Pub Date : 2024-10-16DOI: 10.1109/TIM.2024.3481553
Lei Yang;Yubing Han
One of the main tasks of a wideband reconnaissance receiver is to measure the carrier frequency and direction-of-arrival (DOA) rapidly once the radar signal is intercepted. To address the conflict between the wideband requirements and the Nyquist sampling theorem, a joint spatial-temporal sub-Nyquist sampling structure is developed. The parallel multiple low-rate analog-to-digital converters (ADCs) are first employed to achieve coprime sampling in the temporal domain, and the closed-form robust Chinese remainder theorem (CRT) is utilized to solve for ambiguity-free frequencies. Then, the coprime array achieves coprime sampling in the spatial domain, and its spatial spectrum completes the ambiguity-free DOA estimation due to the linear superposition of the two subarrays. To further minimize redundant samples, the coprime subarrays are matched one-to-one with the coprime ADCs so that only one ADC is connected to each antenna. Considering hypothetical physical conditions, we propose guidelines for parameter selection, including sampling rate and array arrangement. Numerical simulations demonstrate the robustness of the proposed structure and it is also effective in multisource and chirp signal scenarios.
宽带侦察接收机的主要任务之一是在截获雷达信号后迅速测量载波频率和到达方向（DOA）。为了解决宽带要求与奈奎斯特采样定理之间的矛盾，开发了一种空间-时间联合亚奈奎斯特采样结构。首先采用并行的多个低速率模数转换器（ADC）来实现时域的共轭采样，并利用闭式稳健中国余数定理（CRT）来求解无歧义频率。然后，共轭阵列在空间域实现共轭采样，由于两个子阵列的线性叠加，其空间频谱可完成无歧义 DOA 估计。为了进一步减少冗余采样，共轭子阵列与共轭 ADC 一对一匹配，这样每个天线只需连接一个 ADC。考虑到假设的物理条件，我们提出了参数选择指南，包括采样率和阵列排列。数值模拟证明了所提结构的鲁棒性，而且在多信号源和啁啾信号情况下也很有效。
{"title":"A Joint Spatio-Temporal Sub-Nyquist Sampling Structure for Wideband Receivers","authors":"Lei Yang;Yubing Han","doi":"10.1109/TIM.2024.3481553","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481553","url":null,"abstract":"One of the main tasks of a wideband reconnaissance receiver is to measure the carrier frequency and direction-of-arrival (DOA) rapidly once the radar signal is intercepted. To address the conflict between the wideband requirements and the Nyquist sampling theorem, a joint spatial-temporal sub-Nyquist sampling structure is developed. The parallel multiple low-rate analog-to-digital converters (ADCs) are first employed to achieve coprime sampling in the temporal domain, and the closed-form robust Chinese remainder theorem (CRT) is utilized to solve for ambiguity-free frequencies. Then, the coprime array achieves coprime sampling in the spatial domain, and its spatial spectrum completes the ambiguity-free DOA estimation due to the linear superposition of the two subarrays. To further minimize redundant samples, the coprime subarrays are matched one-to-one with the coprime ADCs so that only one ADC is connected to each antenna. Considering hypothetical physical conditions, we propose guidelines for parameter selection, including sampling rate and array arrangement. Numerical simulations demonstrate the robustness of the proposed structure and it is also effective in multisource and chirp signal scenarios.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Power semiconductor device condition monitoring technology based on the mechanical stress wave (MSW) is an important means to study device failure mechanisms and evaluate device reliability. However, MSW signals have mainly been detected through acoustic emission (AE) sensors for contact detection. The operating temperature of the device will affect the detection results, and the presence of MSW low-frequency components may be overlooked due to limitations in sensor performance. This article proposes an offline, noncontact MSW measurement method based on a laser Doppler vibrometer for power semiconductor devices. This method obtained the complete MSW signal generated during the switching process of power semiconductor devices, especially the low-frequency components below 20 kHz. Moreover, the proposed method exhibits greater sensitivity in detecting the high-frequency components of the MSW compared to contact detection. This study extends the detection methods and frequency ranges for the MSW in power semiconductor devices, thereby expecting to facilitate the generation and propagation mechanisms research of the MSW.
{"title":"Mechanical Stress Wave Noncontact Laser Detection Method of Power Semiconductor Devices—Observation of Low-Frequency Signals","authors":"Qiying Li;Yunze He;Mengchuan Li;Yang Ping;Longhai Tang;Xuefeng Geng;Guangxin Wang;Shan Chang;Jie Zhang","doi":"10.1109/TIM.2024.3481529","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481529","url":null,"abstract":"Power semiconductor device condition monitoring technology based on the mechanical stress wave (MSW) is an important means to study device failure mechanisms and evaluate device reliability. However, MSW signals have mainly been detected through acoustic emission (AE) sensors for contact detection. The operating temperature of the device will affect the detection results, and the presence of MSW low-frequency components may be overlooked due to limitations in sensor performance. This article proposes an offline, noncontact MSW measurement method based on a laser Doppler vibrometer for power semiconductor devices. This method obtained the complete MSW signal generated during the switching process of power semiconductor devices, especially the low-frequency components below 20 kHz. Moreover, the proposed method exhibits greater sensitivity in detecting the high-frequency components of the MSW compared to contact detection. This study extends the detection methods and frequency ranges for the MSW in power semiconductor devices, thereby expecting to facilitate the generation and propagation mechanisms research of the MSW.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1109/TIM.2024.3481568
Ali Ahsan Hasnain;Abdul Basir;Youngdae Cho;Izaz Ali Shah;Hyoungsuk Yoo
Precise localization of a wireless capsule endoscope (WCE) in the gastrointestinal (GI) tract is paramount for accurate identification of lesions and targeted drug delivery. However, tracking a WCE with high accuracy remains a challenging task. This study presents a WCE localization system with high accuracy and a low root-mean-square error (RMSE) that can localize and track a capsule inside the GI tract with a resolution of 1 cm. The proposed system is based on a comprehensive collection of measured received signal strength (RSS) in a saline-filled American Society for Testing and Materials (ASTMs) phantom. A conformal capsule transmitter, along with an optimized configuration of four on-body receiver antennas operating in the industrial, scientific, and medical (ISM) band at 2.45 GHz, is connected to software-defined radios (SDRs). This setup enables the collection of a substantial dataset comprising 11400 RSS data points, which are systematically mapped to determine the capsule’s position. Data-driven frameworks incorporating three different machine learning (ML) regression models: k-nearest neighbors (KNNs), support vector regression (SVR), and adaptive boosting (AdaBoost), are employed to improve positional accuracy in the localization and tracking processes. Among the utilized ML models, AdaBoost exhibited significant performance with a positional accuracy of 92.60% and an RMSE of 2.38 cm. Moreover, the AdaBoost regression model emerged as the most proficient in tracking a realistic intestinal trajectory with an RMSE of 0.38 cm. Considering its remarkable accuracy, the proposed ML-assisted system is a potential candidate for accurate localization and tracking of a capsule within the GI tract.
{"title":"RSS-Based Machine-Learning-Assisted Localization and Tracking of a Wireless Capsule Endoscope","authors":"Ali Ahsan Hasnain;Abdul Basir;Youngdae Cho;Izaz Ali Shah;Hyoungsuk Yoo","doi":"10.1109/TIM.2024.3481568","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481568","url":null,"abstract":"Precise localization of a wireless capsule endoscope (WCE) in the gastrointestinal (GI) tract is paramount for accurate identification of lesions and targeted drug delivery. However, tracking a WCE with high accuracy remains a challenging task. This study presents a WCE localization system with high accuracy and a low root-mean-square error (RMSE) that can localize and track a capsule inside the GI tract with a resolution of 1 cm. The proposed system is based on a comprehensive collection of measured received signal strength (RSS) in a saline-filled American Society for Testing and Materials (ASTMs) phantom. A conformal capsule transmitter, along with an optimized configuration of four on-body receiver antennas operating in the industrial, scientific, and medical (ISM) band at 2.45 GHz, is connected to software-defined radios (SDRs). This setup enables the collection of a substantial dataset comprising 11400 RSS data points, which are systematically mapped to determine the capsule’s position. Data-driven frameworks incorporating three different machine learning (ML) regression models: k-nearest neighbors (KNNs), support vector regression (SVR), and adaptive boosting (AdaBoost), are employed to improve positional accuracy in the localization and tracking processes. Among the utilized ML models, AdaBoost exhibited significant performance with a positional accuracy of 92.60% and an RMSE of 2.38 cm. Moreover, the AdaBoost regression model emerged as the most proficient in tracking a realistic intestinal trajectory with an RMSE of 0.38 cm. Considering its remarkable accuracy, the proposed ML-assisted system is a potential candidate for accurate localization and tracking of a capsule within the GI tract.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1109/TIM.2024.3481655
Kazım Zengin;Aydın Yeşildirek
Humans and animals possess the ability to roughly estimate the distance and direction of specific sound sources through their auditory organs. This study introduces an innovative hybrid model for 3-D gunshot sound localization, drawing inspiration from human sound localization models and leveraging the strengths of both microphone arrays and deep learning techniques. The proposed system employs a six-microphone array with co-centered orthogonal placement to capture the sounds of gunshot muzzle blasts. The methodology involves first estimating azimuth and elevation angles using an approximate closed-form direction-of-arrival (DoA) formula based on time-difference-of-arrival (TDoA) values obtained from the Generalized Cross Correlation with Phase Transform (GCC-Phat) algorithm. Subsequently, Mel spectrograms are derived from the captured sound data and fed into a convolutional neural network (CNN) for distance estimation. Ultimately, the direction and distance estimations are integrated to achieve the 3-D localization of the gunshot source. The proposed approach stands as a notable contribution to the literature, relying solely on the sound of a muzzle blast for localization with a single microphone array. The average distance estimation error in the range of 50–500 m is 6.87%, demonstrating an improvement compared to existing systems with an error rate of approximately 16%. This study is pioneering in its application of deep learning training on a dataset of actual explosion sounds for distance estimation in gunshot localization systems.
{"title":"A Hybrid Model for 3-D Gunshot Localization Using Muzzle Blast Sound Only","authors":"Kazım Zengin;Aydın Yeşildirek","doi":"10.1109/TIM.2024.3481655","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481655","url":null,"abstract":"Humans and animals possess the ability to roughly estimate the distance and direction of specific sound sources through their auditory organs. This study introduces an innovative hybrid model for 3-D gunshot sound localization, drawing inspiration from human sound localization models and leveraging the strengths of both microphone arrays and deep learning techniques. The proposed system employs a six-microphone array with co-centered orthogonal placement to capture the sounds of gunshot muzzle blasts. The methodology involves first estimating azimuth and elevation angles using an approximate closed-form direction-of-arrival (DoA) formula based on time-difference-of-arrival (TDoA) values obtained from the Generalized Cross Correlation with Phase Transform (GCC-Phat) algorithm. Subsequently, Mel spectrograms are derived from the captured sound data and fed into a convolutional neural network (CNN) for distance estimation. Ultimately, the direction and distance estimations are integrated to achieve the 3-D localization of the gunshot source. The proposed approach stands as a notable contribution to the literature, relying solely on the sound of a muzzle blast for localization with a single microphone array. The average distance estimation error in the range of 50–500 m is 6.87%, demonstrating an improvement compared to existing systems with an error rate of approximately 16%. This study is pioneering in its application of deep learning training on a dataset of actual explosion sounds for distance estimation in gunshot localization systems.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate spatial 3-D vibration and deformation displacement measurement is essential in various fields, including structural health monitoring and smart manufacturing. However, traditional techniques such as laser and visual-based approaches suffer from limitations in performance, system complexity, and adaptability to harsh environments. In this article, we develop a novel spatial displacement measurement approach with a single microwave transceiver, creating the first unique 3-D microwave displacement sensor (3D-MDS), which can get rid of the fundamental issue of the 1-D displacement measurement along the line-of-sight in microwave sensing. In 3D-MDS, we utilize three noncollinear reference targets to establish the measurement coordinate system and further extract the desired 3-D displacement. To this end, a rigorous 3-D displacement reconstruction method is established leveraging on the multivariate function for spatial position and displacement with Taylor expansion. Moreover, the overall implementation procedures and primary considerations are described. Finally, the performance of the proposed method is validated through simulation and experiment with various scenarios, offering an appealing approach for accurate 3-D displacement measurement with microwave sensing.
{"title":"3-D Microwave Displacement Sensor: 3-D Displacement Measurement Using a Single Microwave Transceiver","authors":"Yingjie Gou;Yuyong Xiong;Zesheng Ren;Guang Meng;Zhike Peng","doi":"10.1109/TIM.2024.3481558","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481558","url":null,"abstract":"Accurate spatial 3-D vibration and deformation displacement measurement is essential in various fields, including structural health monitoring and smart manufacturing. However, traditional techniques such as laser and visual-based approaches suffer from limitations in performance, system complexity, and adaptability to harsh environments. In this article, we develop a novel spatial displacement measurement approach with a single microwave transceiver, creating the first unique 3-D microwave displacement sensor (3D-MDS), which can get rid of the fundamental issue of the 1-D displacement measurement along the line-of-sight in microwave sensing. In 3D-MDS, we utilize three noncollinear reference targets to establish the measurement coordinate system and further extract the desired 3-D displacement. To this end, a rigorous 3-D displacement reconstruction method is established leveraging on the multivariate function for spatial position and displacement with Taylor expansion. Moreover, the overall implementation procedures and primary considerations are described. Finally, the performance of the proposed method is validated through simulation and experiment with various scenarios, offering an appealing approach for accurate 3-D displacement measurement with microwave sensing.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, a novel Wollaston prism (WP) and retroreflector (RR) cooperative sensing heterodyne interferometer (CSHI) for simultaneous measurement of straightness errors and displacement is proposed. In this CSHI, a WP, a corner cube reflector (CCR), and a beam splitter are assembled together to act as the sensor for straightness errors and displacement measurements. Thanks to this ingenious design, the return direction of the straightness and displacement measurement beams does not change with the rotation of the measured object, thereby increasing the linear measurement range from a few hundred millimeters to several meters. Therefore, the proposed CSHI can not only achieve the simultaneous measurement of the straightness errors and displacement but also make the straightness error measurement unaffected by the rotation error of the measured object. In addition, the influence of horizontal straightness error on displacement measurement is analyzed and validated. The optical configuration of the proposed CSHI is described in detail. An experimental setup was constructed and a series of experiments were carried out to verify the feasibility and effectiveness of the CSHI. The experimental results showed that within a range of 4 m, the standard deviations (SDs) of the vertical straightness error and displacement measurement results between the proposed CSHI and a commercial interferometer achieved 0.12 and $0.11~mu $ m/m, respectively. Thus, the proposed CSHI has a great potential in industrial applications such as the ultraprecision machine-tool control, precision motion stage testing, and displacement sensor calibration.
{"title":"A Wollaston Prism and Corner Cube Reflector Cooperative Sensing Heterodyne Interferometer for Simultaneous Measurement of Straightness Errors and Displacement","authors":"Liping Yan;Yu Chen;Yingtian Lou;Jiandong Xie;Benyong Chen","doi":"10.1109/TIM.2024.3481550","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481550","url":null,"abstract":"In this study, a novel Wollaston prism (WP) and retroreflector (RR) cooperative sensing heterodyne interferometer (CSHI) for simultaneous measurement of straightness errors and displacement is proposed. In this CSHI, a WP, a corner cube reflector (CCR), and a beam splitter are assembled together to act as the sensor for straightness errors and displacement measurements. Thanks to this ingenious design, the return direction of the straightness and displacement measurement beams does not change with the rotation of the measured object, thereby increasing the linear measurement range from a few hundred millimeters to several meters. Therefore, the proposed CSHI can not only achieve the simultaneous measurement of the straightness errors and displacement but also make the straightness error measurement unaffected by the rotation error of the measured object. In addition, the influence of horizontal straightness error on displacement measurement is analyzed and validated. The optical configuration of the proposed CSHI is described in detail. An experimental setup was constructed and a series of experiments were carried out to verify the feasibility and effectiveness of the CSHI. The experimental results showed that within a range of 4 m, the standard deviations (SDs) of the vertical straightness error and displacement measurement results between the proposed CSHI and a commercial interferometer achieved 0.12 and \u0000<inline-formula> <tex-math>$0.11~mu $ </tex-math></inline-formula>\u0000 m/m, respectively. Thus, the proposed CSHI has a great potential in industrial applications such as the ultraprecision machine-tool control, precision motion stage testing, and displacement sensor calibration.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1109/TIM.2024.3481584
Song Ye;Chao Wang;Ran Pang;Bingkai Qiu
In the tunnel magnetoresistance (TMR)-based electromagnetic tomography (EMT) (TMR-EMT) system, TMR measurements of alternating magnetic induction around the object field can reconstruct the permeability distribution image within the object. However, TMR’s nonlinear characteristics affect the accuracy of magnetic induction measurements and image reconstruction quality. Existing solutions to TMR nonlinearity often involve complex sensor circuit designs or extensive data fitting, making them cumbersome to implement. To address this issue, this article proposes a method to compensate for magnetic induction measurements using the information on odd harmonic components in the TMR output signal. The method is simple and easy to implement as it obtains the quantitative relationship between the amplitudes of the odd harmonic components and the magnetic induction measurements by formula derivation and is realized based on field-programmable gate array (FPGA) and host computer. Simulation results show that this method reduces the mean relative error of TMR measurements for an alternating magnetic induction amplitude range of 0.1–3 mT from 38% to 4.2% and decreases the nonlinear error from 70% to 3.3%. Experimental results indicate that the average correlation coefficient (CC) of the image reconstruction results for five magnetic permeability distributions improves from 0.8139 to 0.8495 after compensating for TMR-EMT’s nonlinear measurements.
{"title":"TMR Nonlinear Compensation Method Based on Odd Harmonic Components","authors":"Song Ye;Chao Wang;Ran Pang;Bingkai Qiu","doi":"10.1109/TIM.2024.3481584","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481584","url":null,"abstract":"In the tunnel magnetoresistance (TMR)-based electromagnetic tomography (EMT) (TMR-EMT) system, TMR measurements of alternating magnetic induction around the object field can reconstruct the permeability distribution image within the object. However, TMR’s nonlinear characteristics affect the accuracy of magnetic induction measurements and image reconstruction quality. Existing solutions to TMR nonlinearity often involve complex sensor circuit designs or extensive data fitting, making them cumbersome to implement. To address this issue, this article proposes a method to compensate for magnetic induction measurements using the information on odd harmonic components in the TMR output signal. The method is simple and easy to implement as it obtains the quantitative relationship between the amplitudes of the odd harmonic components and the magnetic induction measurements by formula derivation and is realized based on field-programmable gate array (FPGA) and host computer. Simulation results show that this method reduces the mean relative error of TMR measurements for an alternating magnetic induction amplitude range of 0.1–3 mT from 38% to 4.2% and decreases the nonlinear error from 70% to 3.3%. Experimental results indicate that the average correlation coefficient (CC) of the image reconstruction results for five magnetic permeability distributions improves from 0.8139 to 0.8495 after compensating for TMR-EMT’s nonlinear measurements.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1109/TIM.2024.3481580
Zoltán Ádám Tamus
The voltage response (VR) measurement was developed decades ago as a condition monitoring technique for oil-paper insulation of electrical equipment. Later, the improved version of the method, namely, the extended voltage response (EVR), was introduced as an efficient tool for determining the electrical model and the dielectric response of insulations. This article presents a new interpretation of the EVR measurement results, namely, the deduction of how the trap distribution could be determined from the measurement results and how the trap distribution provided by the method can be used as a potential condition indicator for nuclear cables. The proposed method was tested on gamma-irradiated and thermally aged ethylene-propylene rubber (EPR)-insulated instrumentation and control (I&C) cable samples. The shallow and deep traps around 0.76- and 0.86-eV energy depth were identified, and the trap densities showed an increasing trend with aging time for irradiation and thermal aging. The results were compared with elongation at break (EaB) data, the most generally accepted condition indicator for polymeric cable components in the nuclear industry. Since a strong correlation between the EaB and deep trap density was shown, regression curves were calculated. In addition, based on the regression curves, threshold trap density values were determined for 25% of the remaining lifetime. The results revealed that the deep trap density can be a reliable condition indicator for nuclear power plant (NPP) cables.
{"title":"Determination of Trap Distribution Based on Voltage Response Measurement for Condition Monitoring of Nuclear Power Plant I&C Cables","authors":"Zoltán Ádám Tamus","doi":"10.1109/TIM.2024.3481580","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481580","url":null,"abstract":"The voltage response (VR) measurement was developed decades ago as a condition monitoring technique for oil-paper insulation of electrical equipment. Later, the improved version of the method, namely, the extended voltage response (EVR), was introduced as an efficient tool for determining the electrical model and the dielectric response of insulations. This article presents a new interpretation of the EVR measurement results, namely, the deduction of how the trap distribution could be determined from the measurement results and how the trap distribution provided by the method can be used as a potential condition indicator for nuclear cables. The proposed method was tested on gamma-irradiated and thermally aged ethylene-propylene rubber (EPR)-insulated instrumentation and control (I&C) cable samples. The shallow and deep traps around 0.76- and 0.86-eV energy depth were identified, and the trap densities showed an increasing trend with aging time for irradiation and thermal aging. The results were compared with elongation at break (EaB) data, the most generally accepted condition indicator for polymeric cable components in the nuclear industry. Since a strong correlation between the EaB and deep trap density was shown, regression curves were calculated. In addition, based on the regression curves, threshold trap density values were determined for 25% of the remaining lifetime. The results revealed that the deep trap density can be a reliable condition indicator for nuclear power plant (NPP) cables.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1109/TIM.2024.3481531
Li Cong;Jing Zhang;Jingnan Tian;Xiaojie Yang;Hongmin Li
The global navigation satellite system (GNSS) and inertial navigation system (INS) integrated navigation can realize accurate and reliable positioning for land vehicles. However, during GNSS outages, the cumulative errors caused by inertial sensors pose a threat to the GNSS/INS integrated system, especially when low-cost microelectromechanical system (MEMS) inertial sensors are utilized. The existing methods usually apply nonholonomic constraint (NHC) and machine-learning (ML)-/deep-learning (DL)-based modeling techniques to constrain the errors. Nevertheless, the performance of using NHC alone is limited if the forward velocity is not accurate enough, so additional constraints are needed, while the existing ML-/DL-based modeling techniques only utilize acceleration or angular velocity features, which are still susceptible to the stochastic errors of MEMS sensors. In this article, an ML-based modeling technique combining acceleration and frequency modulation (FM) radio signal is applied to further improve the performance of NHC-constrained MEMS-INS/GNSS integrated system. First, we derive the relationship between the received signal strength indicator (RSSI) of FM signal and vehicle distance increment to provide theoretical basis for the proposed algorithm. Then, FM signal features related to distance increment are extracted. Afterward, an availability assessment strategy is proposed to eliminate the moments when errors of using RSSI are large. Subsequently, we apply support vector regression (SVR) to estimate distance increment by combining FM signal and acceleration features. Finally, extended Kalman filter (EKF) is applied to fuse the predicted distance with INS during GNSS outages. Results show that the introduction of FM signal can significantly reduce distance estimation errors, thus improving positioning performance.
全球导航卫星系统（GNSS）和惯性导航系统（INS）集成导航可为陆地车辆实现准确可靠的定位。然而，在全球导航卫星系统中断期间，惯性传感器造成的累积误差会对全球导航卫星系统/惯性导航系统集成系统构成威胁，尤其是在使用低成本微机电系统（MEMS）惯性传感器的情况下。现有方法通常采用非整体约束（NHC）和基于机器学习（ML）/深度学习（DL）的建模技术来约束误差。然而，如果前向速度不够精确，仅使用 NHC 的性能就会受到限制，因此需要额外的约束条件，而现有的基于 ML-/DL 的建模技术仅利用加速度或角速度特征，这仍然容易受到 MEMS 传感器随机误差的影响。本文将结合加速度和调频（FM）无线电信号，采用基于 ML 的建模技术，进一步提高受 NHC 约束的 MEMS-INS/GNSS 集成系统的性能。首先，我们推导出调频信号的接收信号强度指标（RSSI）与车辆距离增量之间的关系，为所提出的算法提供理论依据。然后，提取与距离增量相关的调频信号特征。随后，提出了可用性评估策略，以消除使用 RSSI 误差较大的时刻。随后，我们结合调频信号和加速度特征，应用支持向量回归（SVR）来估计距离增量。最后，应用扩展卡尔曼滤波器（EKF），在全球导航卫星系统中断期间将预测距离与 INS 融合。结果表明，调频信号的引入可以显著减少距离估计误差，从而提高定位性能。
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Pub Date : 2024-10-16DOI: 10.1109/TIM.2024.3481543
Hong Liu;Zhixian Zhang;Haoyuan Tian;Yuxuan Song;Jianxin Wang;Zhiqing Shu;Weigen Chen
Power transformer internal partial discharge fault detection is a guarantee for the safe operation of the power system. Fiber-optic Fabry–Perot (F-P) sensors are widely used in the partial discharge detection of power equipment due to the antielectromagnetic interference, high sensitivity, and built-in fault location. Based on two different types of sensing principles, F-P sensors are divided into two categories: F-P cavity length change (indirect-coupled F-P) and medium refractive index change (direct-coupled F-P). At present, most scholars focus on indirect-coupled F-P studies, and direct-coupled F-P sensors based on the change of refractive index of the medium are less studied. This article focuses on the effect of fluid parameter variations on its internal spatial optical waveguide in the liquid-phase environment, revealing the acousto-optic direct-coupled F-P sensing mechanism in oil. Two types of F-P sensors were developed and the band characteristics, directional performance, and sensitivity of F-P sensors were comparatively tested. The results show that the indirect-coupled F-P sensors have a narrow and highly sensitive response region, typically 20–30 kHz, but are less directional and insensitive to lateral signals. Direct-coupled F-P sensors have a wide bandwidth, a wide dynamic range, a flat response in the 1–200 kHz range, and omnidirectional detectability. In this article, a comparison of partial discharge fault detection of oil-paper insulation equipment was carried out using two types of F-P sensors. The results show that indirect-coupled F-P sensors detect partial discharges with partial signal distortion, while direct-coupled F-P sensors can effectively detect different types of partial discharge ultrasonic signals of oil-paper insulated equipment.
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