A newly proposed method, Feature Mode Decomposition (FMD), can effectively enhance signal features while decomposing the signal. This feature is beneficial for analyzing weak vibration signals. However, input parameters (the segment number K, the filter length L, and the mode number n,) significantly influence the decomposition performance and efficiency. Based on the analysis of filter properties and decomposition performance of the FMD method, a step-by-step parameter-adaptive FMD method is proposed. First, parameters K and L are optimized; Secondly, parameter n is determined. In addition, a comprehensive evaluation indicator, the ratio of sample entropy and ensemble kurtosis (SEKR) is constructed considering both the periodic impact characteristics of fault signals and the noise intensity to created objective functions for each step. Compared with the methods of Variational Mode Decomposition (VMD) spectral kurtosis method and the wavelet packet(WP) decomposition, the proposed method exhibits better decomposition performance: the amplitude has increased by nearly 10 times for the simulation data and 6 times for the actual engineering data; and three evaluation factors (the crest factor, the impulse factor, and the kurtosis) have higher value. Therefore, it can be concluded that the proposed method has better superiority in identifying weak periodic fault features.
一种新提出的方法--特征模式分解（FMD）--可以在分解信号的同时有效增强信号特征。这一特征有利于分析微弱的振动信号。然而，输入参数（段数 K、滤波器长度 L 和模式数 n）对分解性能和效率有很大影响。在分析滤波器特性和 FMD 方法分解性能的基础上，提出了一种分步参数自适应 FMD 方法。首先，优化参数 K 和 L；其次，确定参数 n。此外，考虑到故障信号的周期性影响特征和噪声强度，构建了一个综合评价指标--样本熵与集合峰度之比（SEKR），以创建每一步的目标函数。与变异模态分解（VMD）频谱峰度法和小波包（WP）分解法相比，所提出的方法表现出更好的分解性能：模拟数据的振幅提高了近 10 倍，实际工程数据的振幅提高了 6 倍；三个评价因子（波峰因子、脉冲因子和峰度）的值更高。因此，可以认为所提出的方法在识别弱周期性故障特征方面具有更好的优越性。
{"title":"A step-by-step parameter-adaptive FMD method and its application in fault diagnosis","authors":"Xiangrong Wang, Congming Li, Hongying Tian, Xiaoyan Xiong","doi":"10.1088/1361-6501/ad197b","DOIUrl":"https://doi.org/10.1088/1361-6501/ad197b","url":null,"abstract":"A newly proposed method, Feature Mode Decomposition (FMD), can effectively enhance signal features while decomposing the signal. This feature is beneficial for analyzing weak vibration signals. However, input parameters (the segment number K, the filter length L, and the mode number n,) significantly influence the decomposition performance and efficiency. Based on the analysis of filter properties and decomposition performance of the FMD method, a step-by-step parameter-adaptive FMD method is proposed. First, parameters K and L are optimized; Secondly, parameter n is determined. In addition, a comprehensive evaluation indicator, the ratio of sample entropy and ensemble kurtosis (SEKR) is constructed considering both the periodic impact characteristics of fault signals and the noise intensity to created objective functions for each step. Compared with the methods of Variational Mode Decomposition (VMD) spectral kurtosis method and the wavelet packet(WP) decomposition, the proposed method exhibits better decomposition performance: the amplitude has increased by nearly 10 times for the simulation data and 6 times for the actual engineering data; and three evaluation factors (the crest factor, the impulse factor, and the kurtosis) have higher value. Therefore, it can be concluded that the proposed method has better superiority in identifying weak periodic fault features.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"269 1‐4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139152792","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 : 2023-12-28DOI: 10.1088/1361-6501/ad1808
Yujian Wu, Gang Yang, Jiangang Sun, L. Cui, Mengzhu Wang
The foundational settlement and deformation of vertical storage tanks are crucial factors influencing their safe operation. To enable rapid deformation assessment of storage tanks, this paper combines point cloud data acquired through terrestrial laser scanning with relevant data processing algorithms to construct a digital twin (DT) model. This achieves high-precision automated detection of tank deformation, facilitating the digital transformation of deformation assessment and offering an integrated detection strategy. First, Euclidean distance clustering is applied to the point cloud, and the point density within clusters is statistically analyzed using a Gaussian distribution. This results in a collection of point clusters within one standard deviation, effectively filtering out outliers and noise points, which facilitates the rapid global registration of the point cloud. Second, in order to quickly segment tank point clouds in the scene, back propagation neural network classification learning based on principal component analysis information is used. The point cloud model is combined with the fitting information of slices to generate a DT model, whose deformation can be evaluated through comparison with appropriate storage tank specifications, taking radial deformation, tank inclination, and foundation settlement as indicators.
{"title":"Construction of a digital twin model for vertical storage tank deformation assessment using terrestrial laser scanning technology","authors":"Yujian Wu, Gang Yang, Jiangang Sun, L. Cui, Mengzhu Wang","doi":"10.1088/1361-6501/ad1808","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1808","url":null,"abstract":"The foundational settlement and deformation of vertical storage tanks are crucial factors influencing their safe operation. To enable rapid deformation assessment of storage tanks, this paper combines point cloud data acquired through terrestrial laser scanning with relevant data processing algorithms to construct a digital twin (DT) model. This achieves high-precision automated detection of tank deformation, facilitating the digital transformation of deformation assessment and offering an integrated detection strategy. First, Euclidean distance clustering is applied to the point cloud, and the point density within clusters is statistically analyzed using a Gaussian distribution. This results in a collection of point clusters within one standard deviation, effectively filtering out outliers and noise points, which facilitates the rapid global registration of the point cloud. Second, in order to quickly segment tank point clouds in the scene, back propagation neural network classification learning based on principal component analysis information is used. The point cloud model is combined with the fitting information of slices to generate a DT model, whose deformation can be evaluated through comparison with appropriate storage tank specifications, taking radial deformation, tank inclination, and foundation settlement as indicators.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"44 11","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139151326","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 : 2023-12-28DOI: 10.1088/1361-6501/ad1978
Runfa Tong, Chao Liu, Yuan Tao, Xiangyang Wang, Jingqiang Sun
In high-precision global navigation satellite system (GNSS) short-baseline positioning, multipath is the main source of errors. If the station environment is quasi-static, repeat periods of satellites can be utilized to generate time- or space-dependent multipath models to mitigate multipaths. However, two general problems are associated with the multipath models constructed based on satellite mechanics: (1) an accuracy decrease occurs when the above models are applied to multipath mitigation over a long time-span; (2) when constructing the spatial and temporal grids of the satellite-based spatially dependent multipath model, it is challenging to balance computational efficiency and spatial resolution. We propose a convolutional neural network-gated recurrent unit enhanced multipath hemispherical map (ConvGRU-MHM) in the observational domain to address these problems. The proposed method directly mines the deep features of elevation, azimuth angle, and multipath and the mapping relationship between these to establish a real-time prediction model. The predicted multipath is obtained and returned to the observation equation for multipath mitigation when the real-time position of the satellite is placed in the pre-trained model. We compared the multipath mitigation performance of sidereal filtering (SF) and a multipath hemispherical map (MHM) with that of the ConvGRU-MHM to demonstrate the advantages of the proposed method. The experimental results are as follows: (1) in the short time-span (first 20 days), the mean accuracy improvements of the ConvGRU-MHM in the E/N/U direction performed better than those of the SF and MHM; and (2) in the long-term time (after 50 days), the mean accuracy improvements of the ConvGRU-MHM in the E/N/U direction are higher than that of the SF and MHM by 10–20%. As a lightweight model, the ConvGRU-MHM can effectively improve the measurement accuracy of GNSS real-time monitoring in fields, such as deformation monitoring and seismic research.
{"title":"ConvGRU-MHM: A CNN GRU-enhanced MHM for mitigating GNSS multipath","authors":"Runfa Tong, Chao Liu, Yuan Tao, Xiangyang Wang, Jingqiang Sun","doi":"10.1088/1361-6501/ad1978","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1978","url":null,"abstract":"In high-precision global navigation satellite system (GNSS) short-baseline positioning, multipath is the main source of errors. If the station environment is quasi-static, repeat periods of satellites can be utilized to generate time- or space-dependent multipath models to mitigate multipaths. However, two general problems are associated with the multipath models constructed based on satellite mechanics: (1) an accuracy decrease occurs when the above models are applied to multipath mitigation over a long time-span; (2) when constructing the spatial and temporal grids of the satellite-based spatially dependent multipath model, it is challenging to balance computational efficiency and spatial resolution. We propose a convolutional neural network-gated recurrent unit enhanced multipath hemispherical map (ConvGRU-MHM) in the observational domain to address these problems. The proposed method directly mines the deep features of elevation, azimuth angle, and multipath and the mapping relationship between these to establish a real-time prediction model. The predicted multipath is obtained and returned to the observation equation for multipath mitigation when the real-time position of the satellite is placed in the pre-trained model. We compared the multipath mitigation performance of sidereal filtering (SF) and a multipath hemispherical map (MHM) with that of the ConvGRU-MHM to demonstrate the advantages of the proposed method. The experimental results are as follows: (1) in the short time-span (first 20 days), the mean accuracy improvements of the ConvGRU-MHM in the E/N/U direction performed better than those of the SF and MHM; and (2) in the long-term time (after 50 days), the mean accuracy improvements of the ConvGRU-MHM in the E/N/U direction are higher than that of the SF and MHM by 10–20%. As a lightweight model, the ConvGRU-MHM can effectively improve the measurement accuracy of GNSS real-time monitoring in fields, such as deformation monitoring and seismic research.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"66 s94","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139150291","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 : 2023-12-28DOI: 10.1088/1361-6501/ad1813
D. Noto, H. Ulloa
Deepening our understanding of animals’ collective motions represents a multidisciplinary goal. Yet, quantifying the motions of hundreds of animals in the laboratory and nature posits a fundamental challenge for digital image processing: How do we track each object out of the crowd while allowing them to move freely in a three-dimensional (3D) domain? Here, we present a simple tracking strategy to reconstruct 3D trajectories with the aid of a mirror, even if moving objects experience occlusion. We explain the method using synthetically generated datasets and apply it to measure collective motions of phototactic zooplankton, Daphnia magna, swimming in a lab-scale aquarium at intermediate Reynolds numbers, 1
{"title":"Simple tracking of occluded self-propelled organisms","authors":"D. Noto, H. Ulloa","doi":"10.1088/1361-6501/ad1813","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1813","url":null,"abstract":"Deepening our understanding of animals’ collective motions represents a multidisciplinary goal. Yet, quantifying the motions of hundreds of animals in the laboratory and nature posits a fundamental challenge for digital image processing: How do we track each object out of the crowd while allowing them to move freely in a three-dimensional (3D) domain? Here, we present a simple tracking strategy to reconstruct 3D trajectories with the aid of a mirror, even if moving objects experience occlusion. We explain the method using synthetically generated datasets and apply it to measure collective motions of phototactic zooplankton, Daphnia magna, swimming in a lab-scale aquarium at intermediate Reynolds numbers, 1<Re<13 . The method enables measuring statistics of characteristic features of D. magna swarm, including sinking velocities and flapping frequencies. Beyond the lab-scale animal tracking, we foresee further implementations of the method to study wild animals freely behaving in 3D environments irrespective of their species.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"361 14","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139149105","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 : 2023-12-27DOI: 10.1088/1361-6501/ad191e
Ting Zhang
Weak signal detection has attracted considerable attention in a wide range of research fields, especially the weak signal detection under strong noise is an urgent problem researches are concerned with. In this paper, a new criterion to select singular values using correlation coefficient is proposed for weak exponential damped sinusoidal signal detection, which has a wide variety of signal processing applications. The innovation of our method lies in selecting K most informative singular values rather than the most energetic singular values. The proposed method measures the similarity between component signals and useful signal through autocorrelation function and correlation coefficient, which can preserve more information of the original signal and be more suitable for weak signal detection scenarios under strong noise. The numerical experiments and analysis are performed to verify the efficiency and effectiveness of our method, which indicates the proposed method is superior to the singular value selection methods based on energy or simple difference principle for correlation coefficients. Compared with stochastic resonance methods suitable for weak signal detection under strong background noise, our proposed method also has significant advantages. Thus, the proposed method is beneficial to theoretical analysis and engineering applications.
弱信号检测在众多研究领域都引起了广泛关注，尤其是强噪声下的弱信号检测更是研究人员亟待解决的问题。本文提出了一种利用相关系数选择奇异值的新准则，用于弱指数阻尼正弦信号检测，在信号处理中有着广泛的应用。我们方法的创新之处在于选择 K 个信息量最大的奇异值，而不是能量最大的奇异值。所提出的方法通过自相关函数和相关系数来衡量分量信号和有用信号之间的相似性，可以保留原始信号的更多信息，更适用于强噪声下的弱信号检测场景。通过数值实验和分析验证了我们方法的效率和有效性，表明所提出的方法优于基于能量或相关系数简单差分原理的奇异值选择方法。与适用于强背景噪声下弱信号检测的随机共振方法相比，我们提出的方法也具有显著优势。因此，我们提出的方法有利于理论分析和工程应用。
{"title":"A New Selection Method of Singular Values Using Correlation Coefficient for Weak Exponential Damped Sinusoidal Signal Detection under Strong Noise","authors":"Ting Zhang","doi":"10.1088/1361-6501/ad191e","DOIUrl":"https://doi.org/10.1088/1361-6501/ad191e","url":null,"abstract":"Weak signal detection has attracted considerable attention in a wide range of research fields, especially the weak signal detection under strong noise is an urgent problem researches are concerned with. In this paper, a new criterion to select singular values using correlation coefficient is proposed for weak exponential damped sinusoidal signal detection, which has a wide variety of signal processing applications. The innovation of our method lies in selecting K most informative singular values rather than the most energetic singular values. The proposed method measures the similarity between component signals and useful signal through autocorrelation function and correlation coefficient, which can preserve more information of the original signal and be more suitable for weak signal detection scenarios under strong noise. The numerical experiments and analysis are performed to verify the efficiency and effectiveness of our method, which indicates the proposed method is superior to the singular value selection methods based on energy or simple difference principle for correlation coefficients. Compared with stochastic resonance methods suitable for weak signal detection under strong background noise, our proposed method also has significant advantages. Thus, the proposed method is beneficial to theoretical analysis and engineering applications.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"18 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139153400","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 : 2023-12-22DOI: 10.1088/1361-6501/ad1872
Haixin Zhao, Xiao-Qiang Jiang, Bo Wang, Xueyu Chen
The early identification of bearing defects has recently attracted increasing attention in the fields of condition monitoring and predictive maintenance because of the critical role of bearings on the reliability and safety of turbomachines. The weak features representing early faults in the vibration signals are often submerged in the environmental noise, which poses a major challenge for the early fault diagnosis of rolling bearings. This study proposes a negative entropy of the square envelope spectrum approach integrated with optimized stochastic resonance-based signal enhancement for accurate early defect detection of rolling element bearings. The proposed method considers the cyclostationarity and impulsivity of the raw signal, as well as its similarity with the enhanced signal, thus reinforcing the characteristic frequency while integrating the regularity of the raw signal to evaluate the stochastic resonance performance. A comparison study with different existing methods using both numerical and experimental data was conducted to illustrate the effectiveness and accuracy of the proposed methodology for early defect detection of rolling element bearings in different locations. The results show that the proposed method improves the fault detection by 3.5 days earlier than other stochastic resonance methods, and produces the best enhancement results for fault detection in the outer race, inner race, and rolling element of bearings, with the increase of characteristic frequency intensity coefficient by 126.3%, 118.1%, and 100.5% compared to traditional envelope signals, respectively.
{"title":"Bearing fault feature extraction method: Stochastic resonance-based negative entropy of square envelope spectrum","authors":"Haixin Zhao, Xiao-Qiang Jiang, Bo Wang, Xueyu Chen","doi":"10.1088/1361-6501/ad1872","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1872","url":null,"abstract":"The early identification of bearing defects has recently attracted increasing attention in the fields of condition monitoring and predictive maintenance because of the critical role of bearings on the reliability and safety of turbomachines. The weak features representing early faults in the vibration signals are often submerged in the environmental noise, which poses a major challenge for the early fault diagnosis of rolling bearings. This study proposes a negative entropy of the square envelope spectrum approach integrated with optimized stochastic resonance-based signal enhancement for accurate early defect detection of rolling element bearings. The proposed method considers the cyclostationarity and impulsivity of the raw signal, as well as its similarity with the enhanced signal, thus reinforcing the characteristic frequency while integrating the regularity of the raw signal to evaluate the stochastic resonance performance. A comparison study with different existing methods using both numerical and experimental data was conducted to illustrate the effectiveness and accuracy of the proposed methodology for early defect detection of rolling element bearings in different locations. The results show that the proposed method improves the fault detection by 3.5 days earlier than other stochastic resonance methods, and produces the best enhancement results for fault detection in the outer race, inner race, and rolling element of bearings, with the increase of characteristic frequency intensity coefficient by 126.3%, 118.1%, and 100.5% compared to traditional envelope signals, respectively.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"80 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139164102","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 : 2023-12-22DOI: 10.1088/1361-6501/ad1873
Bingnan Hou, Yanchun Wang
WiFi-based fingerprint indoor positioning technology has been widely concerned, but it has been facing the challenge of lack of robustness to signal changes, and the positioning service requires fast and accurate positioning estimation. Therefore, an RF-KELM positioning algorithm with good comprehensive performance is proposed in this paper. Both offline and online phases are included by this algorithm. In the offline phase, the original data of WiFi fingerprint is first transformed into a form more suitable for positioning. Then, AP selection is performed on the fingerprint database containing many useless access points (APs), in which random forest algorithm (RF) which can evaluate the importance of features is used. Finally, the KELM algorithm is trained with the sub-database that have undergone data transformation and AP selection. In the online phase, firstly, the obtained signal is processed, and then the trained KELM is used to predict the position of the data processed signal. In this paper, the performance of the proposed RF-KELM positioning algorithm is thoroughly tested on a publicly available dataset, and the experimental results demonstrate that the proposed algorithm not only has high positioning accuracy and robustness, but also takes only 0.08 s to position online.
{"title":"RF-KELM Indoor Positioning Algorithm Based on WiFi RSS Fingerprint","authors":"Bingnan Hou, Yanchun Wang","doi":"10.1088/1361-6501/ad1873","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1873","url":null,"abstract":"\u0000 WiFi-based fingerprint indoor positioning technology has been widely concerned, but it has been facing the challenge of lack of robustness to signal changes, and the positioning service requires fast and accurate positioning estimation. Therefore, an RF-KELM positioning algorithm with good comprehensive performance is proposed in this paper. Both offline and online phases are included by this algorithm. In the offline phase, the original data of WiFi fingerprint is first transformed into a form more suitable for positioning. Then, AP selection is performed on the fingerprint database containing many useless access points (APs), in which random forest algorithm (RF) which can evaluate the importance of features is used. Finally, the KELM algorithm is trained with the sub-database that have undergone data transformation and AP selection. In the online phase, firstly, the obtained signal is processed, and then the trained KELM is used to predict the position of the data processed signal. In this paper, the performance of the proposed RF-KELM positioning algorithm is thoroughly tested on a publicly available dataset, and the experimental results demonstrate that the proposed algorithm not only has high positioning accuracy and robustness, but also takes only 0.08 s to position online.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"27 24","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138947142","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 : 2023-12-22DOI: 10.1088/1361-6501/ad1870
Shan Liu, Zhao Yuan, Jin Zhang, Liming Liu, Lixue Chen, Chuanqi Wu, Yuan Pan
The distribution of microscopic particles directly affect the recovery of post arc insulating medium and determine whether fault current can be interrupted. Based on the principle of vacuum arc emission spectrum, a high-speed spectrum observation platform was built to study the distribution of microscopic particles during the arc burning, and the axial and radial spectral line intensity and density distributions of atoms and ions under different current amplitudes were obtained. The results show that the CuI spectral line intensity has two peaks near the cathode and anode, while in the arc column it is relatively low. The peak of CuI spectral line intensity near the electrode is caused by electrode evaporation. The peak duration of CuI spectral line intensity near cathode is always longer than that near anode.CuII spectral line intensity is lower and appears later, disappears earlier, which in arc column increased obviously, compared with CuI. The CuII density near the cathode is higher than that near the anode, and the CuII density in the arc column is higher than two electrodes, which is similar to the axial distribution of electron density. The radial distribution curve of CuI and CuII density is similar to the radial distribution curve of spectral line intensity, showing the characteristics of high center and low edge in the radial direction of the arc gap. The difference is that the width of atomic spectral line is larger than that of corresponding ionic spectral line, the distribution curve of ionic spectral line is smooth and symmetrical, and the radial attenuation rate of CuII density is larger than that of corresponding CuI. This reflects the ionization of atoms and the effect of magnetic fields. The radial and axial distribution of particle density is opposite to that of excitation temperature. Electrode emission will first affect the atom formation.
微观粒子的分布直接影响电弧后绝缘介质的恢复，决定故障电流能否中断。基于真空电弧发射光谱原理，搭建了高速光谱观测平台，研究电弧燃烧过程中微观粒子的分布，获得了不同电流幅值下原子和离子的轴向、径向谱线强度和密度分布。结果表明，CuI 光谱线强度在阴极和阳极附近有两个峰值，而在弧柱中则相对较低。电极附近的 CuI 光谱线强度峰是由电极蒸发引起的。阴极附近 CuI 光谱线强度的峰值持续时间总是长于阳极附近。CuII 光谱线强度较低，出现较晚，消失较早，在弧柱中比 CuI 明显增加。阴极附近的 CuII 密度高于阳极附近，弧柱中的 CuII 密度高于两个电极，这与电子密度的轴向分布相似。CuI 和 CuII 密度的径向分布曲线与光谱线强度的径向分布曲线相似，在弧隙的径向方向上呈现出中心高、边缘低的特点。不同的是，原子谱线的宽度大于相应的离子谱线的宽度，离子谱线的分布曲线平滑对称，CuII 密度的径向衰减率大于相应的 CuI 密度的径向衰减率。这反映了原子的电离和磁场的影响。粒子密度的径向和轴向分布与激发温度的径向和轴向分布相反。电极发射首先会影响原子的形成。
{"title":"Observation of dynamic evolution process of vacuum arc microscopic particles based on emission spectrum principle","authors":"Shan Liu, Zhao Yuan, Jin Zhang, Liming Liu, Lixue Chen, Chuanqi Wu, Yuan Pan","doi":"10.1088/1361-6501/ad1870","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1870","url":null,"abstract":"\u0000 The distribution of microscopic particles directly affect the recovery of post arc insulating medium and determine whether fault current can be interrupted. Based on the principle of vacuum arc emission spectrum, a high-speed spectrum observation platform was built to study the distribution of microscopic particles during the arc burning, and the axial and radial spectral line intensity and density distributions of atoms and ions under different current amplitudes were obtained. The results show that the CuI spectral line intensity has two peaks near the cathode and anode, while in the arc column it is relatively low. The peak of CuI spectral line intensity near the electrode is caused by electrode evaporation. The peak duration of CuI spectral line intensity near cathode is always longer than that near anode.CuII spectral line intensity is lower and appears later, disappears earlier, which in arc column increased obviously, compared with CuI. The CuII density near the cathode is higher than that near the anode, and the CuII density in the arc column is higher than two electrodes, which is similar to the axial distribution of electron density. The radial distribution curve of CuI and CuII density is similar to the radial distribution curve of spectral line intensity, showing the characteristics of high center and low edge in the radial direction of the arc gap. The difference is that the width of atomic spectral line is larger than that of corresponding ionic spectral line, the distribution curve of ionic spectral line is smooth and symmetrical, and the radial attenuation rate of CuII density is larger than that of corresponding CuI. This reflects the ionization of atoms and the effect of magnetic fields. The radial and axial distribution of particle density is opposite to that of excitation temperature. Electrode emission will first affect the atom formation.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"16 10","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138947622","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 : 2023-12-22DOI: 10.1088/1361-6501/ad1871
Ning Jia, Weiguo Huang, Yao Cheng, Chuancang Ding, Jun Wang, Changqing Shen
Although fault diagnosis methods integrating transfer learning are research hotspots, their ability to handle industrial fault diagnosis problems with large domain differences still needs to be improved. A multi-source domain feature adaptation and selection (MDFAS) method is presented to address the issues of domain mismatch and domain negative transfer. The method integrates the top-level network parameter transfer strategy with the 2D Convolutional Neural Network (2DCNN) backbone network to acquire the target domain feature extractor quickly. Multiple feature adaptive extractors (FAEs) are constructed using a multi-branch structure to align the source and target domain's feature distributions, respectively. The inter-domain distance computed by multi-kernel maximum mean discrepancy (MK-MMD) is embedded in the FAEs loss function to improve the inter-domain matching degree. Based on the information gain of the adaptively integrated features, the ensemble adaptive selection is performed on the extracted feature matrices to exclude the negative transfer feature. Finally, the effective feature matrix is input into the diagnosis classifier for classification. Cross-domain fault diagnosis experiments are developed based on the data set gathered from several types of rotating machinery operated under varied working conditions. The experimental results show that the proposed method outperforms the existing intelligent fault diagnosis methods in terms of fault detection accuracy, generalization, and stability.
{"title":"A Cross-Domain Intelligent Fault Diagnosis Method Based on Multi-source Domain Feature Adaptation and Selection","authors":"Ning Jia, Weiguo Huang, Yao Cheng, Chuancang Ding, Jun Wang, Changqing Shen","doi":"10.1088/1361-6501/ad1871","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1871","url":null,"abstract":"\u0000 Although fault diagnosis methods integrating transfer learning are research hotspots, their ability to handle industrial fault diagnosis problems with large domain differences still needs to be improved. A multi-source domain feature adaptation and selection (MDFAS) method is presented to address the issues of domain mismatch and domain negative transfer. The method integrates the top-level network parameter transfer strategy with the 2D Convolutional Neural Network (2DCNN) backbone network to acquire the target domain feature extractor quickly. Multiple feature adaptive extractors (FAEs) are constructed using a multi-branch structure to align the source and target domain's feature distributions, respectively. The inter-domain distance computed by multi-kernel maximum mean discrepancy (MK-MMD) is embedded in the FAEs loss function to improve the inter-domain matching degree. Based on the information gain of the adaptively integrated features, the ensemble adaptive selection is performed on the extracted feature matrices to exclude the negative transfer feature. Finally, the effective feature matrix is input into the diagnosis classifier for classification. Cross-domain fault diagnosis experiments are developed based on the data set gathered from several types of rotating machinery operated under varied working conditions. The experimental results show that the proposed method outperforms the existing intelligent fault diagnosis methods in terms of fault detection accuracy, generalization, and stability.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"15 10","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944427","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 ship navigation, the most widely used technology is Global Navigation Satellite Systems (GNSS), which provide the ship’s position and velocity continuously over a period of time. However, when ships are blocked by port buildings or bridges, the quality of signals received from shipborne GNSS receivers may be reduced, resulting in inaccurate ship positioning that poses a risk to navigational safety. In an occluded environment, the measurement process during the signal processing of shipborne GNSS receivers is nonstationary and prone to measurement anomalies, which can contaminate measurement noise with outliers. To address this problem, a cascaded non-coherent vector tracking loop (VTL) is designed, with the Maximum correntropy Kalman filter (MCKF) serving as a cascaded carrier/code pre-filter for shipborne GNSS receivers. The measurement noise covariance matrix of the pre-filter is adaptively calculated and corrected using the carrier-to-noise ratio (CNR) and the maximum correntropy criterion, respectively. The algorithm proposed is more sensitive to outliers than the traditional tracking methods and can effectively solve the state estimation problem under the condition of measurement anomalies. Specifically, the algorithm offers ships with more precise position and velocity estimations and lower signal tracking errors than traditional tracking methods under both static and dynamic conditions, as demonstrated by shipboard experiments. The horizontal positioning is increased by 88.4% and the horizontal velocity error is reduced by 62.1% in the occluded environment under dynamic conditions.
{"title":"A MCKF-based Cascade Vector Tracking Method Designed for Ship Navigation","authors":"Wei Liu, Panting Ma, Yuan hu, Xingdi Wang, Tsung-Hsuan Hsieh, Bing Han, Shengzheng Wang","doi":"10.1088/1361-6501/ad1874","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1874","url":null,"abstract":"\u0000 In ship navigation, the most widely used technology is Global Navigation Satellite Systems (GNSS), which provide the ship’s position and velocity continuously over a period of time. However, when ships are blocked by port buildings or bridges, the quality of signals received from shipborne GNSS receivers may be reduced, resulting in inaccurate ship positioning that poses a risk to navigational safety. In an occluded environment, the measurement process during the signal processing of shipborne GNSS receivers is nonstationary and prone to measurement anomalies, which can contaminate measurement noise with outliers. To address this problem, a cascaded non-coherent vector tracking loop (VTL) is designed, with the Maximum correntropy Kalman filter (MCKF) serving as a cascaded carrier/code pre-filter for shipborne GNSS receivers. The measurement noise covariance matrix of the pre-filter is adaptively calculated and corrected using the carrier-to-noise ratio (CNR) and the maximum correntropy criterion, respectively. The algorithm proposed is more sensitive to outliers than the traditional tracking methods and can effectively solve the state estimation problem under the condition of measurement anomalies. Specifically, the algorithm offers ships with more precise position and velocity estimations and lower signal tracking errors than traditional tracking methods under both static and dynamic conditions, as demonstrated by shipboard experiments. The horizontal positioning is increased by 88.4% and the horizontal velocity error is reduced by 62.1% in the occluded environment under dynamic conditions.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"60 12","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138945877","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}
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