Pub Date : 2024-07-23DOI: 10.1088/1361-6501/ad6687
Huachuan Zhao, Zicheng Wang, Guochen Wang, Fei Yu
During ship operations at sea, the vessel's attitude undergoes continuous changes due to various factors such as wind, waves, and its own motion. These influences are challenging to mathematically describe, and the changes in attitude are also influenced by multiple interconnected factors. Consequently, accurately predicting the ship's attitude presents significant challenges. Previous studies have demonstrated that phenomena like wind speed and wave patterns exhibit chaotic characteristics when affecting attitude changes. However, research on predicting ship attitudes lacks an exploration of whether chaotic characteristics exist and how they can be described and applied. This paper initially identifies the chaotic characteristics of ship attitude data through phase space reconstruction analysis and provides mathematical representations for them. Based on these identified chaotic characteristics, a Transformer model incorporating feature embedding layers is employed for time series prediction. Finally, a comparison with traditional methods validates the superiority of our proposed approach.
{"title":"Dynamic Chaos Unveiled: Enhancing Ship's Attitude Time Series Prediction through Spatiotemporal Embedding and Improved Transformer Model","authors":"Huachuan Zhao, Zicheng Wang, Guochen Wang, Fei Yu","doi":"10.1088/1361-6501/ad6687","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6687","url":null,"abstract":"\u0000 During ship operations at sea, the vessel's attitude undergoes continuous changes due to various factors such as wind, waves, and its own motion. These influences are challenging to mathematically describe, and the changes in attitude are also influenced by multiple interconnected factors. Consequently, accurately predicting the ship's attitude presents significant challenges. Previous studies have demonstrated that phenomena like wind speed and wave patterns exhibit chaotic characteristics when affecting attitude changes. However, research on predicting ship attitudes lacks an exploration of whether chaotic characteristics exist and how they can be described and applied. This paper initially identifies the chaotic characteristics of ship attitude data through phase space reconstruction analysis and provides mathematical representations for them. Based on these identified chaotic characteristics, a Transformer model incorporating feature embedding layers is employed for time series prediction. Finally, a comparison with traditional methods validates the superiority of our proposed approach.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"140 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141811154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1088/1361-6501/ad6682
Fan Chen, Haotian Wei, Yong Li, Luming Wang, Lushuai Xu, Shaohua Dong, Hang Zhang
As an essential means of energy transportation, pipelines have been widely used in various fields. However, many external factors such as vibration and corrosion can cause damage at the flange part, which seriously affects the safety of pipeline transportation. Quite a number of methods for troubleshooting at pipeline flanges have been continuously proposed, yet there is little research on diagnostic methods for the stabilizer at the flange. Therefore, in this paper, we focus on the stabilizer of the flange and a method that combines traditional detection and machine learning with each other to detect stabilizer faults is proposed. At first, we can obtain a stable and reliable diagnostic data by combining the advantages of the preload of the bolt and the acoustic signal. Subsequently, the optimized N-Beats model is trained based on the measured bolt preload data to predict the service state of the stabilizer. Finally, the data measured by the sensors as well as the predicted data are analyzed by a simplified classification algorithm to determine whether a fault has occurred and to classify the fault. The fault detection method used in this paper not only improves the accuracy of detection and shortens the fault detection time, but also improves the automation level of pipeline inspection. Hence, the work done in this paper has far-reaching practical significance for ensuring the safe and stable operation of pipelines.
{"title":"A Fault Diagnosis Approach for Flange Stabilizer Based on Multi-Signal Fusion","authors":"Fan Chen, Haotian Wei, Yong Li, Luming Wang, Lushuai Xu, Shaohua Dong, Hang Zhang","doi":"10.1088/1361-6501/ad6682","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6682","url":null,"abstract":"\u0000 As an essential means of energy transportation, pipelines have been widely used in various fields. However, many external factors such as vibration and corrosion can cause damage at the flange part, which seriously affects the safety of pipeline transportation. Quite a number of methods for troubleshooting at pipeline flanges have been continuously proposed, yet there is little research on diagnostic methods for the stabilizer at the flange. Therefore, in this paper, we focus on the stabilizer of the flange and a method that combines traditional detection and machine learning with each other to detect stabilizer faults is proposed. At first, we can obtain a stable and reliable diagnostic data by combining the advantages of the preload of the bolt and the acoustic signal. Subsequently, the optimized N-Beats model is trained based on the measured bolt preload data to predict the service state of the stabilizer. Finally, the data measured by the sensors as well as the predicted data are analyzed by a simplified classification algorithm to determine whether a fault has occurred and to classify the fault. The fault detection method used in this paper not only improves the accuracy of detection and shortens the fault detection time, but also improves the automation level of pipeline inspection. Hence, the work done in this paper has far-reaching practical significance for ensuring the safe and stable operation of pipelines.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"21 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liquid jet in crossflow tunnel has widespread applications in various industrial devices, with the measurements of liquid film on the bottom surface pivotal in exploring relevant mechanisms such as heat transfer and film breakup. This work reports the measurements of liquid film on the bottom surface of a crossflow tunnel using the brightness-based laser induced fluorescence (LIF) method under different flow conditions at ambient pressure and temperature. Film breakup phenomena are observed downstream within the tunnel. Employing the shadowgraph method, two distinct patterns of film breakup associated with the droplet impingement positions on the film wave are identified, i.e., bag breakup and membrane breakup. The film thickness is subsequently calculated, and jet impingement and spray impingement of injected liquid on tunnel bottom surface are classified based on the centerline film thickness. A critical jet-to-crossflow momentum flux ratio (q) is determined to be proportional to the square of tunnel height. The averaged film thickness across the entire cross-section downstream at a distance of 50 mm from the nozzle is found to increase with the logarithm of q. Besides, the film boundaries are also identified under different flow conditions, which can be well predicted by a quadratic fit with the fitting parameters also correlated to the logarithm of q.
{"title":"Evolution of liquid film in a crossflow tunnel: Liquid film thickness measurement and effect of droplet impingement on film breakup","authors":"Tianyu Li, Xiaoyuan Yang, Bingyao Huang, Tianyou Lian, Wei Li, Yuyang Li","doi":"10.1088/1361-6501/ad6683","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6683","url":null,"abstract":"\u0000 Liquid jet in crossflow tunnel has widespread applications in various industrial devices, with the measurements of liquid film on the bottom surface pivotal in exploring relevant mechanisms such as heat transfer and film breakup. This work reports the measurements of liquid film on the bottom surface of a crossflow tunnel using the brightness-based laser induced fluorescence (LIF) method under different flow conditions at ambient pressure and temperature. Film breakup phenomena are observed downstream within the tunnel. Employing the shadowgraph method, two distinct patterns of film breakup associated with the droplet impingement positions on the film wave are identified, i.e., bag breakup and membrane breakup. The film thickness is subsequently calculated, and jet impingement and spray impingement of injected liquid on tunnel bottom surface are classified based on the centerline film thickness. A critical jet-to-crossflow momentum flux ratio (q) is determined to be proportional to the square of tunnel height. The averaged film thickness across the entire cross-section downstream at a distance of 50 mm from the nozzle is found to increase with the logarithm of q. Besides, the film boundaries are also identified under different flow conditions, which can be well predicted by a quadratic fit with the fitting parameters also correlated to the logarithm of q.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"74 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1088/1361-6501/ad6685
Ronghua Chen, Yingkui Gu, Guangqi Qiu, Peng Huang
This paper proposes a novel dynamic model considering manufacturing errors and eccentricity errors to analyze the dynamics of planetary gear transmission (PGT). The dynamic model is established based on the fractional-order calculus (FOC) and solved by an enhanced fourth-order Lagrange interpolation polynomials (LIP) method. Three numerical examples and the vibration experiments of planetary gear transmission are employed for verification. The comparison results indicate that the proposed solution method has higher solution accuracy and efficient than the existing algorithms in solving fractional equations, and the relative errors of the proposed solution method in three examples are 0.32%, 0.78% and 0.16%, respectively. The proposed dynamic model of PGT has better agreement with the experimentally measured signal compared with the integer-order dynamic model, and the maximum error and average error of the characteristic frequency amplitude between the proposed dynamic model and the measured signal are 4.76% and 3.57%, respectively. The proposed method contributes to the theoretical foundation for the signal monitoring of PGT.
{"title":"Dynamic analysis of planetary gear transmission based on Lagrange interpolation polynomials","authors":"Ronghua Chen, Yingkui Gu, Guangqi Qiu, Peng Huang","doi":"10.1088/1361-6501/ad6685","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6685","url":null,"abstract":"\u0000 This paper proposes a novel dynamic model considering manufacturing errors and eccentricity errors to analyze the dynamics of planetary gear transmission (PGT). The dynamic model is established based on the fractional-order calculus (FOC) and solved by an enhanced fourth-order Lagrange interpolation polynomials (LIP) method. Three numerical examples and the vibration experiments of planetary gear transmission are employed for verification. The comparison results indicate that the proposed solution method has higher solution accuracy and efficient than the existing algorithms in solving fractional equations, and the relative errors of the proposed solution method in three examples are 0.32%, 0.78% and 0.16%, respectively. The proposed dynamic model of PGT has better agreement with the experimentally measured signal compared with the integer-order dynamic model, and the maximum error and average error of the characteristic frequency amplitude between the proposed dynamic model and the measured signal are 4.76% and 3.57%, respectively. The proposed method contributes to the theoretical foundation for the signal monitoring of PGT.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"6 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1088/1361-6501/ad6684
Yan Zhang, Cao Jie, Xiaomei Zhao, Yongyong Hui
Batch processes play an important role in modern chemical industrial and manufacturing production, while the control of product quality relies largely on online quality prediction. However, the complex nonlinearities of batch process and the dispersion of quality-related features may affect the quality prediction performance. In this paper, a deep quality-related stacked isomorphic autoencoder for batch process quality prediction is proposed. Firstly, the same raw input data is reconstructed layer-by-layer by isomorphic autoencoder and the raw data features are obtained. Secondly, the correlation between the isomorphic representations of each layer and the output is analyzed by maximum information coefficient to construct the relevant loss function and enhance the quality-related information. Thirdly, deep quality-related prediction model is constructed to predict the batch process quality variables. Finally, the effectiveness of the proposed method is verified by applying on penicillin fermentation process.
{"title":"Deep quality-related stacked isomorphic autoencoder for batch process quality prediction","authors":"Yan Zhang, Cao Jie, Xiaomei Zhao, Yongyong Hui","doi":"10.1088/1361-6501/ad6684","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6684","url":null,"abstract":"\u0000 Batch processes play an important role in modern chemical industrial and manufacturing production, while the control of product quality relies largely on online quality prediction. However, the complex nonlinearities of batch process and the dispersion of quality-related features may affect the quality prediction performance. In this paper, a deep quality-related stacked isomorphic autoencoder for batch process quality prediction is proposed. Firstly, the same raw input data is reconstructed layer-by-layer by isomorphic autoencoder and the raw data features are obtained. Secondly, the correlation between the isomorphic representations of each layer and the output is analyzed by maximum information coefficient to construct the relevant loss function and enhance the quality-related information. Thirdly, deep quality-related prediction model is constructed to predict the batch process quality variables. Finally, the effectiveness of the proposed method is verified by applying on penicillin fermentation process.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"6 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kalman or Kalman-related filtering methods are routinely applied in precise point positioning (PPP). However, in robot simultaneous localization and mapping (SLAM) systems, the factor graph optimization (FGO) has proved advantages over filtering methods in recent years, e.g., reducing the linearization errors and support of plug-and-play feature for multiple sensor fusion. Therefore, it would be interesting to apply the FGO to PPP. In addition, it will also facilitate the tight integration of PPP with Visual/LiDAR SLAM. In this work, PPP is solved under the factor graph optimization framework. A factor graph for PPP has been constructed. Results from 268 IGS-MGEX stations show that the factor graph optimization method can achieve a similar performance with that of Kalman filtering. First, the positioning accuracy in the convergence period can be improved for PPP based on factor graph optimization because it optimizes the entire state variables based on all the available observations. For applications that do not require real-time processing, the observation after the current states, e.g., future observations, can also be used to enhance the current state estimation. Second, the accuracy of static PPP is almost the same for the two methods with millimeter-accuracy for horizontal directions and centimeter-accuracy for vertical directions. Third, the kinematic PPP for both methods can achieve centimeter-level accuracy in horizontal directions and decimeter-level accuracy in vertical directions. Although the performance is comparable, it is noted that the computational efficiency of factor graph optimization method is still a problem. For each epoch, the average of elapsed time for Kalman filtering is 132 microseconds, while that of factor graph optimization method is 9664 microseconds. The elapsed time of factor graph optimization method can be further improved if the fix-window optimization technique is applied, which will be investigated in the future.
{"title":"PPP based on factor graph optimization","authors":"Guorui Xiao, Zhengyang Xiao, Peiyuan Zhou, Xiaoxue Jia, Ningbo Wang, Dongqing Zhao, Haopeng Wei","doi":"10.1088/1361-6501/ad6680","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6680","url":null,"abstract":"\u0000 Kalman or Kalman-related filtering methods are routinely applied in precise point positioning (PPP). However, in robot simultaneous localization and mapping (SLAM) systems, the factor graph optimization (FGO) has proved advantages over filtering methods in recent years, e.g., reducing the linearization errors and support of plug-and-play feature for multiple sensor fusion. Therefore, it would be interesting to apply the FGO to PPP. In addition, it will also facilitate the tight integration of PPP with Visual/LiDAR SLAM. In this work, PPP is solved under the factor graph optimization framework. A factor graph for PPP has been constructed. Results from 268 IGS-MGEX stations show that the factor graph optimization method can achieve a similar performance with that of Kalman filtering. First, the positioning accuracy in the convergence period can be improved for PPP based on factor graph optimization because it optimizes the entire state variables based on all the available observations. For applications that do not require real-time processing, the observation after the current states, e.g., future observations, can also be used to enhance the current state estimation. Second, the accuracy of static PPP is almost the same for the two methods with millimeter-accuracy for horizontal directions and centimeter-accuracy for vertical directions. Third, the kinematic PPP for both methods can achieve centimeter-level accuracy in horizontal directions and decimeter-level accuracy in vertical directions. Although the performance is comparable, it is noted that the computational efficiency of factor graph optimization method is still a problem. For each epoch, the average of elapsed time for Kalman filtering is 132 microseconds, while that of factor graph optimization method is 9664 microseconds. The elapsed time of factor graph optimization method can be further improved if the fix-window optimization technique is applied, which will be investigated in the future.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"118 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141811879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High-resolution ultrasonic imaging for defects in anisotropic multilayer carbon fiber reinforced polymers (CFRPs) is challenging because of the severe ultrasonic attenuation and the low signal-to-noise ratio (SNR) of echoes. The existing delay-multiply-and-sum (DMAS) beamforming outperforms delay-and-sum (DAS) beamforming in resolution, but with high computational complexity and energy loss. This paper presents a novel delay-sum-and-square (DSAS) beamforming algorithm. It takes full advantage of spatial coherence of captured data in the receiving and transmitting apertures. The non-coherent components caused by background noise are suppressed during the imaging. The back-wall reflection method (BRM) is used to correct the direction-dependent velocity. Full-matrix data is experimentally captured and processed on three different CFRP samples. Compared with DAS and DMAS, DSAS has a significant improvement in resolution, SNR and contrast. It demonstrates excellent defect characterization and noise suppression capability with only 17.4% computation time of DMAS.
{"title":"High-resolution defect imaging of composites using delay-sum-and-square beamforming algorithm","authors":"Junhui Zhao, Haiyan Zhang, Hui Zhang, Yiting Chen, Wenfa Zhu, Qi Zhu","doi":"10.1088/1361-6501/ad667f","DOIUrl":"https://doi.org/10.1088/1361-6501/ad667f","url":null,"abstract":"\u0000 High-resolution ultrasonic imaging for defects in anisotropic multilayer carbon fiber reinforced polymers (CFRPs) is challenging because of the severe ultrasonic attenuation and the low signal-to-noise ratio (SNR) of echoes. The existing delay-multiply-and-sum (DMAS) beamforming outperforms delay-and-sum (DAS) beamforming in resolution, but with high computational complexity and energy loss. This paper presents a novel delay-sum-and-square (DSAS) beamforming algorithm. It takes full advantage of spatial coherence of captured data in the receiving and transmitting apertures. The non-coherent components caused by background noise are suppressed during the imaging. The back-wall reflection method (BRM) is used to correct the direction-dependent velocity. Full-matrix data is experimentally captured and processed on three different CFRP samples. Compared with DAS and DMAS, DSAS has a significant improvement in resolution, SNR and contrast. It demonstrates excellent defect characterization and noise suppression capability with only 17.4% computation time of DMAS.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"82 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Point cloud registration techniques based on marker points are widely used in optical 3D industrial measurements. However, in this process, marker points 3D matching methods are often haunted by low efficiency and accuracy. To improve the performance of marker points 3D matching, we propose a two-step method of “matching-verification”. In the matching process, Delaunay triangulation is introduced to extract the 3D structure of the marker points set, and then the 3D structure is deconstructed into 2D units for matching, which simplifies complexity and improves the efficiency of the algorithm. In the verification process, the mismatched pairs of points are located and removed by the method that is based on the error dispersion of initial matched results, and the initial transformation results are iteratively verified to obtain the optimal transformation matrix. The experimental results show that our method takes an average of 2.2s for each matching, the average error of coarse registration point cloud is 0.075mm and the RMS is 0.219mm, which effectively solves the problem of the low efficiency and accuracy of marker points 3D matching methods.
{"title":"Marker Points 3D Matching Based on Delaunay Triangulation Structure and Error Dispersion","authors":"Ruidi Jin, Zhao Wang, Junhui Huang, Zijun Li, Qiongqiong Duan, M. Qi, Wei Wang, Qiang Dong","doi":"10.1088/1361-6501/ad667e","DOIUrl":"https://doi.org/10.1088/1361-6501/ad667e","url":null,"abstract":"\u0000 Point cloud registration techniques based on marker points are widely used in optical 3D industrial measurements. However, in this process, marker points 3D matching methods are often haunted by low efficiency and accuracy. To improve the performance of marker points 3D matching, we propose a two-step method of “matching-verification”. In the matching process, Delaunay triangulation is introduced to extract the 3D structure of the marker points set, and then the 3D structure is deconstructed into 2D units for matching, which simplifies complexity and improves the efficiency of the algorithm. In the verification process, the mismatched pairs of points are located and removed by the method that is based on the error dispersion of initial matched results, and the initial transformation results are iteratively verified to obtain the optimal transformation matrix. The experimental results show that our method takes an average of 2.2s for each matching, the average error of coarse registration point cloud is 0.075mm and the RMS is 0.219mm, which effectively solves the problem of the low efficiency and accuracy of marker points 3D matching methods.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"8 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1088/1361-6501/ad667d
Xizhi Sun, E. Heaps, A. Yacoot, Qingping Yang, Petr Grolich, P. Klapetek
Non-raster scanning can increase the scanning frame rate and measurement speed of atomic force microscopes (AFMs). It is also possible to correct the 3D drift during the non-raster scanning. However, the algorithm for the drift correction depends upon the properties of each scan pattern. While localised non-raster scanning using a rosette scan may be faster than the frequently used Lissajous scanning patterns, the drift correction is more challenging because the scan has crossing points only in local neighbouring segments where there are short temporal and spatial separations of the crossing paths. This design note presents a novel solution that successfully overcomes this problem and extends a drift correction method previously developed for Lissajous scans to the 3D drift correction of localised non-raster scanning using a rosette scan trajectory. The drift in the X, Y and Z axes can be determined using the crossing points and locally repeated scans of the same features. The general procedure is presented together with experiments using rosette scans of a two-dimensional lateral calibration standard. Experimental results have demonstrated that the method can effectively correct both the drift in the three axes and sample tilt, leading to significantly improved images. The method requires only localised crossing points in the scan and does not need additional scans to determine the three-dimensional drift based on cross-correlation and least squares techniques, and it can be used with any AFMs capable of rosette scanning.
非光栅扫描可以提高原子力显微镜（AFM）的扫描帧频和测量速度。在非栅格扫描过程中还可以纠正三维漂移。不过，漂移校正算法取决于每个扫描模式的特性。虽然使用轮状扫描的局部非光栅扫描可能比常用的利萨如斯扫描模式更快，但漂移校正更具挑战性，因为扫描仅在局部相邻区段有交叉点，而交叉路径的时空间隔很短。本设计说明提出了一种新的解决方案，成功地克服了这一问题，并将以前为利萨如扫描开发的漂移校正方法扩展到使用玫瑰花扫描轨迹进行局部非光栅扫描的三维漂移校正。通过交叉点和对相同地物的局部重复扫描，可以确定 X、Y 和 Z 轴的漂移。在介绍一般程序的同时，还利用二维横向校准标准的轮状扫描进行了实验。实验结果表明，该方法能有效纠正三轴漂移和样本倾斜，从而显著改善图像质量。该方法只需要扫描中的局部交叉点，而不需要额外的扫描来确定基于交叉相关和最小二乘法技术的三维漂移，它可用于任何能够进行轮状扫描的原子力显微镜。
{"title":"Three-dimensional drift correction of localised non-raster scanning on atomic force microscopy","authors":"Xizhi Sun, E. Heaps, A. Yacoot, Qingping Yang, Petr Grolich, P. Klapetek","doi":"10.1088/1361-6501/ad667d","DOIUrl":"https://doi.org/10.1088/1361-6501/ad667d","url":null,"abstract":"\u0000 Non-raster scanning can increase the scanning frame rate and measurement speed of atomic force microscopes (AFMs). It is also possible to correct the 3D drift during the non-raster scanning. However, the algorithm for the drift correction depends upon the properties of each scan pattern. While localised non-raster scanning using a rosette scan may be faster than the frequently used Lissajous scanning patterns, the drift correction is more challenging because the scan has crossing points only in local neighbouring segments where there are short temporal and spatial separations of the crossing paths. This design note presents a novel solution that successfully overcomes this problem and extends a drift correction method previously developed for Lissajous scans to the 3D drift correction of localised non-raster scanning using a rosette scan trajectory. The drift in the X, Y and Z axes can be determined using the crossing points and locally repeated scans of the same features. The general procedure is presented together with experiments using rosette scans of a two-dimensional lateral calibration standard. Experimental results have demonstrated that the method can effectively correct both the drift in the three axes and sample tilt, leading to significantly improved images. The method requires only localised crossing points in the scan and does not need additional scans to determine the three-dimensional drift based on cross-correlation and least squares techniques, and it can be used with any AFMs capable of rosette scanning.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"100 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In response to the deficiencies of traditional power transformer fault detection techniques, such as low sensitivity and the inability for online monitoring, a novel transformer fault diagnosis model combining Laser-Induced Fluorescence (LIF) technology with deep learning is proposed. Initially, the spectral data of transformer insulation oil is acquired using LIF technology, yielding spectral data for various fault types. Subsequently, MinMaxScaler (MMS) and Standard Normalized Variate (SNV) methods are employed for denoising and preprocessing the spectral data. The preprocessed data is then subjected to dimensionality reduction using Linear Discriminant Analysis (LDA) and T-distributed Stochastic Neighbor Embedding (T-SNE) to ensure that the spectral data retains maximal feature information while minimizing its dimensionality. Following this, Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), Dung Beetle Optimizer-Bi-directional Long Short Term Memory (DBO-BiLSTM), Convolutional Neural Network (CNN), and Support Vector Machine (SVM) models are constructed. The reduced-dimensional data is fed into each of the five models for training to facilitate transformer fault diagnosis. Through comparative analysis among the five models, the optimal model is selected. Experimental results indicate that the DBO-BiLSTM model is the most suitable for transformer fault diagnosis in this experiment, underscoring its significant implications for ensuring the safety of power systems.
针对传统电力变压器故障检测技术灵敏度低、无法在线监测等缺陷，提出了一种结合激光诱导荧光（LIF）技术和深度学习的新型变压器故障诊断模型。首先，利用激光诱导荧光技术获取变压器绝缘油的光谱数据，得到各种故障类型的光谱数据。随后，采用 MinMaxScaler（MMS）和标准归一化变量（SNV）方法对光谱数据进行去噪和预处理。然后使用线性判别分析法（LDA）和 T 分布随机邻域嵌入法（T-SNE）对预处理后的数据进行降维处理，以确保频谱数据在最小化维数的同时保留最大的特征信息。然后，构建长短期记忆（LSTM）、双向长短期记忆（BiLSTM）、蜣螂优化器-双向长短期记忆（DBO-BiLSTM）、卷积神经网络（CNN）和支持向量机（SVM）模型。将降维数据分别输入五个模型进行训练，以促进变压器故障诊断。通过对五个模型的比较分析，选出了最优模型。实验结果表明，在本实验中，DBO-BiLSTM 模型最适合用于变压器故障诊断，凸显了其对确保电力系统安全的重要意义。
{"title":"Transformer fault diagnosis based on DBO-BiLSTM algorithm and LIF technology","authors":"Peng-cheng Yan, JingBao Wang, Wenchang Wang, Guo-dong Li, Yuting Zhao, Ziming Wen","doi":"10.1088/1361-6501/ad6686","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6686","url":null,"abstract":"\u0000 In response to the deficiencies of traditional power transformer fault detection techniques, such as low sensitivity and the inability for online monitoring, a novel transformer fault diagnosis model combining Laser-Induced Fluorescence (LIF) technology with deep learning is proposed. Initially, the spectral data of transformer insulation oil is acquired using LIF technology, yielding spectral data for various fault types. Subsequently, MinMaxScaler (MMS) and Standard Normalized Variate (SNV) methods are employed for denoising and preprocessing the spectral data. The preprocessed data is then subjected to dimensionality reduction using Linear Discriminant Analysis (LDA) and T-distributed Stochastic Neighbor Embedding (T-SNE) to ensure that the spectral data retains maximal feature information while minimizing its dimensionality. Following this, Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), Dung Beetle Optimizer-Bi-directional Long Short Term Memory (DBO-BiLSTM), Convolutional Neural Network (CNN), and Support Vector Machine (SVM) models are constructed. The reduced-dimensional data is fed into each of the five models for training to facilitate transformer fault diagnosis. Through comparative analysis among the five models, the optimal model is selected. Experimental results indicate that the DBO-BiLSTM model is the most suitable for transformer fault diagnosis in this experiment, underscoring its significant implications for ensuring the safety of power systems.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"33 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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