null,"abstract":"","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141292452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1109/OJIM.2024.3390214
Relindis Rott;Selim Solmaz
We present an assessment of simulated lidar point clouds based on different phenomenological range-reflectivity models. In sensor model development, the validation of individual model features is favorable. For lidar sensors, range limits depend on surface reflectivities. Two phenomenological feature models are derived from the lidar range equation, for clear and adverse weather conditions. The underlying parameters are the maximum ranges for best environment conditions, based on sensor datasheets, and a maximum range measurement for attenuation conditions. Furthermore, an assessment of different feature models is needed, similar to unit tests. Therefore, resulting point clouds are compared with respect to the total number of corresponding points and the number of points with no correspondences for pair-wise cloud comparison. Applications are presented using a point cloud lidar model. Results of the point cloud comparison are demonstrated for a single scene or time step and an entire scenario of 40 time steps. When a reference point cloud is provided by the sensor manufacturer, feature validation becomes possible.
{"title":"Assessment of Lidar Point Cloud Simulation Using Phenomenological Range-Reflectivity Limits for Feature Validation","authors":"Relindis Rott;Selim Solmaz","doi":"10.1109/OJIM.2024.3390214","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3390214","url":null,"abstract":"We present an assessment of simulated lidar point clouds based on different phenomenological range-reflectivity models. In sensor model development, the validation of individual model features is favorable. For lidar sensors, range limits depend on surface reflectivities. Two phenomenological feature models are derived from the lidar range equation, for clear and adverse weather conditions. The underlying parameters are the maximum ranges for best environment conditions, based on sensor datasheets, and a maximum range measurement for attenuation conditions. Furthermore, an assessment of different feature models is needed, similar to unit tests. Therefore, resulting point clouds are compared with respect to the total number of corresponding points and the number of points with no correspondences for pair-wise cloud comparison. Applications are presented using a point cloud lidar model. Results of the point cloud comparison are demonstrated for a single scene or time step and an entire scenario of 40 time steps. When a reference point cloud is provided by the sensor manufacturer, feature validation becomes possible.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents a novel method for accurate online extraction of semiconductor ON-state resistance in the presence of measurement noise. In this method, the ON-state resistance value is extracted from the measured ON-state voltage of the semiconductors and the measured load current. The extracted ON-state resistance can be used for online condition monitoring of semiconductors. The proposed method is based on the extraction of selective harmonic content. The estimated values are further enhanced through an integral action that increases the signal-to-noise ratio, making the proposed method suitable in the presence of noisy measurements. The efficacy of the proposed method is verified through simulations in the MATLAB/Simulink environment, and experimentally. The estimated ON-state resistance values from the online setup are compared to offline measurements from an industrial curve tracer, where an overall estimation error of less than 1% is observed. The proposed solution maintains its estimation accuracy under variable load conditions and for different temperatures of the device under test.
{"title":"A Novel ON-State Resistance Estimation Technique for Online Condition Monitoring of Semiconductor Devices Under Noisy Conditions","authors":"Mohsen Asoodar;Mehrdad Nahalparvari;Simon Schneider;Iman Shafikhani;Gunnar Ingeström;Hans-Peter Nee","doi":"10.1109/OJIM.2024.3379414","DOIUrl":"https://doi.org/10.1109/OJIM.2024.3379414","url":null,"abstract":"This article presents a novel method for accurate online extraction of semiconductor ON-state resistance in the presence of measurement noise. In this method, the ON-state resistance value is extracted from the measured ON-state voltage of the semiconductors and the measured load current. The extracted ON-state resistance can be used for online condition monitoring of semiconductors. The proposed method is based on the extraction of selective harmonic content. The estimated values are further enhanced through an integral action that increases the signal-to-noise ratio, making the proposed method suitable in the presence of noisy measurements. The efficacy of the proposed method is verified through simulations in the MATLAB/Simulink environment, and experimentally. The estimated ON-state resistance values from the online setup are compared to offline measurements from an industrial curve tracer, where an overall estimation error of less than 1% is observed. The proposed solution maintains its estimation accuracy under variable load conditions and for different temperatures of the device under test.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10479961","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141439496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-25DOI: 10.1109/OJIM.2024.3379386
Mohammad Avzayesh;Wasim Al-Masri;Mamoun F. Abdel-Hafez;Mohammad AlShabi
This article proposes an enhanced fusion technique to improve the accuracy of the state estimation of a navigational system. The smooth variable structure filter (SVSF) is examined to estimate the system’s state under model uncertainty. Its combination with the unscented Kalman filter (UKF) to acquire better navigational accuracy while being robust to the system’s modeling uncertainty is investigated. The proposed hybrid method is compared with the extended Kalman filter (EKF), the UKF, and the SVSF. The proposed algorithms fuse an inertial measurement unit (IMU) with the Global Positioning Systems (GPS) measurements to obtain the vehicle’s state. Experimental results are compared to a commercial off-the-shelf (COTS) solution. It is shown that all filtering strategies have similar performance in the absence of large-magnitude noise and model uncertainties. When injecting modeling uncertainties, the performance of the UKF degrades, and that of the EKF goes out of bounds. On the other hand, increasing the covariances of the measurement and dynamics noise sequences causes the path of the SVSF to become nonsmooth and roughly oscillates around the true path. The proposed integrated UK-SVSF algorithm achieves the following objectives: first, using the Kaman-based filter enhances the optimality of the filter to GPS/IMU dynamics and measurements noise. Second, using the UKF reduces the estimation error by eliminating the first-order linearization step. Finally, using the SVSF enhances the estimate’s robustness to model uncertainty. Results reveal that, in the presence of both large-magnitude noise and model uncertainties, the UK-SVSF gives an enhanced estimation performance.
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