Pub Date : 2024-07-02DOI: 10.1088/1361-6501/ad5dde
Jian Liu, yong jiang, Ziyi Wang, Chongliang Zou, Chenguang Liu
Subsurface defects (SSD) in optical components pose a significant challenge for enhancing the power density of high-energy laser devices. This study investigated the issue of systematic deviation between the measured and actual depths of subsurface defects when employing optical dark-field confocal microscopy for three-dimensional measurements, which is attributed to refractive index disparities between the sample and the observation environment. This paper introduces geometric and diffraction optical models for correcting errors in the subsurface defect depth, along with a calculation method for determining the correction coefficient. By comparing the experimental data and model simulations, a linear relationship between the measured and actual depths was identified with linearity errors below 2.5% and a minimum of 0.67%. The correction coefficients derived from the optical diffraction model are in good agreement with those obtained experimentally. These findings offer valuable insights for calculating subsurface defect depth correction coefficients across various scenarios and requirements to ensure precise measurements.
{"title":"Depth localization of subsurface defects by optical dark-field confocal microscopy","authors":"Jian Liu, yong jiang, Ziyi Wang, Chongliang Zou, Chenguang Liu","doi":"10.1088/1361-6501/ad5dde","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5dde","url":null,"abstract":"\u0000 Subsurface defects (SSD) in optical components pose a significant challenge for enhancing the power density of high-energy laser devices. This study investigated the issue of systematic deviation between the measured and actual depths of subsurface defects when employing optical dark-field confocal microscopy for three-dimensional measurements, which is attributed to refractive index disparities between the sample and the observation environment. This paper introduces geometric and diffraction optical models for correcting errors in the subsurface defect depth, along with a calculation method for determining the correction coefficient. By comparing the experimental data and model simulations, a linear relationship between the measured and actual depths was identified with linearity errors below 2.5% and a minimum of 0.67%. The correction coefficients derived from the optical diffraction model are in good agreement with those obtained experimentally. These findings offer valuable insights for calculating subsurface defect depth correction coefficients across various scenarios and requirements to ensure precise measurements.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684673","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 : 2024-07-02DOI: 10.1088/1361-6501/ad5747
Tong Qu, Changchun Chai, Jiahui Guo, Shuai Wang, Zhuohang Ye, Zehao Li, Xiaojun Liu
Structured illumination microscopy (SIM) can achieve optical sectioning with high resolution, and have aroused extensive research interest. In SIM, a set of high-contrast illumination patterns are projected onto the sample to modulate the surface height information, and then, a decoding algorithm is applied to the modulated pattern images for high-quality optical sectioning. Applied to samples with large dynamic range of reflectivity, however, SIM may fail to achieve high quality sectioning for accurate surface reconstruction. Herein, an active digital micromirror device (DMD) based illumination method using per-pixel coded strategy is proposed in SIM to realize high-quality measurement for surface with complex reflection characteristics. In this method, the mapping relationship between DMD and the camera is established pixels by pixels, which enables the illumination intensity on the sample surface can be flexibly modulated by DMD pixel-level modulation corresponding to reflectivity distribution of the surface, and allows the camera pixels always to have reasonable exposure intensity for high precision measurement. More importantly, we put forward an adaptive light intensity control algorithm to improves the signal-to-noise ratio of acquired images without compromising modulation depth of pattern and measurement efficiency. Extensive comparative experiments were conducted and demonstrated that the proposed method can retrieve the surface morphology information of micro-scale complex reflectivity surfaces with high accuracy.
{"title":"High dynamic range structured illumination microscopy based on per-pixel coding","authors":"Tong Qu, Changchun Chai, Jiahui Guo, Shuai Wang, Zhuohang Ye, Zehao Li, Xiaojun Liu","doi":"10.1088/1361-6501/ad5747","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5747","url":null,"abstract":"Structured illumination microscopy (SIM) can achieve optical sectioning with high resolution, and have aroused extensive research interest. In SIM, a set of high-contrast illumination patterns are projected onto the sample to modulate the surface height information, and then, a decoding algorithm is applied to the modulated pattern images for high-quality optical sectioning. Applied to samples with large dynamic range of reflectivity, however, SIM may fail to achieve high quality sectioning for accurate surface reconstruction. Herein, an active digital micromirror device (DMD) based illumination method using per-pixel coded strategy is proposed in SIM to realize high-quality measurement for surface with complex reflection characteristics. In this method, the mapping relationship between DMD and the camera is established pixels by pixels, which enables the illumination intensity on the sample surface can be flexibly modulated by DMD pixel-level modulation corresponding to reflectivity distribution of the surface, and allows the camera pixels always to have reasonable exposure intensity for high precision measurement. More importantly, we put forward an adaptive light intensity control algorithm to improves the signal-to-noise ratio of acquired images without compromising modulation depth of pattern and measurement efficiency. Extensive comparative experiments were conducted and demonstrated that the proposed method can retrieve the surface morphology information of micro-scale complex reflectivity surfaces with high accuracy.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141688642","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 : 2024-07-02DOI: 10.1088/1361-6501/ad5dd8
Yuntao Wei, T. Yi, Dong‐Hui Yang, Hong‐Nan Li, Hua Liu
Bridge responses that are excited by high-speed trains have the characteristics of high amplitude, high cycle, and large dynamic effects, which greatly affect the fatigue bearing capacity of affected bridges. To achieve reliable analysis of the fatigue performance of high-speed railway bridges, this study developed a bridge fatigue life prediction method based on the reconstruction of the train-induced dynamic stress time history. First, the equations for solving the static stress time history under influence line virtual loading are derived, and then the dynamic stress time history reconstruction method based on two types of dynamic correction factors is proposed. The statistical characteristics of the train loads and dynamic correction factors are fit according to monitoring data, and bridge fatigue life prediction is realized by use of the reliability theory. Finally, the applicability and effectiveness of the proposed method are verified by using a train-bridge interaction model and monitoring data from a long-span high-speed railway bridge. The results show that the proposed method can greatly improve the accuracy of fatigue performance analysis and can effectively predict the fatigue life of high-speed railway bridges under complex loads. These results can provide an important reference for fatigue evaluation of high-speed railway bridges.
{"title":"Fatigue life prediction for high-speed railway bridges by reconstructing monitoring-based dynamic stress","authors":"Yuntao Wei, T. Yi, Dong‐Hui Yang, Hong‐Nan Li, Hua Liu","doi":"10.1088/1361-6501/ad5dd8","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5dd8","url":null,"abstract":"\u0000 Bridge responses that are excited by high-speed trains have the characteristics of high amplitude, high cycle, and large dynamic effects, which greatly affect the fatigue bearing capacity of affected bridges. To achieve reliable analysis of the fatigue performance of high-speed railway bridges, this study developed a bridge fatigue life prediction method based on the reconstruction of the train-induced dynamic stress time history. First, the equations for solving the static stress time history under influence line virtual loading are derived, and then the dynamic stress time history reconstruction method based on two types of dynamic correction factors is proposed. The statistical characteristics of the train loads and dynamic correction factors are fit according to monitoring data, and bridge fatigue life prediction is realized by use of the reliability theory. Finally, the applicability and effectiveness of the proposed method are verified by using a train-bridge interaction model and monitoring data from a long-span high-speed railway bridge. The results show that the proposed method can greatly improve the accuracy of fatigue performance analysis and can effectively predict the fatigue life of high-speed railway bridges under complex loads. These results can provide an important reference for fatigue evaluation of high-speed railway bridges.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685306","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 : 2024-07-02DOI: 10.1088/1361-6501/ad5dda
Qingqing Zhang, Lingling Gong, Kang Tian, Zhenao Jian
Many load identification methods have been proposed, but most are affected by the basic axle parameters and lateral distribution of vehicles. To effectively measure traffic flow with lateral distribution information, this article presents an innovative method that employs a strain decoupling model (SDM) and a vehicle information identification model (VIDM) to measure multi-lane vehicle load depending on the bending strain and shear strain from long-gauge fiber bragg grating (FBG) sensors. The SDM decouples the measured coupling strain into the strain for a single lane load, thereby simplifying the complex structural response resulting from lateral distributed vehicles. By exploiting the distinct characteristics of different strain types that reflect various aspects of the structure, the VIDM establishes a sophisticated mapping relationship between bending, shear strain and axle parameters, which enables the accurate determination of axle parameters including axle speed and spacing. The real-time estimation of the multi-lane vehicle load is achieved by combining the obtained axle information with the decoupled bending strain. This method effectively solves the problem of large load estimation error caused by inaccurate identification of axle parameters, and enables accurate acquisition of vehicle load in lateral distribution using bending and shear strains near the bridge entrance. Both numerical studies and laboratory tests are carried out on a simply supported beam for conceptual verification. The results demonstrate that the proposed method successfully improves the measurement of multi-lane vehicle load.
{"title":"Multi-lane vehicle load measurement using bending and shear strains","authors":"Qingqing Zhang, Lingling Gong, Kang Tian, Zhenao Jian","doi":"10.1088/1361-6501/ad5dda","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5dda","url":null,"abstract":"\u0000 Many load identification methods have been proposed, but most are affected by the basic axle parameters and lateral distribution of vehicles. To effectively measure traffic flow with lateral distribution information, this article presents an innovative method that employs a strain decoupling model (SDM) and a vehicle information identification model (VIDM) to measure multi-lane vehicle load depending on the bending strain and shear strain from long-gauge fiber bragg grating (FBG) sensors. The SDM decouples the measured coupling strain into the strain for a single lane load, thereby simplifying the complex structural response resulting from lateral distributed vehicles. By exploiting the distinct characteristics of different strain types that reflect various aspects of the structure, the VIDM establishes a sophisticated mapping relationship between bending, shear strain and axle parameters, which enables the accurate determination of axle parameters including axle speed and spacing. The real-time estimation of the multi-lane vehicle load is achieved by combining the obtained axle information with the decoupled bending strain. This method effectively solves the problem of large load estimation error caused by inaccurate identification of axle parameters, and enables accurate acquisition of vehicle load in lateral distribution using bending and shear strains near the bridge entrance. Both numerical studies and laboratory tests are carried out on a simply supported beam for conceptual verification. The results demonstrate that the proposed method successfully improves the measurement of multi-lane vehicle load.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684367","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 : 2024-07-02DOI: 10.1088/1361-6501/ad5deb
Xiru Liu, Changfeng Yan, Ming Lv, Shen Li, Lixiao Wu
In industrial production, rolling bearings are widely used as key mechanical components in all types of rotating machinery. Fault diagnosis is essential for predicting bearing damage in advance, avoiding sudden equipment downtime and reducing economic losses. However, rolling element fault diagnosis of rolling bearings continues to be a challenge, especially with multi-rolling element faults. In view of the characteristics of randomness, weakness, and coupling in the vibration signal generated by multi-rolling element faults in rolling bearings, a multi-rolling element fault detection method is proposed by combination time-frequency (TF) analysis (TFA) with multi-curves extraction methods. The pre-processing method combined autoregressive model with maximum correlated kurtosis deconvolution is employed to enhance the weak periodic fault impulses in the raw vibration signals of the rolling bearing. Then an improved dynamic path multi-curves extraction method is proposed to extract multiple TF curves from the TF spectrogram (TFS) constructed via short-time Fourier transform. According to the proposed classification criteria, the TF curves are classified as homologous faults. The TF masking (TFM) method is employed to keep TF information closely associated with the fault impulse. Finally, the fault signals are reconstructed sequentially based on the TFS processed by TFM, and precise identification of multi-rolling element faults is achieved by envelope analysis. Experimental results demonstrate the effectiveness of the proposed method in extracting the weak fault features of multi-rolling elements and accomplishing fault separation and diagnosis.
{"title":"Multi-rolling element faults diagnosis of rolling bearing based on time-frequency analysis and multi-curves extraction","authors":"Xiru Liu, Changfeng Yan, Ming Lv, Shen Li, Lixiao Wu","doi":"10.1088/1361-6501/ad5deb","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5deb","url":null,"abstract":"\u0000 In industrial production, rolling bearings are widely used as key mechanical components in all types of rotating machinery. Fault diagnosis is essential for predicting bearing damage in advance, avoiding sudden equipment downtime and reducing economic losses. However, rolling element fault diagnosis of rolling bearings continues to be a challenge, especially with multi-rolling element faults. In view of the characteristics of randomness, weakness, and coupling in the vibration signal generated by multi-rolling element faults in rolling bearings, a multi-rolling element fault detection method is proposed by combination time-frequency (TF) analysis (TFA) with multi-curves extraction methods. The pre-processing method combined autoregressive model with maximum correlated kurtosis deconvolution is employed to enhance the weak periodic fault impulses in the raw vibration signals of the rolling bearing. Then an improved dynamic path multi-curves extraction method is proposed to extract multiple TF curves from the TF spectrogram (TFS) constructed via short-time Fourier transform. According to the proposed classification criteria, the TF curves are classified as homologous faults. The TF masking (TFM) method is employed to keep TF information closely associated with the fault impulse. Finally, the fault signals are reconstructed sequentially based on the TFS processed by TFM, and precise identification of multi-rolling element faults is achieved by envelope analysis. Experimental results demonstrate the effectiveness of the proposed method in extracting the weak fault features of multi-rolling elements and accomplishing fault separation and diagnosis.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684700","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 : 2024-07-02DOI: 10.1088/1361-6501/ad5de9
Zhengming Xiao, chengjunyi li, Tao Liu, Wenbin Liu, Shuai Mo, H. Houjoh
Rotating machinery will inevitably fail under long-term heavy load working conditions. Obtaining enough data to train the deep learning model can enable managers to detect and deal with related failures in time, which greatly improves the safety of equipment operation. Mechanical fault samples are often much smaller than healthy samples. Traditional data enhancement methods mostly change the original data, but cannot improve the diversity of its features, so that the number of fault samples becomes larger, but the features remain unchanged. Aiming at the above problems, a diffusion model based on parameter sharing and inverted bottleneck residual structure (DDPM) is proposed. Firstly, the diffusion process gradually covers the original data with Gaussian noise, to learn the corresponding fault characteristics of the original data. In the diffusion process, the parameter sharing attention mechanism is embedded in the learning process of the diffusion process. Then, the feature extraction module is constructed by using the inverted bottleneck residual structure to enhance the learning ability of the network. After obtaining the fault characteristics of the original data, the reverse process of the results restores the Gaussian noise to data with different fault characteristics through the same steps as the diffusion process. By comparing the results of various generation models and analysing the characteristics of the generated data, the feasibility and universality of the proposed method in data augmentation tasks are verified.
{"title":"Parameter Sharing Fault Data Generation Method Based on Diffusion Model Under Imbalance Data","authors":"Zhengming Xiao, chengjunyi li, Tao Liu, Wenbin Liu, Shuai Mo, H. Houjoh","doi":"10.1088/1361-6501/ad5de9","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5de9","url":null,"abstract":"\u0000 Rotating machinery will inevitably fail under long-term heavy load working conditions. Obtaining enough data to train the deep learning model can enable managers to detect and deal with related failures in time, which greatly improves the safety of equipment operation. Mechanical fault samples are often much smaller than healthy samples. Traditional data enhancement methods mostly change the original data, but cannot improve the diversity of its features, so that the number of fault samples becomes larger, but the features remain unchanged. Aiming at the above problems, a diffusion model based on parameter sharing and inverted bottleneck residual structure (DDPM) is proposed. Firstly, the diffusion process gradually covers the original data with Gaussian noise, to learn the corresponding fault characteristics of the original data. In the diffusion process, the parameter sharing attention mechanism is embedded in the learning process of the diffusion process. Then, the feature extraction module is constructed by using the inverted bottleneck residual structure to enhance the learning ability of the network. After obtaining the fault characteristics of the original data, the reverse process of the results restores the Gaussian noise to data with different fault characteristics through the same steps as the diffusion process. By comparing the results of various generation models and analysing the characteristics of the generated data, the feasibility and universality of the proposed method in data augmentation tasks are verified.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687832","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 : 2024-07-02DOI: 10.1088/1361-6501/ad5de4
Yin Tang, Zhongliang Lv, Xiangyu Jia, Li Peng, Lingfeng Li, Jie Zhou, Jiasen Luo, Youwei Xu
Aiming at the problem that the non-stationary and nonlinear weak fault signal of RV (rotate vector) reducers is hard to extract fault features due to the influence of noise and transmission paths, as well as the selection of parameters for maximum correlation kurtosis deconvolution (MCKD) relies heavily on manual experience, this article proposes a fault feature extraction method based on parameter adaptive MCKD for the gear faults of RV reducers. Firstly, the sparrow search algorithm combining sine-cosine and Cauchy mutation（SCSSA）is used to adaptively search for the input parameters of MCKD and obtain the signal after deconvolution with the optimal parameters. Secondly, the deconvoluted signal is subjected to ensemble empirical mode decomposition (EEMD) to obtain modal components on different frequency bands. Finally, calculate the multi-scale fuzzy entropy (MFE) of each component, constructing a MFE feature set vector, and input the feature vector into the support vector machine (SVM) for fault classification and recognition. The experimental analysis and verification results both indicate that the proposed method can adaptively enhance the weak impact components in the gear signals of the RV reducer, effectively extracting weak fault features disturbed by noise. Compared with minimum entropy deconvolution (MED), multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and MCKD, the proposed method has improved identification rate by 17.50%, 10.63% and 15.63%, respectively. In addition, in comparison to multiverse optimization (MVO) and particle swarm optimization (PSO) algorithms, the SCSSA exhibits superior performance when optimizing MCKD parameters, offering faster convergence speed, higher accuracy, and greater robustness.
{"title":"A novel adaptive blind deconvolution algorithm: application to feature extraction of weak faults in RV reducer gears","authors":"Yin Tang, Zhongliang Lv, Xiangyu Jia, Li Peng, Lingfeng Li, Jie Zhou, Jiasen Luo, Youwei Xu","doi":"10.1088/1361-6501/ad5de4","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5de4","url":null,"abstract":"\u0000 Aiming at the problem that the non-stationary and nonlinear weak fault signal of RV (rotate vector) reducers is hard to extract fault features due to the influence of noise and transmission paths, as well as the selection of parameters for maximum correlation kurtosis deconvolution (MCKD) relies heavily on manual experience, this article proposes a fault feature extraction method based on parameter adaptive MCKD for the gear faults of RV reducers. Firstly, the sparrow search algorithm combining sine-cosine and Cauchy mutation（SCSSA）is used to adaptively search for the input parameters of MCKD and obtain the signal after deconvolution with the optimal parameters. Secondly, the deconvoluted signal is subjected to ensemble empirical mode decomposition (EEMD) to obtain modal components on different frequency bands. Finally, calculate the multi-scale fuzzy entropy (MFE) of each component, constructing a MFE feature set vector, and input the feature vector into the support vector machine (SVM) for fault classification and recognition. The experimental analysis and verification results both indicate that the proposed method can adaptively enhance the weak impact components in the gear signals of the RV reducer, effectively extracting weak fault features disturbed by noise. Compared with minimum entropy deconvolution (MED), multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and MCKD, the proposed method has improved identification rate by 17.50%, 10.63% and 15.63%, respectively. In addition, in comparison to multiverse optimization (MVO) and particle swarm optimization (PSO) algorithms, the SCSSA exhibits superior performance when optimizing MCKD parameters, offering faster convergence speed, higher accuracy, and greater robustness.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685347","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}
Soil moisture (SM) retrieval is of great significance in climate, agriculture, ecology, hydrology, and natural disaster monitoring, and it is one of the essential hydrometeorological parameters studied in the world at present. With the continuous development of the GNSS, a technique called GNSS-IR became widely used in ground SM inversion. Therefore, based on the frequency, amplitude and phase of signal-to-noise ratio residuals (δSNR), this study takes P037 and P043 stations set by UNAVCO in the United States as examples and develops the research of SM inversion from Random Forest Regression (RFR) prediction. The experimental results show that the retrieval accuracy of SM under different practical schemes can be in descending order: L1 + L2 dual frequency combination > L2 single frequency > L1 single frequency. It is confirmed that the experimental scheme based on the L1+L2 dual-frequency combination is beneficial to the inversion of SM. In the L1+L2 dual-frequency combination, the prediction set accuracy of the P037 station is as follows: R is 0.796, RMSE is 0.032 cm3cm-3, ME is 0.002 cm3cm-3. The prediction accuracy of the P043 station is as follows: R is 0.858, RMSE is 0.039 cm3cm-3, ME is -0.009 cm3cm-3. Among them, the RMSE of the L1+L2 dual-frequency combination of the two stations has an improvement effect of 13%-37% compared with their single-frequency, which has a noticeable improvement effect. The difference between the SM retrieved by GNSS-IR and the reference value of PBO-H2O is concentrated around 0, further showing the accuracy of SM retrieved by GNSS-IR technology. To sum up, this study considers that SM retrieval based on the RFR model has good reliability and accuracy, which makes GNSS-IR technology an efficient means for SM retrieval. With the continuous improvement of the GNSS system and technology, the application of GNSS-IR technology in SM will become broader.
{"title":"Research on GNSS-IR Soil Moisture Retrieval Based on Random Forest Algorithm","authors":"Naiquan Zheng, Hongzhou Chai, Zhihao Wang, Dongdong Pu, Qiankun Zhang","doi":"10.1088/1361-6501/ad5de3","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5de3","url":null,"abstract":"\u0000 Soil moisture (SM) retrieval is of great significance in climate, agriculture, ecology, hydrology, and natural disaster monitoring, and it is one of the essential hydrometeorological parameters studied in the world at present. With the continuous development of the GNSS, a technique called GNSS-IR became widely used in ground SM inversion. Therefore, based on the frequency, amplitude and phase of signal-to-noise ratio residuals (δSNR), this study takes P037 and P043 stations set by UNAVCO in the United States as examples and develops the research of SM inversion from Random Forest Regression (RFR) prediction. The experimental results show that the retrieval accuracy of SM under different practical schemes can be in descending order: L1 + L2 dual frequency combination > L2 single frequency > L1 single frequency. It is confirmed that the experimental scheme based on the L1+L2 dual-frequency combination is beneficial to the inversion of SM. In the L1+L2 dual-frequency combination, the prediction set accuracy of the P037 station is as follows: R is 0.796, RMSE is 0.032 cm3cm-3, ME is 0.002 cm3cm-3. The prediction accuracy of the P043 station is as follows: R is 0.858, RMSE is 0.039 cm3cm-3, ME is -0.009 cm3cm-3. Among them, the RMSE of the L1+L2 dual-frequency combination of the two stations has an improvement effect of 13%-37% compared with their single-frequency, which has a noticeable improvement effect. The difference between the SM retrieved by GNSS-IR and the reference value of PBO-H2O is concentrated around 0, further showing the accuracy of SM retrieved by GNSS-IR technology. To sum up, this study considers that SM retrieval based on the RFR model has good reliability and accuracy, which makes GNSS-IR technology an efficient means for SM retrieval. With the continuous improvement of the GNSS system and technology, the application of GNSS-IR technology in SM will become broader.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685477","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 : 2024-07-02DOI: 10.1088/1361-6501/ad5de7
Yongchang Xiao, Lingli Cui, Dongdong Liu
Graph neural network (GNN) has the proven ability to learn feature representations from graph data, and has been utilized for the tasks of predicting the machinery remaining useful life (RUL). However, existing methods only focus on a single graph structure and cannot integrate the correlation information contained in multi-graph structures. To address these issues, a multi-graph structure GNN prediction method with attention fusion (MGAFGNN) is proposed in this paper for GNN-based bearing RUL prediction. Specifically, a multi-channel graph attention module (MCGAM) is designed to effectively learn the similar features of node neighbors from different graph data and capture the multi-scale latent features of nodes through the nonlinear transformation. Furthermore, a multi-graph attention fusion module (MGAFM) is proposed to extract the collaborative features from the interaction graph, thereby fusing the feature embeddings from different graph structures. The fused feature representation is sent to the long short-term memory (LSTM) network to further learn the temporal features and achieve RUL prediction. The experimental results on two bearing datasets demonstrate that MGAFGNN outperforms existing methods in terms of prediction performance by effectively incorporating multi-graph structural information.
{"title":"Multi-graph attention fusion graph neural network for remaining useful life prediction of rolling bearings","authors":"Yongchang Xiao, Lingli Cui, Dongdong Liu","doi":"10.1088/1361-6501/ad5de7","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5de7","url":null,"abstract":"\u0000 Graph neural network (GNN) has the proven ability to learn feature representations from graph data, and has been utilized for the tasks of predicting the machinery remaining useful life (RUL). However, existing methods only focus on a single graph structure and cannot integrate the correlation information contained in multi-graph structures. To address these issues, a multi-graph structure GNN prediction method with attention fusion (MGAFGNN) is proposed in this paper for GNN-based bearing RUL prediction. Specifically, a multi-channel graph attention module (MCGAM) is designed to effectively learn the similar features of node neighbors from different graph data and capture the multi-scale latent features of nodes through the nonlinear transformation. Furthermore, a multi-graph attention fusion module (MGAFM) is proposed to extract the collaborative features from the interaction graph, thereby fusing the feature embeddings from different graph structures. The fused feature representation is sent to the long short-term memory (LSTM) network to further learn the temporal features and achieve RUL prediction. The experimental results on two bearing datasets demonstrate that MGAFGNN outperforms existing methods in terms of prediction performance by effectively incorporating multi-graph structural information.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684307","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 : 2024-07-02DOI: 10.1088/1361-6501/ad5dd6
Jianlong Li, Xiao-qin Liu, Xing Wu, Dongxiao Wang, Kai Xu, sheng lin
Motor Current Signal Analysis (MCSA) provides a non-intrusive approach to fault diagnosis. However, the fault impact reacting in the current is reduced due to the presence of flexible structures in the transmission path from the fault source to the motor. Therefore, this paper proposes a method to enhance the frequency domain of the current signal of a single mechanical fault through a transfer model between motor torque and link vibration. First, the joint system dynamics model was developed based on a three-inertia simplified model. The transfer model of motor torque and link vibration was defined based on the system dynamics. The link vibration is then estimated based on the transfer model and electromagnetic torque. Link vibration signal is considered as an enhancement of the torque signal. Finally, the link vibration signature analysis is performed instead of MCSA. The experimental results show that the method is effective in enhancing the features of individual mechanical faults and improving the fault diagnosis performance.
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