Pub Date : 2024-07-15DOI: 10.1088/1361-6501/ad6344
D. Bai, Gongfa Li, Du Jiang, Guozhang Jiang, Zhiqiang Hao, Dalin Zhou, Zhaojie Ju
Advances in the field of measurement science and technology have improved the detection of defects in industrial production. One of the key challenges in steel plate surface defect detection is the need to quickly detect a small number of defects in an overwhelmingly defect-free sample. Unlike supervised learning, which relies heavily on precise sample labeling, unsupervised learning leverages its inherent learning capabilities for detection. This paper introduces an innovative method for smart steel diagnosis, integrating joint optimization of feature extraction and clustering. The proposed approach merges mini-batch K-Means clustering with a feature extraction network to acquire pseudo-label information for current images. It employs a multi-view transformation strategy, enabling classification through the optimized feedback from pseudo-labels. This method allows the network to self-optimize the distinction of image features through backpropagation. The method exhibits a mere 4% classification failure rate for steel surface images. This significant reduction in additional data processing requirements enhances the inspection system's efficiency and accuracy. Furthermore, the versatility of this method extends beyond steel defect diagnosis. It holds potential for application in various engineering domains, particularly in scenarios characterized by data imbalance.
{"title":"Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering","authors":"D. Bai, Gongfa Li, Du Jiang, Guozhang Jiang, Zhiqiang Hao, Dalin Zhou, Zhaojie Ju","doi":"10.1088/1361-6501/ad6344","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6344","url":null,"abstract":"\u0000 Advances in the field of measurement science and technology have improved the detection of defects in industrial production. One of the key challenges in steel plate surface defect detection is the need to quickly detect a small number of defects in an overwhelmingly defect-free sample. Unlike supervised learning, which relies heavily on precise sample labeling, unsupervised learning leverages its inherent learning capabilities for detection. This paper introduces an innovative method for smart steel diagnosis, integrating joint optimization of feature extraction and clustering. The proposed approach merges mini-batch K-Means clustering with a feature extraction network to acquire pseudo-label information for current images. It employs a multi-view transformation strategy, enabling classification through the optimized feedback from pseudo-labels. This method allows the network to self-optimize the distinction of image features through backpropagation. The method exhibits a mere 4% classification failure rate for steel surface images. This significant reduction in additional data processing requirements enhances the inspection system's efficiency and accuracy. Furthermore, the versatility of this method extends beyond steel defect diagnosis. It holds potential for application in various engineering domains, particularly in scenarios characterized by data imbalance.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-07-15DOI: 10.1088/1361-6501/ad6340
Jiafu Wang, Xianwen Yu
Ambiguity resolution (AR) is fundamental to achieve high-precision solution in GNSS (Global Navigation Satellite System) relative positioning. Extensive research has shown that systematic errors are associated with the performance of AR. However, due to the physical complexity, some systematic errors would inevitably remain in the observation equations even after processed with some popular models and parameterization. In the medium and long baselines, these unmodeled errors are the leading cause of the slow or even incorrect fixation of ambiguity. Therefore, to improve the AR performance in the medium and long baselines, we present a procedure with the careful consideration of unmodeled errors. At first, we develop a method to estimate the unmodeled errors based on the float ambiguity bias. Then, the overall procedure and key steps to fix the float solutions corrected by the unmodeled error estimate is designed. Finally, some real-measured baselines (from 68 km to 120 km) are utilized to validate the proposed procedure. The experimental results are analyzed and discussed from the aspects of AR and positioning, respectively. For the AR performance, the time required for the first fixation have been reduced by about 41.58% to 83.51%, from 12 to 100 min. Besides, 12.72% to 48.59% and 2.96% to 36.28% improvements of the ambiguity-fixed rate and the ambiguity-correct rate can be respectively obtained in the four baselines. As for the positioning performance, the mean values and RMSEs have improved by 0.2 to 4.8 cm (1.63% to 22.43%) and 0.2 to 2.8 cm (1.47% to 10.57%), respectively.
{"title":"Improving the ambiguity resolution with the consideration of unmodeled errors in GNSS medium and long baselines","authors":"Jiafu Wang, Xianwen Yu","doi":"10.1088/1361-6501/ad6340","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6340","url":null,"abstract":"\u0000 Ambiguity resolution (AR) is fundamental to achieve high-precision solution in GNSS (Global Navigation Satellite System) relative positioning. Extensive research has shown that systematic errors are associated with the performance of AR. However, due to the physical complexity, some systematic errors would inevitably remain in the observation equations even after processed with some popular models and parameterization. In the medium and long baselines, these unmodeled errors are the leading cause of the slow or even incorrect fixation of ambiguity. Therefore, to improve the AR performance in the medium and long baselines, we present a procedure with the careful consideration of unmodeled errors. At first, we develop a method to estimate the unmodeled errors based on the float ambiguity bias. Then, the overall procedure and key steps to fix the float solutions corrected by the unmodeled error estimate is designed. Finally, some real-measured baselines (from 68 km to 120 km) are utilized to validate the proposed procedure. The experimental results are analyzed and discussed from the aspects of AR and positioning, respectively. For the AR performance, the time required for the first fixation have been reduced by about 41.58% to 83.51%, from 12 to 100 min. Besides, 12.72% to 48.59% and 2.96% to 36.28% improvements of the ambiguity-fixed rate and the ambiguity-correct rate can be respectively obtained in the four baselines. As for the positioning performance, the mean values and RMSEs have improved by 0.2 to 4.8 cm (1.63% to 22.43%) and 0.2 to 2.8 cm (1.47% to 10.57%), respectively.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-07-15DOI: 10.1088/1361-6501/ad633e
Kai Sun, Yuling He, Xue-wei Wu, Hao-ran Luo, Ling-yu Jiao, David Gerada
Synchronous generators are widely used in power generation systems. Static air-gap eccentricity (SAGE) often occurs in synchronous generators due to the component wear over prolonged operation. This paper presents a comprehensive mathematical model specifically tailored for SAGE fault, incorporating for the influence of stator slotting. The study thoroughly examines the impacts of both eccentricity and varying loads on the shaft voltage using the developed model. Furthermore, a novel method for detecting SAGE is introduced, leveraging the mathematical model of shaft voltage. This detection method proves effective for identifying eccentricity in synchronous generators across different load conditions by reasonably combining shaft voltage and phase current. The mathematical model of shaft voltage and the proposed detection method are validated through three-dimensional finite-element calculations and experimental studies. The work is helpful to manage and predict the shaft voltage. This paper contributes to the prevention of shaft voltage damage and real-time monitoring of the SAGE fault in synchronous generators.
{"title":"An Investigation of Shaft Voltage in Synchronous Generators Under SAGE and Variable Load Condition","authors":"Kai Sun, Yuling He, Xue-wei Wu, Hao-ran Luo, Ling-yu Jiao, David Gerada","doi":"10.1088/1361-6501/ad633e","DOIUrl":"https://doi.org/10.1088/1361-6501/ad633e","url":null,"abstract":"\u0000 Synchronous generators are widely used in power generation systems. Static air-gap eccentricity (SAGE) often occurs in synchronous generators due to the component wear over prolonged operation. This paper presents a comprehensive mathematical model specifically tailored for SAGE fault, incorporating for the influence of stator slotting. The study thoroughly examines the impacts of both eccentricity and varying loads on the shaft voltage using the developed model. Furthermore, a novel method for detecting SAGE is introduced, leveraging the mathematical model of shaft voltage. This detection method proves effective for identifying eccentricity in synchronous generators across different load conditions by reasonably combining shaft voltage and phase current. The mathematical model of shaft voltage and the proposed detection method are validated through three-dimensional finite-element calculations and experimental studies. The work is helpful to manage and predict the shaft voltage. This paper contributes to the prevention of shaft voltage damage and real-time monitoring of the SAGE fault in synchronous generators.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-07-15DOI: 10.1088/1361-6501/ad6346
T. Cai, Ruiyu Fu, Di Luan, Yingzheng Liu, Di Peng
In this study, we propose a three-dimensional (3D) surface temperature measurement method based on the principle of stereoscopic 3D reconstruction and the dependence of phosphorescence lifetime on temperature. A 385-nm UV(Ultraviolet) light was used as the excitation light, and two high-speed cameras were used as the detectors. The phosphor MFG (Mg4FGeO6: Mn4+) was mixed with the binder HPC and sprayed onto the tested 3D surface. The natural texture generated by the surface roughness of the phosphor coating was used as a feature for cross-correlation calculations. The digital image correlation (DIC) algorithm was used to match these feature positions in the phosphorescent images from the two cameras. The effects of the excitation angle and detecting angle were analyzed. The results indicate that the temperature measurement based on phosphorescent lifetime was not affected by the excitation and detecting angle. The method was validated on a turbine blade as an example of a 3D surface to demonstrate the capability. A comparison of the measurement results with the thermocouples proves that the current method can successfully measure the temperature on 3D surfaces with a maximum difference of 1.63°C. The spatial accuracy of the method was obtained by comparing with the measurement results of a 3D scanner, which shows that the maximum absolute error of the 3D reconstruction was 0.350 mm. The current study proposes a promising 3D surface temperature measurement method, which is expected to be widely used in gas turbine blades, Internal Combustion (IC) engine cylinders, complex curved heat exchangers, and other fields due to its non-contact measurement, low susceptibility to infrared radiation interference, high measurement accuracy, and ability to withstand harsh environments.
{"title":"Three-dimensional surface temperature measurement using lifetime-based phosphor thermometry","authors":"T. Cai, Ruiyu Fu, Di Luan, Yingzheng Liu, Di Peng","doi":"10.1088/1361-6501/ad6346","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6346","url":null,"abstract":"\u0000 In this study, we propose a three-dimensional (3D) surface temperature measurement method based on the principle of stereoscopic 3D reconstruction and the dependence of phosphorescence lifetime on temperature. A 385-nm UV(Ultraviolet) light was used as the excitation light, and two high-speed cameras were used as the detectors. The phosphor MFG (Mg4FGeO6: Mn4+) was mixed with the binder HPC and sprayed onto the tested 3D surface. The natural texture generated by the surface roughness of the phosphor coating was used as a feature for cross-correlation calculations. The digital image correlation (DIC) algorithm was used to match these feature positions in the phosphorescent images from the two cameras. The effects of the excitation angle and detecting angle were analyzed. The results indicate that the temperature measurement based on phosphorescent lifetime was not affected by the excitation and detecting angle. The method was validated on a turbine blade as an example of a 3D surface to demonstrate the capability. A comparison of the measurement results with the thermocouples proves that the current method can successfully measure the temperature on 3D surfaces with a maximum difference of 1.63°C. The spatial accuracy of the method was obtained by comparing with the measurement results of a 3D scanner, which shows that the maximum absolute error of the 3D reconstruction was 0.350 mm. The current study proposes a promising 3D surface temperature measurement method, which is expected to be widely used in gas turbine blades, Internal Combustion (IC) engine cylinders, complex curved heat exchangers, and other fields due to its non-contact measurement, low susceptibility to infrared radiation interference, high measurement accuracy, and ability to withstand harsh environments.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In response to the issue of poor Network Real-Time Kinematic (NRTK) service performance in regions with significant height differences, an Improved Tropospheric Height Correction (ITHC) method is proposed. Precise Point Positioning (PPP) is employed to compute the troposphere delay at base stations. Subsequently, a Tropospheric Vertical Profile Fitting Model (TVPFM) is established for the vertical reduction of the troposphere in regions with significant height differences. In this case, the tropospheric errors introduced by the height differences between the base and rover stations can be calculated. Finally, the tropospheric error can be corrected during the generation of virtual observations, ensuring high-accuracy positioning of NRTK rovers. With the troposphere delay computed based on the PPP approach, datum errors introduced by inaccurate tropospheric correction methods are mitigated. To reduce the dependence of the troposphere delay on height, ECMWF Reanalysis v5 (ERA5) data are employed to fit the TVPFM. Experimental analysis demonstrates that the troposphere exhibits distinct vertical variation characteristics, allowing for its segmentation into three layers. Consequently, a piecewise TVPFM is established. Observations obtained from the Continuously Operating Reference Stations (CORS) network located in Yunnan, China, are utilized for validation. The selected stations exhibit a maximum height difference of approximately 2 km. The experimental results exhibit a notable enhancement in correction accuracy with the ITHC in comparison to conventional correction methodologies. Specifically, the ambiguity fixing rate demonstrates a noteworthy improvement of 13.3%, accompanied by a substantial increase in positioning accuracy by 51.4%.
{"title":"High-Precision Tropospheric Correction Method for NRTK Regions with Significant Height Differences","authors":"xiaoting lei, Xiaolong Xu, Jun Tao, Tianyu Yang, Qile Zhao, Jing Guo","doi":"10.1088/1361-6501/ad6343","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6343","url":null,"abstract":"\u0000 In response to the issue of poor Network Real-Time Kinematic (NRTK) service performance in regions with significant height differences, an Improved Tropospheric Height Correction (ITHC) method is proposed. Precise Point Positioning (PPP) is employed to compute the troposphere delay at base stations. Subsequently, a Tropospheric Vertical Profile Fitting Model (TVPFM) is established for the vertical reduction of the troposphere in regions with significant height differences. In this case, the tropospheric errors introduced by the height differences between the base and rover stations can be calculated. Finally, the tropospheric error can be corrected during the generation of virtual observations, ensuring high-accuracy positioning of NRTK rovers. With the troposphere delay computed based on the PPP approach, datum errors introduced by inaccurate tropospheric correction methods are mitigated. To reduce the dependence of the troposphere delay on height, ECMWF Reanalysis v5 (ERA5) data are employed to fit the TVPFM. Experimental analysis demonstrates that the troposphere exhibits distinct vertical variation characteristics, allowing for its segmentation into three layers. Consequently, a piecewise TVPFM is established. Observations obtained from the Continuously Operating Reference Stations (CORS) network located in Yunnan, China, are utilized for validation. The selected stations exhibit a maximum height difference of approximately 2 km. The experimental results exhibit a notable enhancement in correction accuracy with the ITHC in comparison to conventional correction methodologies. Specifically, the ambiguity fixing rate demonstrates a noteworthy improvement of 13.3%, accompanied by a substantial increase in positioning accuracy by 51.4%.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-07-15DOI: 10.1088/1361-6501/ad6342
Anaïs Guerenneur, Dmitry Kouznetsov, Daniele Narducci, Federica Luciano, Xiao Sun, Pol Van Dorpe, F. Ciubotaru, Christoph Adelmann
An equivalent fitting analysis scheme is proposed for extending a method developed for high-k dielectrics to correctly characterise low-k dielectrics, which are more sensitive to various parasitics. The same concentric capacitor devices and measurement setup are used as in the original method, as they are easy to fabricate, which made the old approach attractive in the first place. The physical model used in the analysis step of the original method, which overestimates the dielectric permittivity, is improved by implementing fringing fields and a parasitic gap capacitance as a circuit element. The new approach is verified on experimental data and is demonstrated to more accurately determine the dielectric permittivity compared to the original method.
本文提出了一种等效拟合分析方案,将针对高 k 电介质开发的方法扩展到正确描述低 k 电介质的特性,因为低 k 电介质对各种寄生现象更为敏感。使用的同心电容器器件和测量设置与原始方法相同,因为它们易于制造,这也是旧方法的吸引力所在。原始方法的分析步骤中使用的物理模型高估了介电常数,而新方法则通过将频闪场和寄生间隙电容作为电路元素加以改进。新方法在实验数据上进行了验证,证明与原始方法相比,新方法能更准确地确定介电常数。
{"title":"Equivalent circuit fitting method for microwave characterisation of low-k dielectric thin films","authors":"Anaïs Guerenneur, Dmitry Kouznetsov, Daniele Narducci, Federica Luciano, Xiao Sun, Pol Van Dorpe, F. Ciubotaru, Christoph Adelmann","doi":"10.1088/1361-6501/ad6342","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6342","url":null,"abstract":"\u0000 An equivalent fitting analysis scheme is proposed for extending a method developed for high-k dielectrics to correctly characterise low-k dielectrics, which are more sensitive to various parasitics. The same concentric capacitor devices and measurement setup are used as in the original method, as they are easy to fabricate, which made the old approach attractive in the first place. The physical model used in the analysis step of the original method, which overestimates the dielectric permittivity, is improved by implementing fringing fields and a parasitic gap capacitance as a circuit element. The new approach is verified on experimental data and is demonstrated to more accurately determine the dielectric permittivity compared to the original method.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurately and efficiently detecting steel surface defects is a critical step in steel manufacturing. However, the compromise between the detection speed and accuracy remains a major challenge, especially for steel surface defects with large variations in the scale. To address the issue, an improved YOLO based detection model is proposed through the reinforcement of its backbone and neck. Firstly, for the reduction of the redundant parameters and also the improvement of the characterization ability of the model, an effective channel residual structure is adopted to construct a channel residual convolution module (CRCM) and channel residual cross stage partial (CRCSP) module as components of the backbone network, respectively. They realize the extraction of both the shallow feature and multi-scale feature simultaneously under a small number of convolutional parameters. Secondly, in the neck of YOLO, a fusion-distribution (FD) strategy is employed, which extracts and fuses multi-scale feature maps from the backbone network to provide global information, and then distributes global information into local features of different branches through an inject attention mechanism, thus enhancing the feature gap between different branches. Then, a model called CRFD-YOLO is derived for the steel surface defect detection and localization for the situations where both speed and accuracy are demanding. Finally, extensive experimental validations are conducted to evaluate the performance of CRFD-YOLO. The validation results indicate that CRFD-YOLO achieves a satisfactory detection performance with a mean average precision of 81.3% on the NEU-DET and 71.1% on the GC10-DET. Additionally, CRFD-YOLO achieves a speed of 161 frames per second, giving a great potential in real-time detection and localization tasks.
{"title":"A high-speed YOLO detection model for steel surface defects with the channel residual convolution and fusion-distribution","authors":"建行 Huang 黄, Xinliang Zhang, Lijie Jia, Yitian Zhou","doi":"10.1088/1361-6501/ad6281","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6281","url":null,"abstract":"\u0000 Accurately and efficiently detecting steel surface defects is a critical step in steel manufacturing. However, the compromise between the detection speed and accuracy remains a major challenge, especially for steel surface defects with large variations in the scale. To address the issue, an improved YOLO based detection model is proposed through the reinforcement of its backbone and neck. Firstly, for the reduction of the redundant parameters and also the improvement of the characterization ability of the model, an effective channel residual structure is adopted to construct a channel residual convolution module (CRCM) and channel residual cross stage partial (CRCSP) module as components of the backbone network, respectively. They realize the extraction of both the shallow feature and multi-scale feature simultaneously under a small number of convolutional parameters. Secondly, in the neck of YOLO, a fusion-distribution (FD) strategy is employed, which extracts and fuses multi-scale feature maps from the backbone network to provide global information, and then distributes global information into local features of different branches through an inject attention mechanism, thus enhancing the feature gap between different branches. Then, a model called CRFD-YOLO is derived for the steel surface defect detection and localization for the situations where both speed and accuracy are demanding. Finally, extensive experimental validations are conducted to evaluate the performance of CRFD-YOLO. The validation results indicate that CRFD-YOLO achieves a satisfactory detection performance with a mean average precision of 81.3% on the NEU-DET and 71.1% on the GC10-DET. Additionally, CRFD-YOLO achieves a speed of 161 frames per second, giving a great potential in real-time detection and localization tasks.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-07-12DOI: 10.1088/1361-6501/ad627f
Lin Du, Feng Hui Feng, Xin Li, Xianjun Shao, Zhi Yang
With the increasing proportion of new energy and the power electronic equipment in the power grid, accurate measurement of harmonic voltage has become increasingly important for power quality monitoring. In order to solve the problem of high-precision measurement of harmonic voltage in the power grid, this manuscript proposes a high-precision harmonic voltage measurement method based on the dielectric equivalent model (DEM) of capacitive equipment and its responding current. Based on DEM, a voltage-current transfer function of the capacitive device is established, and harmonic voltage is reconstructed with the responding current. Considering the dielectric relaxation characteristics of capacitive device other than a pure capacitor model, this manuscript analyzes the fitting performance of different equivalent capacitance models and improves the traditional pure capacitance model to a more suitable DEM for harmonic voltage reconstruction. The DEM parameters of capacitive devices are obtained through the frequency domain spectroscopy (FDS) and intelligent parameter identification algorithms, which improved the measurement accuracy of harmonic voltage and reduced computational complexity. The harmonic voltage testing platform is established to test the simulated high-voltage harmonics and the harmonic voltage of the actual grid voltage. The results show that the proposed harmonic voltage measurement method can meet the high-precision reconstruction of harmonic voltage in the frequency range of 50~2500Hz, and the system testing error with sensors is less than 2%. The testing accuracy is higher than traditional voltage transformers and testing systems based on pure capacitance models.
随着新能源和电力电子设备在电网中所占比例的不断增加,谐波电压的精确测量对于电能质量监测变得越来越重要。为了解决电网中谐波电压的高精度测量问题,本手稿提出了一种基于电容式设备的介质等效模型(DEM)及其响应电流的高精度谐波电压测量方法。基于 DEM,建立了电容式设备的电压-电流传递函数,并利用响应电流重建谐波电压。考虑到纯电容器模型以外的电容设备介电弛豫特性,本稿件分析了不同等效电容模型的拟合性能,并将传统的纯电容模型改进为更适合谐波电压重建的 DEM。通过频域光谱(FDS)和智能参数识别算法获得电容器件的 DEM 参数,提高了谐波电压的测量精度,降低了计算复杂度。建立谐波电压测试平台,测试模拟高压谐波和实际电网电压的谐波电压。结果表明,所提出的谐波电压测量方法可满足 50~2500Hz 频率范围内谐波电压的高精度重建,且系统测试与传感器的误差小于 2%。测试精度高于传统的电压互感器和基于纯电容模型的测试系统。
{"title":"Harmonic Voltage Measurement Based on Capacitive Equipment Dielectric Equivalent Model and Responding Current","authors":"Lin Du, Feng Hui Feng, Xin Li, Xianjun Shao, Zhi Yang","doi":"10.1088/1361-6501/ad627f","DOIUrl":"https://doi.org/10.1088/1361-6501/ad627f","url":null,"abstract":"\u0000 With the increasing proportion of new energy and the power electronic equipment in the power grid, accurate measurement of harmonic voltage has become increasingly important for power quality monitoring. In order to solve the problem of high-precision measurement of harmonic voltage in the power grid, this manuscript proposes a high-precision harmonic voltage measurement method based on the dielectric equivalent model (DEM) of capacitive equipment and its responding current. Based on DEM, a voltage-current transfer function of the capacitive device is established, and harmonic voltage is reconstructed with the responding current. Considering the dielectric relaxation characteristics of capacitive device other than a pure capacitor model, this manuscript analyzes the fitting performance of different equivalent capacitance models and improves the traditional pure capacitance model to a more suitable DEM for harmonic voltage reconstruction. The DEM parameters of capacitive devices are obtained through the frequency domain spectroscopy (FDS) and intelligent parameter identification algorithms, which improved the measurement accuracy of harmonic voltage and reduced computational complexity. The harmonic voltage testing platform is established to test the simulated high-voltage harmonics and the harmonic voltage of the actual grid voltage. The results show that the proposed harmonic voltage measurement method can meet the high-precision reconstruction of harmonic voltage in the frequency range of 50~2500Hz, and the system testing error with sensors is less than 2%. The testing accuracy is higher than traditional voltage transformers and testing systems based on pure capacitance models.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-07-12DOI: 10.1088/1361-6501/ad6282
Jun Wu, Shuo Huang, Shaobo Yuan, Long Jin, Runxia Guo, Jiusheng Chen
In the current methods of point cloud processing, there are still several limitations, particularly in achieving high precision and accuracy for large objects in complex environments. Existing techniques often struggle with incomplete or noisy data, leading to inaccurate contour extraction. In view of the challenges associated with the sparse and discrete nature of point clouds in complex environments, which lead to poor accuracy and stability in object contour extraction, this paper proposes a novel method for accurately extracting the contours of three-dimensional target point clouds. The method integrates high-resolution images with sparse point cloud information to address these issues. Firstly, the local characteristics of the point cloud are calculated, allowing for the selection of a contour point cloud. Next, depth information from two-dimensional images is obtained through a fuzzy mapping relationship. Finally, constraint conditions are established to derive a more accurate predicted value of the contour point cloud. Experiments demonstrate that the proposed method effectively improves the precision and accuracy of contour extraction for large objects, reducing measurement deviation by approximately 64.9% compared to using the original point cloud alone. Additionally, the method shows a more accurate completion effect on parts of the contour that are missing, underscoring its robustness and effectiveness in challenging scenarios.
{"title":"Three-dimensional contour detection method based on fusion of machine vision and laser radar","authors":"Jun Wu, Shuo Huang, Shaobo Yuan, Long Jin, Runxia Guo, Jiusheng Chen","doi":"10.1088/1361-6501/ad6282","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6282","url":null,"abstract":"\u0000 In the current methods of point cloud processing, there are still several limitations, particularly in achieving high precision and accuracy for large objects in complex environments. Existing techniques often struggle with incomplete or noisy data, leading to inaccurate contour extraction. In view of the challenges associated with the sparse and discrete nature of point clouds in complex environments, which lead to poor accuracy and stability in object contour extraction, this paper proposes a novel method for accurately extracting the contours of three-dimensional target point clouds. The method integrates high-resolution images with sparse point cloud information to address these issues. Firstly, the local characteristics of the point cloud are calculated, allowing for the selection of a contour point cloud. Next, depth information from two-dimensional images is obtained through a fuzzy mapping relationship. Finally, constraint conditions are established to derive a more accurate predicted value of the contour point cloud. Experiments demonstrate that the proposed method effectively improves the precision and accuracy of contour extraction for large objects, reducing measurement deviation by approximately 64.9% compared to using the original point cloud alone. Additionally, the method shows a more accurate completion effect on parts of the contour that are missing, underscoring its robustness and effectiveness in challenging scenarios.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141652244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Supervised data-driven bearing fault diagnosis methods rely on completed datasets of faults, which can be challenging for signals collected in real engineering. Recognizing unknown faults using a data-driven approach is particularly difficult, as purposefully modeling these faults is complex. To address this challenge, this study proposes a new simulation-assisted transfer bearing unknown fault diagnosis method for realizing unknown compound fault diagnosis of rotating machinery. Firstly, finite element method is used to obtain the compound fault data that does not exist in the historical data, and wavelet packet transform is performed on the simulated and measured signals to enhance the detailed features of the signals. Then, a deep convolutional feature fusion network based on hybrid multi-wavelet spatial attention is constructed to fuse the time-frequency information processed by different wavelet bases. Finally, by integrating the concepts of intra-class splitting and transfer learning, the model is fine-tuned using simulation data to recognize unknown compound faults of rolling bearings. The method validates the simulated signals’ feasibility and the unknown faults’ diagnostic validity under the publicly available rolling bearings dataset. Compared to the comparison methods, the method’s accuracy increased by 2.86%, 2.61%, 5.41%, 4.77%, and 7.07%, respectively.
{"title":"A novel simulation-assisted transfer method for bearing unknown fault diagnosis","authors":"Fengfei Huang, Xianxin Li, Kai Zhang, Qing Zheng, Jiahao Ma, Guofu Ding","doi":"10.1088/1361-6501/ad6280","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6280","url":null,"abstract":"\u0000 Supervised data-driven bearing fault diagnosis methods rely on completed datasets of faults, which can be challenging for signals collected in real engineering. Recognizing unknown faults using a data-driven approach is particularly difficult, as purposefully modeling these faults is complex. To address this challenge, this study proposes a new simulation-assisted transfer bearing unknown fault diagnosis method for realizing unknown compound fault diagnosis of rotating machinery. Firstly, finite element method is used to obtain the compound fault data that does not exist in the historical data, and wavelet packet transform is performed on the simulated and measured signals to enhance the detailed features of the signals. Then, a deep convolutional feature fusion network based on hybrid multi-wavelet spatial attention is constructed to fuse the time-frequency information processed by different wavelet bases. Finally, by integrating the concepts of intra-class splitting and transfer learning, the model is fine-tuned using simulation data to recognize unknown compound faults of rolling bearings. The method validates the simulated signals’ feasibility and the unknown faults’ diagnostic validity under the publicly available rolling bearings dataset. Compared to the comparison methods, the method’s accuracy increased by 2.86%, 2.61%, 5.41%, 4.77%, and 7.07%, respectively.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141652968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}