Measurement Science and Technology最新文献

{"title":"Diffusion Model and Vision Transformer for Intelligent Fault Diagnosis under Small Samples","authors":"Jian Cen, Weiwei Si, Xi Liu, Bichuang Zhao, Chenhua Xu, Shan Liu, Yanli Xin","doi":"10.1088/1361-6501/ad179c","DOIUrl":"https://doi.org/10.1088/1361-6501/ad179c","url":null,"abstract":"\u0000 The existing deep learning models can achieve a high level of fault diagnosis accuracy in the case of a large number of samples. However, in actual production, data is often limited due to the difficulty of data collection and labeling. For small sample fault diagnosis, a fault diagnosis method called Diffusion Model-Overlapping-Patch Vision Transformer (DM-OVT) is proposed in this paper. The method adds Coordinate Attention (CA) to the diffusion model, so that it can consider both channel information and spatial information. In the patch embedding part of Vision Transformer (ViT), features are first extracted using convolutional layers, and then overlapping patch divisions are used to improve the correlation between each patch. To be specific, DM-OVT first uses short-time Fourier transform (STFT) to convert the one-dimensional signals into the time-frequency maps. And then inputs them into the diffusion model (DM) to generate different classes of fault data according to labels. Finally, Overlapping-Patch Vision Transformer (OVT) is used to classify the expanded data. The effectiveness of the proposed method was tested on data sets from laboratory multistage centrifugal fans and Case Western Reserve University, and the highest accuracy was achieved in the comparison experiments.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"115 16","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138953834","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}

{"title":"Development of a vehicle–mounted soil organic matter detection system based on near–infrared spectroscopy and image information fusion","authors":"Yongyan Cao, Wei Yang, Hao Li, Hao Zhang, Minzan Li","doi":"10.1088/1361-6501/ad179f","DOIUrl":"https://doi.org/10.1088/1361-6501/ad179f","url":null,"abstract":"\u0000 In the practical application of farmland, the soil organic matter prediction model es-tablished by the traditional near-infrared spectroscopy is affected by factors such as soil texture, which leads to a serious decline in the accuracy of the model. To im-prove the robustness and prediction accuracy of the model, a prediction model based on near-infrared spectroscopy and image fusion is proposed. A 1D CNN organic matter prediction model (based on near-infrared spectroscopy) was established using eight characteristic wavelengths of extracted soil organic matter (932 nm, 999 nm, 1083 nm, 1191 nm, 1316 nm, 1356 nm, 1583 nm, and 1626 nm) as spectral infor-mation. A 2D CNN organic matter prediction model was established using soil RGB images as information. Based on the idea of model weight fusion, 1D CNN and 2D CNN models are fused. When using small convolutional kernels(3-layer convolu-tional kernel size: 3 * 3, 1 * 1, 1 * 1)and 1D-CNN: 2D-CNN = 6:4, the model has the highest prediction accuracy(R2=0.872). The optimal fusion model was embedded into the inspection system. The final laboratory and field testing results are as fol-lows: under laboratory conditions, the detection accuracy R2 of the 1D CNN predic-tion model, 2D CNN prediction model, and fusion model are 0.838, 0.781, and 0.869, respectively. The RMSE is 3.005, 3.546, and 2.678, respectively. The above experi-mental data indicates that the R2 of the fused model is more accurate compared to the model established with a single information. In the field test, the R2 detection accuracy of 1D CNN prediction model, 2D CNN prediction model and fusion model is 0.809, 0.731 and 0.835, respectively. The root mean square errors are 3.466, 3.828 and 2.973, respectively. The results show that this testing system can meet the needs of soil nutrient testing in farmland and provide guidance for precision agriculture management.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"68 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138954740","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}

{"title":"Advanced robust control design and experimental verification for trajectory tracking of model-based uncertain collaborative robots","authors":"Shengchao Zhen, Runtong Li, Xiaoli Liu, Ye-hwa Chen","doi":"10.1088/1361-6501/ad179d","DOIUrl":"https://doi.org/10.1088/1361-6501/ad179d","url":null,"abstract":"\u0000 At the core of this research is the pursuit of enhancing the trajectory tracking performance of six-degree-of-freedom (6-DOF) collaborative robots, with a particular focus on addressing the challenges posed by uncertainties in real-world applications. One of the primary issues encountered with existing methods is the susceptibility of trajectory tracking to uncertainties, which can significantly hinder the performance of robotic systems. To address these challenges, we propose an advanced control method, known as the Model-based proportional-derivative controller, or MPDP controller for short, which represents an innovative fusion of model-based PD control principles with a robust control algorithm. This amalgamation is driven by the need to mitigate the impact of uncertainties and external disturbances on trajectory tracking. A comprehensive assessment grounded in Lyapunov theory has been undertaken to validate the effectiveness of our approach. The analysis has firmly established that our method ensures not only the ultimate boundedness but also the uniform boundedness of the robotic system, which is critical for its operational stability. Both experimental and simulation studies have been meticulously conducted to benchmark the performance of the MPDP controller against the conventional proportional-integral-derivative (PID) controller, which serves as a widely adopted baseline in the field. The results unequivocally demonstrate the superiority of the MPDP controller across multiple dimensions. It exhibits exceptional robustness, resulting in a smaller steady-state tracking error, a critical advantage when addressing inherent uncertainties and external disturbances that can perturb the robot system. This translates to a more stable trajectory tracking performance. Furthermore, the MPDP controller empowers the robot with the capability to precisely follow predefined trajectories, thus ensuring high-precision and reliable execution of tasks. This feature significantly contributes to an overall enhancement of system performance and productivity.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"26 20","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138955361","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}

{"title":"A rapid ray tracing method to evaluate the performances of ERA5 and MERRA2 in retrieving global tropospheric delay","authors":"Mingyuan Zhang, Peng Yuan, Weiping Jiang, Yong Zou, Wenlan Fan, Jian Wang","doi":"10.1088/1361-6501/ad1707","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1707","url":null,"abstract":"\u0000 Atmospheric reanalysis plays an important role in retrieving the atmospheric tropospheric delays with ray tracing for space geodetic techniques. In order to represent the complex weather and climate conditions better, the spatiotemporal resolutions of the newly developed atmospheric reanalysis products are improved significantly. The increased spatiotemporal resolution provides a great opportunity to improve the accuracy of the tropospheric delays derived from ray tracing, but it remains a challenge due to the highly increased computation costs. In this paper, we develop a rapid ray tracing method with refined height interval determination to accommodate the increased spatiotemporal resolution of the atmospheric reanalysis products. The accuracy of this method was validated by the 2010 International Association of Geodesy (IAG) Working Group 4.3.3 ray tracing Comparison Campaign reference results. Zenith and slant delays were obtained by tracing 342 global International GNSS Service (IGS) stations. Compared to the traditional method, this reduced memory footprint by 16.1%, global refractivity field construction time by 13.6%, and per ray trace time by 22.5% while maintaining accuracy. Based on this methodology, ray tracing using state-of-the-art fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) and second Modern-Era Retrospective Analysis for Research and Applications (MERRA2) at 342 IGS stations assessed tropospheric delay performance in 2021. Results showed significant ERA5 and MERRA2 slant delay and mapping factor differences up to the decimeter level, especially for the wet component. Additionally, using IGS Zenith Total Delay (ZTD) as a reference, ERA5 ZTD bias and Root Mean Square Error (RMSE) were 2.3 and 11.9 mm, versus that of 1.8 and 16.2 mm for MERRA2 ZTD. At extreme weather-affected AIRA stations over August 5-9, 2021, ERA5 ZTD mean and RMSE differences were -3.0 and 19.8 mm, and -5.3 and 21.7 mm for MERRA2 ZTD. Tropospheric delays and models derived from ERA5 can support space geodetic applications given improved performance and temporal resolution.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"5 20","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138959887","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}

{"title":"Dual-frequency transmitter configuration for shallow surface electromagnetic detection","authors":"Weiyu Liu, Shengbao Yu, Xinhao Zhang","doi":"10.1088/1361-6501/ad1742","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1742","url":null,"abstract":"\u0000 In shallow surface electromagnetic detection, the square wave scheme is generally used in conventional transmission systems. Based on frequency-domain electromagnetic (FDEM) sounding theory, high-frequency measurement helps to improve vertical resolution. However, long grounded cable inductance produces severe reactive power suppression at high frequency transmission frequencies, which will reduce detection. To further improve detection accuracy and efficiency, a dual-frequency transmitter configuration is proposed in this article for shallow surface detection. The transmitter simultaneously powers two LC series resonant circuits for the detection of shallow and deep area. Dual-frequency control strategy is adopted, with both bridge arms being provided with constant switching frequency operation. According to the equivalent model of the transmission system, the control of the load branches is independent of each other. The LC series resonant circuit guarantees a wide passband to match long cable inductance that cannot be accurately estimated in advance. Simulations and experimental tests were carried out using this transmitter configuration and control technique. The simulation and experimental results are in general agreement, verifying the feasibility and effectiveness of the proposed dual-band transmitter configuration.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":" 17","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138961697","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}

{"title":"Multi-condition tool wear prediction for milling CFRP base on a novel hybrid monitoring method","authors":"Shipeng Li, Siming Huang, Hao Li, Wentao Liu, Weizhou Wu, Jian Liu","doi":"10.1088/1361-6501/ad1478","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1478","url":null,"abstract":"In the carbon fiber-reinforced plastic milling process, the high abrasive property of carbon fiber will lead to the rapid growth of tool wear, resulting in poor surface quality of parts. However, due to the signal data distribution discrepancy under different working conditions, addressing the problem of local degradation and low prediction accuracy in tool wear monitoring model is a significant challenge. This paper proposes an entropy criterion deep conditional domain adaptation network, which effectively exploits domain invariant features of the signals and enhances the stability of model training. Furthermore, a novel unsupervised optimization method based on tool wear distribution is proposed, which refines the monitoring results of data-driven models. This approach reduces misclassification of tool wear conditions resulting from defects in data-driven models and interference from the manufacturing process, thereby enhancing the accuracy of the monitoring model. The experimental results show that the hybrid method provides assurance for the accurate construction of tool wear monitoring model under different working conditions.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"2 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139173439","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}

{"title":"Real-time detection and localization method for weld seam of narrow butt joint based on semantic segmentation","authors":"Xinyu Chen, Qihao Ma, Zhuzhen He, Xiaoyu Sun, Yan Ren","doi":"10.1088/1361-6501/ad16b9","DOIUrl":"https://doi.org/10.1088/1361-6501/ad16b9","url":null,"abstract":"Structured light measurement is widely used in welding seam tracking because of its high precision and robustness. For the narrow butt joint, the positioning method by reconstructing the weld contour is not suitable for the welding of the narrow butt joint because it is difficult for the laser stripe to produce obvious deformation when projected to the weld. In this study, high-quality images with laser stripes and narrow butt joints are captured by the improved structured light vision sensor, which is equipped with an auxiliary light source. A two-step processing framework, including semantic segmentation and groove positioning, is raised to locate the feature point of the narrow butt joint. Firstly, we design the strip pooling ENet (SP-ENet), a real-time network specifically designed to accurately segment narrow weld images. Our proposed network outperforms other classical segmentation networks in terms of segmentation accuracy and proves to be highly suitable for the detection of narrow butt joint welds. Secondly, a combining method of random sample consensus (RANSAC) and iterative fitting to calculate the sub-pixel coordinates of weld feature points accurately. Finally, a trajectory smoothing model based on the Kalman filter is proposed to reduce the trajectory jitter. The above methods were tested on a self-built robotic welding experimental platform. Experimental results show that the proposed method can be used for real-time detection and positioning of narrow butt joints. The positioning trajectory is smooth, with most positioning errors less than 2 pixels. The mean tracking error reaches 0.207 mm, which can meet the practical welding requirements.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"48 9","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139174657","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}

{"title":"Fast nonlinear cross-sparse filtering for rolling bearings compound fault diagnosis","authors":"Shunxiang Yao, Zongzhen Zhang, Baokun Han, Jinrui Wang, Jiansong Zheng","doi":"10.1088/1361-6501/ad166f","DOIUrl":"https://doi.org/10.1088/1361-6501/ad166f","url":null,"abstract":"The investigation of faults in rotating machinery has been thoroughly examined. Among the different methods under exploration, sparse optimization-based techniques have arisen as a highly desirable approach. However, in real industrial environments, the collected bearing signals often contain a random impact component resulting from changes in working conditions and load mutations. When a machine malfunctions, it can readily induce and generate new faults, resulting in composite faults. To address this challenge, this paper proposes a novel multidimensional blind deconvolution method named fast nonlinear cross-sparse filtering (FNCr-SF). The FNCr-SF aims to separate weak compound faults under random impact interference. Various preprocessing techniques, including Z-score normalization and nonlinear sigmoid activation function, are employed to amplify the faint characteristics of compound faults and minimize the influence of random interference. Furthermore, the FNCr-SF method enables adaptive decomposition of fault components without the need for prior knowledge or pre-processing. This approach effectively reduces random interference and accurately detects compound faults in bearings. Experimental and simulation signals validate the effectiveness of the FNCr-SF method in compound fault detection, demonstrating its high accuracy and robustness.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"349 17‐18","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138966686","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}

{"title":"Monitoring robot machine tool sate via neural ODE and BP-GA","authors":"Guangyi Zhu, Xi Zeng, Zheng Gong, Zhuohan Gao, Renquan Ji, Yisen Zeng, Pei Wang, Congda Lu","doi":"10.1088/1361-6501/ad166d","DOIUrl":"https://doi.org/10.1088/1361-6501/ad166d","url":null,"abstract":"Tool wear during robotic polishing affects material removal rates and surface roughness, leading to erratic and inconsistent polishing quality. Therefore, a method that can predict the tool state is needed to replace the robot end tool in time. In this paper, based on the cutting-edge neural ordinary differential equations (Neural ODE) and BP neural network optimization based on genetic algorithm (BP-GA), we propose a method to identify the tool state during robotic machining: firstly, a new training method of Neural ODE is proposed to avoid the model from falling into poor stationary points, and then on this basis, Neural ODE is utilized to predict the changes of vibration signals during robot machining; secondly, the predicted vibration signals of the tool are processed using variable modal decomposition method to extract the eigen kurtosis index and envelope entropy of the modal function as the vibration signal eigenvectors, and compare them with the traditional vibration signal eigenvectors. Finally, the predicted tool states were identified using BP-GA, and numerical experiments yielded an F1 score of 91.76% and an accuracy of 96.55% for model identification.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"4 12","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138966440","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}

{"title":"Laser Self-mixing Interference Displacement Signal Filtering Method based on Empirical Mode Decomposition and Wavelet Threshold","authors":"Changying Guo, Qi Wang","doi":"10.1088/1361-6501/ad166c","DOIUrl":"https://doi.org/10.1088/1361-6501/ad166c","url":null,"abstract":"\u0000 Objective: In laser self-mixing interferometry displacement measurement, noise interference has a significant impact on the measurement results. To improve measurement accuracy, this paper proposes a filtering method that combines empirical mode decomposition (EMD) with wavelet thresholding. Method: First, the signal is decomposed into several intrinsic mode functions (IMFs) using EMD. Then, wavelet thresholding is applied to each IMF. Subsequently, the processed IMFs are reconstructed to achieve signal filtering. Finally, by integrating the principles of interpolation and fringe counting, the reconstructed displacement signal is recovered, realizing accurate displacement measurement. Result: This paper presents comprehensive simulation analyses and experimental validations for the proposed method. The accuracy of the displacement recovery is quantitatively evaluated using the absolute error and standard error, comparing the recovered displacement signal with the actual displacement. Conclusion: The experimental results demonstrate that the laser self-mixing interferometry displacement signal filtering method based on EMD and wavelet thresholding has high accuracy.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"13 9","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138966353","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}