The broadband terahertz wave, with its correspondence to the fingerprint spectra of gas molecules and relatively high transmittance through smoke, aerosol, and combustion environments, bears great potential for gas detection and combustion diagnostics. While access to the rotational spectral fingerprints in the terahertz region provides opportunities for species-selective diagnostics with minimized background and cross interference, few studies have been devoted to direct, quantitative, simultaneous analysis of multiple components exploiting the terahertz region. In this work, we achieve quantitative measurements of CO, NH3 and H2O gas concentrations at standard temperature and pressure over a bandwidth of 1 THz, using direct absorption spectrum from femtosecond-laser-based terahertz time-domain spectroscopy. Spectral fitting of the fully resolved rotational lines yields good precision and accuracy with validation against calibrated mixtures. The estimated detection limts of the multi-speices sensing system are 250 ppm·m, 7 ppm·m and 4 ppm·m for CO, NH3 and H2O, respectively. The demonstration of quantitative, multi-species gas sensing indicates the feasibility and practical value of using broadband terahertz absorption spectroscopy for real-time, quantitative analysis and speciation of multicomponent gas in complicated practical environments such as combustion and multi-phase flows.
{"title":"Quantitative, multi-species gas sensing using broadband terahertz time-domain spectroscopy","authors":"Chuxuan Zhao, Weitian Wang, Zhu Ning, Zihao Song, Xing Chao","doi":"10.1088/1361-6501/ad1cc8","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1cc8","url":null,"abstract":"\u0000 The broadband terahertz wave, with its correspondence to the fingerprint spectra of gas molecules and relatively high transmittance through smoke, aerosol, and combustion environments, bears great potential for gas detection and combustion diagnostics. While access to the rotational spectral fingerprints in the terahertz region provides opportunities for species-selective diagnostics with minimized background and cross interference, few studies have been devoted to direct, quantitative, simultaneous analysis of multiple components exploiting the terahertz region. In this work, we achieve quantitative measurements of CO, NH3 and H2O gas concentrations at standard temperature and pressure over a bandwidth of 1 THz, using direct absorption spectrum from femtosecond-laser-based terahertz time-domain spectroscopy. Spectral fitting of the fully resolved rotational lines yields good precision and accuracy with validation against calibrated mixtures. The estimated detection limts of the multi-speices sensing system are 250 ppm·m, 7 ppm·m and 4 ppm·m for CO, NH3 and H2O, respectively. The demonstration of quantitative, multi-species gas sensing indicates the feasibility and practical value of using broadband terahertz absorption spectroscopy for real-time, quantitative analysis and speciation of multicomponent gas in complicated practical environments such as combustion and multi-phase flows.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"101 8","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139444804","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 : 2024-01-09DOI: 10.1088/1361-6501/ad1cc9
Xudong Chen, Liuyang Li, Yajian Liu, YIngda Zhao, Xiangnan Qin, Jinjun Guo, Bo Xu, Guang Yang
The health diagnosis procedure applied to determine the deformation of a roller compacted concrete (RCC) dam is different from that of a conventional concrete dam. Hence, in this study, a deformation health diagnosis model was established for an RCC dam considering the construction interfaces by combining the hydrostatic component simulated using ABAQUS (2016), with the temperature and aging components calculated using a statistical method. The combined method can help monitor the structural health of RCC dams and determine the physical meaning and statistical law of deformation of RCC dams. During the simulation process, the geometric characteristics of the construction interfaces were modelled using the interface equivalent expansion method (IEEM). The material properties were modelled using multi-output least-squares support vector regression optimized with the Jaya algorithm (JA-MLS-SVR). A case study demonstrated that the established deformation–health diagnosis model has good fitting and prediction abilities. The model and methods proposed in this study provide a new concept for the behavior analysis and numerical simulation of mass composite structures similar to RCC dams.
{"title":"Deformation Health Diagnosis of RCC Dams Considering Construction Interfaces Based on Monitoring Data and Numerical Simulation","authors":"Xudong Chen, Liuyang Li, Yajian Liu, YIngda Zhao, Xiangnan Qin, Jinjun Guo, Bo Xu, Guang Yang","doi":"10.1088/1361-6501/ad1cc9","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1cc9","url":null,"abstract":"\u0000 The health diagnosis procedure applied to determine the deformation of a roller compacted concrete (RCC) dam is different from that of a conventional concrete dam. Hence, in this study, a deformation health diagnosis model was established for an RCC dam considering the construction interfaces by combining the hydrostatic component simulated using ABAQUS (2016), with the temperature and aging components calculated using a statistical method. The combined method can help monitor the structural health of RCC dams and determine the physical meaning and statistical law of deformation of RCC dams. During the simulation process, the geometric characteristics of the construction interfaces were modelled using the interface equivalent expansion method (IEEM). The material properties were modelled using multi-output least-squares support vector regression optimized with the Jaya algorithm (JA-MLS-SVR). A case study demonstrated that the established deformation–health diagnosis model has good fitting and prediction abilities. The model and methods proposed in this study provide a new concept for the behavior analysis and numerical simulation of mass composite structures similar to RCC dams.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"37 20","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139442630","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 : 2024-01-08DOI: 10.1088/1361-6501/ad1919
Bo Shi, Tianyu Cao, Qiqi Ge, Yuan Lin, Zitao Wang
Subsea pipelines rely primarily on imaging sonar for detection and identification. We analyze the imaging principles of side scan sonar, multi-beam sonar, synthetic aperture sonar, seafloor penetrating sonar and forward-looking sonar. We discuss their effectiveness in detecting seabed pipelines, as well as their limitations in image recognition capabilities. As intelligent algorithms have become increasingly important in the field of image processing, we review the sonar image intelligent detection and recognition algorithms in the past six years and summarize the internal principles and application effects of classic algorithms such as Scale-Invariant Feature Transform, K-means algorithm, and constant false-alarm rate that currently show good application prospects. Simultaneously, we review the particular strengths exhibited by these algorithms, such as contour feature extraction, image segmentation and clustering, target recognition under background noise, etc. The research on intelligent processing of sonar images opens up a new way to solve the difficult problem of the seabed targets detection and recognition.
{"title":"Sonar image intelligent processing in seabed pipeline detection: review and application","authors":"Bo Shi, Tianyu Cao, Qiqi Ge, Yuan Lin, Zitao Wang","doi":"10.1088/1361-6501/ad1919","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1919","url":null,"abstract":"Subsea pipelines rely primarily on imaging sonar for detection and identification. We analyze the imaging principles of side scan sonar, multi-beam sonar, synthetic aperture sonar, seafloor penetrating sonar and forward-looking sonar. We discuss their effectiveness in detecting seabed pipelines, as well as their limitations in image recognition capabilities. As intelligent algorithms have become increasingly important in the field of image processing, we review the sonar image intelligent detection and recognition algorithms in the past six years and summarize the internal principles and application effects of classic algorithms such as Scale-Invariant Feature Transform, K-means algorithm, and constant false-alarm rate that currently show good application prospects. Simultaneously, we review the particular strengths exhibited by these algorithms, such as contour feature extraction, image segmentation and clustering, target recognition under background noise, etc. The research on intelligent processing of sonar images opens up a new way to solve the difficult problem of the seabed targets detection and recognition.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"59 15","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139447705","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 : 2024-01-08DOI: 10.1088/1361-6501/ad1c4a
Dirk Piester, E. Staliuniene, Andreas Bauch
Received signals from Global Navigation Satellite Systems (GNSS) are nowadays widely used by industry laboratories for ensuring metrological traceability for their respective range of calibration services in the field of time and frequency. Usually, a local frequency standard is steered by continuous GNSS signal reception providing at its output stable and accurate reference signals for the laboratory measurement equipment, in general for synthesizers and counters. Reception of GNSS signals is surely an adequate and practical tool for the purpose, however further steps are needed to establish traceability in a strict metrological sense. Based on already available guidelines and publications, this paper is a contribution to the discussion how metrological traceability to internationally accepted standards can be established in a calibration laboratory. We restrict the discussion to equipment in common use which may not necessarily be of the highest sophistication. In this spirit, we develop a detailed scheme for an uncertainty budget comprising all links of the traceability chain from the device under test to the SI second, the scale-unit of Coordinated Universal Time. Then we go through and apply this scheme step by step to a demonstration setup for frequency measurements with a counter with varying operational parameters. In this framework, a novel approach to distinguish between components of statistical measurement uncertainty is introduced. Furthermore, the limiting uncertainty contributions are discussed and based on a suitable set of parameters an expression for the best measurement capability is given. With this scheme at hand a user may develop an uncertainty budget adapted to his own setup, especially if acceptance from a national accreditation body is sought.
全球导航卫星系统（GNSS）的接收信号如今被工业实验室广泛用于确保其各自时间和频率领域校准服务范围的计量溯源性。通常，本地频率标准由连续的全球导航卫星系统信号接收引导，其输出为实验室测量设备（一般为合成器和计数器）提供稳定准确的参考信号。接收全球导航卫星系统信号无疑是实现这一目的的适当而实用的工具，但还需要采取进一步措施，以建立严格计量意义上的可追溯性。本文以现有的指南和出版物为基础，讨论如何在校准实验室建立国际公认标准的计量溯源性。我们的讨论仅限于常见的设备，这些设备不一定是最先进的。本着这一精神，我们制定了不确定度预算的详细方案，包括从被测设备到协调世界时的标度单位 SI 秒的溯源链的所有环节。然后，我们逐步将这一方案应用于使用具有不同运行参数的计数器进行频率测量的演示装置。在此框架下，我们引入了一种新方法来区分统计测量不确定性的组成部分。此外，还讨论了极限不确定性的贡献，并根据一组合适的参数给出了最佳测量能力的表达式。有了这个方案，用户就可以根据自己的设置制定不确定性预算，尤其是在寻求国家认证机构认可的情况下。
{"title":"Traceable Frequency Measurements with Counters","authors":"Dirk Piester, E. Staliuniene, Andreas Bauch","doi":"10.1088/1361-6501/ad1c4a","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1c4a","url":null,"abstract":"\u0000 Received signals from Global Navigation Satellite Systems (GNSS) are nowadays widely used by industry laboratories for ensuring metrological traceability for their respective range of calibration services in the field of time and frequency. Usually, a local frequency standard is steered by continuous GNSS signal reception providing at its output stable and accurate reference signals for the laboratory measurement equipment, in general for synthesizers and counters. Reception of GNSS signals is surely an adequate and practical tool for the purpose, however further steps are needed to establish traceability in a strict metrological sense. Based on already available guidelines and publications, this paper is a contribution to the discussion how metrological traceability to internationally accepted standards can be established in a calibration laboratory. We restrict the discussion to equipment in common use which may not necessarily be of the highest sophistication. In this spirit, we develop a detailed scheme for an uncertainty budget comprising all links of the traceability chain from the device under test to the SI second, the scale-unit of Coordinated Universal Time. Then we go through and apply this scheme step by step to a demonstration setup for frequency measurements with a counter with varying operational parameters. In this framework, a novel approach to distinguish between components of statistical measurement uncertainty is introduced. Furthermore, the limiting uncertainty contributions are discussed and based on a suitable set of parameters an expression for the best measurement capability is given. With this scheme at hand a user may develop an uncertainty budget adapted to his own setup, especially if acceptance from a national accreditation body is sought.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"33 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445247","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 : 2024-01-08DOI: 10.1088/1361-6501/ad1c46
Bingkun Wei, Chen Chen, Runcong Liu, Jinling Yang, Xiaodong Wang
Copper and aluminum foils serve as predominant materials in fluid collectors, and defects within them can significantly impact the electrochemical performance of cells. However, existing methods for detecting defects within non-ferromagnetic thin metals, such as copper and aluminum foils, have several limitations. This study aims to address the need for detecting micrometer-scale defects on 0.1 mm copper foils, aligning with industrial field requirements. We devised an inspection device based on the induced magnetic field detection principle and explored the impact of copper foil undulations on micrometer-scale defect detection using COMSOL modeling. Subsequently, we introduced a coherent cumulative-differential algorithm to effectively mitigate the influences of circuit noise and sampling heave noise on defect signals. Consequently, the signal-to-noise ratios of 100- and 200-micron defect signals were significantly improved by 157% and 234%, respectively. This approach shows promise for detecting micrometer-scale defects in non-ferromagnetic thin metals and lays a robust foundation for future defect identification and inversion endeavors.
{"title":"Non-ferromagnetic thin metal micron-sized defect detection system based on coherent accumulation-difference method","authors":"Bingkun Wei, Chen Chen, Runcong Liu, Jinling Yang, Xiaodong Wang","doi":"10.1088/1361-6501/ad1c46","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1c46","url":null,"abstract":"\u0000 Copper and aluminum foils serve as predominant materials in fluid collectors, and defects within them can significantly impact the electrochemical performance of cells. However, existing methods for detecting defects within non-ferromagnetic thin metals, such as copper and aluminum foils, have several limitations. This study aims to address the need for detecting micrometer-scale defects on 0.1 mm copper foils, aligning with industrial field requirements. We devised an inspection device based on the induced magnetic field detection principle and explored the impact of copper foil undulations on micrometer-scale defect detection using COMSOL modeling. Subsequently, we introduced a coherent cumulative-differential algorithm to effectively mitigate the influences of circuit noise and sampling heave noise on defect signals. Consequently, the signal-to-noise ratios of 100- and 200-micron defect signals were significantly improved by 157% and 234%, respectively. This approach shows promise for detecting micrometer-scale defects in non-ferromagnetic thin metals and lays a robust foundation for future defect identification and inversion endeavors.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"43 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139444942","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 : 2024-01-08DOI: 10.1088/1361-6501/ad1c47
Fule Li, Xinlong Zhao
Insufficient and imbalanced samples pose a significant challenge in bearing fault diagnosis, leading to low diagnosis accuracy. However, the fault characteristics of vibration signals are weak and difficult to extract when faults occur in the early stage. This paper proposes an effective fault diagnosis method that addresses small and imbalanced sample problems under noise interference. First, the number of faulty samples in the form of 1D signals is increased mainly by the sliding split sampling method. The preprocessed data are used to create 2D time–frequency diagrams using the continuous wavelet transform (CWT), which can extract effective features to improve the data quality. Subsequently, the minority samples are oversampled by combining Synthetic Minority Oversampling Technique (SMOTE) to realize TFCAO. Moreover, the clustering method and random undersampling method are introduced to prevent the overfitting and underfitting problems respectively. Then, we propose a hybrid attention mechanism to enhance the extraction of effective feature information. This combination, integrating CWT with a multicolumn modified DRN, effectively extracts fault characteristics and suppresses noise effects. The experimental results demonstrate the effectiveness of the proposed method by comparison with other advanced methods using two case studies of bearing datasets.
{"title":"A novel approach for bearings multiclass fault diagnosis fusing multiscale deep convolution and hybrid attention networks","authors":"Fule Li, Xinlong Zhao","doi":"10.1088/1361-6501/ad1c47","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1c47","url":null,"abstract":"\u0000 Insufficient and imbalanced samples pose a significant challenge in bearing fault diagnosis, leading to low diagnosis accuracy. However, the fault characteristics of vibration signals are weak and difficult to extract when faults occur in the early stage. This paper proposes an effective fault diagnosis method that addresses small and imbalanced sample problems under noise interference. First, the number of faulty samples in the form of 1D signals is increased mainly by the sliding split sampling method. The preprocessed data are used to create 2D time–frequency diagrams using the continuous wavelet transform (CWT), which can extract effective features to improve the data quality. Subsequently, the minority samples are oversampled by combining Synthetic Minority Oversampling Technique (SMOTE) to realize TFCAO. Moreover, the clustering method and random undersampling method are introduced to prevent the overfitting and underfitting problems respectively. Then, we propose a hybrid attention mechanism to enhance the extraction of effective feature information. This combination, integrating CWT with a multicolumn modified DRN, effectively extracts fault characteristics and suppresses noise effects. The experimental results demonstrate the effectiveness of the proposed method by comparison with other advanced methods using two case studies of bearing datasets.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"31 12","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445813","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 : 2024-01-08DOI: 10.1088/1361-6501/ad1c4c
Haipeng Fan, Zhongjun Qiu
In modern industry, the surface defect inspection of injection moulded products is crucial for controlling product quality and optimizing the manufacturing process. With the development of optical measurement and computer technology, machine vision inspection methods have been widely adopted instead of manual inspection. However, current machine vision inspection methods are difficult to simultaneously ensure the accuracy and efficiency of surface defect inspection of injection moulded products. Considering this problem, a novel deep learning algorithm applied to machine vision inspection for surface defects of injection moulded products is proposed. To train and evaluate the proposed deep learning algorithm, an image acquisition platform is established and the dataset of surface defects in moulded products is obtained. In the proposed deep learning algorithm, reparameterization-based convolution modules are employed for feature extraction and feature fusion. A median iterative clustering algorithm based on hierarchical clustering initialization is proposed to obtain prior anchors that are highly matched with the actual distribution of defect sizes. A novel Focus-Entire Union over Covering (Focus-EUoC) loss function is utilized for bounding box regression. On these bases, the proposed deep learning algorithm applied to machine vision inspection is evaluated on the dataset of surface defects in moulded products. The experimental results indicate that compared to the traditional inspection algorithms and other deep learning algorithms currently used in machine vision inspection, the proposed deep learning algorithm exhibits superior inspection accuracy and inspection efficiency on the acquired dataset. The inspection precision reaches 0.964, the inspection recall reaches 0.955, and the inference time for each subgraph is only 6.1ms, confirming its effectiveness.
在现代工业中，注塑产品的表面缺陷检测对于控制产品质量和优化生产过程至关重要。随着光学测量和计算机技术的发展，机器视觉检测方法取代人工检测已被广泛采用。然而，目前的机器视觉检测方法很难同时确保注塑产品表面缺陷检测的准确性和效率。考虑到这一问题，本文提出了一种应用于注塑产品表面缺陷机器视觉检测的新型深度学习算法。为了训练和评估所提出的深度学习算法，建立了一个图像采集平台，并获得了注塑产品表面缺陷的数据集。在所提出的深度学习算法中，基于重参数化的卷积模块被用于特征提取和特征融合。提出了一种基于分层聚类初始化的中值迭代聚类算法，以获得与实际缺陷尺寸分布高度匹配的先验锚点。在边界框回归中使用了新颖的 Focus-Entire Union over Covering（Focus-EUoC）损失函数。在此基础上，在模制产品表面缺陷数据集上对应用于机器视觉检测的深度学习算法进行了评估。实验结果表明，与传统检测算法和目前机器视觉检测中使用的其他深度学习算法相比，所提出的深度学习算法在获取的数据集上表现出更高的检测精度和检测效率。检测精度达到 0.964，检测召回率达到 0.955，每个子图的推理时间仅为 6.1ms，证实了其有效性。
{"title":"A novel deep learning algorithm applied to machine vision inspection for surface defects of injection moulded products","authors":"Haipeng Fan, Zhongjun Qiu","doi":"10.1088/1361-6501/ad1c4c","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1c4c","url":null,"abstract":"\u0000 In modern industry, the surface defect inspection of injection moulded products is crucial for controlling product quality and optimizing the manufacturing process. With the development of optical measurement and computer technology, machine vision inspection methods have been widely adopted instead of manual inspection. However, current machine vision inspection methods are difficult to simultaneously ensure the accuracy and efficiency of surface defect inspection of injection moulded products. Considering this problem, a novel deep learning algorithm applied to machine vision inspection for surface defects of injection moulded products is proposed. To train and evaluate the proposed deep learning algorithm, an image acquisition platform is established and the dataset of surface defects in moulded products is obtained. In the proposed deep learning algorithm, reparameterization-based convolution modules are employed for feature extraction and feature fusion. A median iterative clustering algorithm based on hierarchical clustering initialization is proposed to obtain prior anchors that are highly matched with the actual distribution of defect sizes. A novel Focus-Entire Union over Covering (Focus-EUoC) loss function is utilized for bounding box regression. On these bases, the proposed deep learning algorithm applied to machine vision inspection is evaluated on the dataset of surface defects in moulded products. The experimental results indicate that compared to the traditional inspection algorithms and other deep learning algorithms currently used in machine vision inspection, the proposed deep learning algorithm exhibits superior inspection accuracy and inspection efficiency on the acquired dataset. The inspection precision reaches 0.964, the inspection recall reaches 0.955, and the inference time for each subgraph is only 6.1ms, confirming its effectiveness.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"4 6","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139446042","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 : 2024-01-08DOI: 10.1088/1361-6501/ad1c49
Jiliang Yi, Huabing Tan, Jun Yan, Xin Chen
An adaptive fault diagnosis method for rotating machinery based on maximum kurtosis incomplete S-transform is proposed in this paper. Firstly, the incomplete S-transform is performed on the fault frequency band of the vibration signal, and the module vector group is obtained through module calculation. Subsequently, the kurtosis of all the modulus vectors are calculated and the vector corresponding to the maximum kurtosis is located to adaptively determine the envelope of the fault frequency component in the vibration signal. Then, fast Fourier transform is performed on the envelope to obtain its main frequency, which is matched with the fault mode frequency to achieve fault diagnosis of rotating machinery. Finally, the mean peak ratio (MPR) was used to evaluate the performance of different methods under various operating conditions. The results show that the maximum MPR is obtained by the proposed method, demonstrating its stronger noise resistance and demodulation ability.
本文提出了一种基于最大峰度不完全 S 变换的旋转机械自适应故障诊断方法。首先，对振动信号的故障频带进行不完全 S 变换，通过模数计算得到模数矢量组。随后，计算所有模数矢量的峰度，找到峰度最大的对应矢量，从而自适应地确定振动信号中故障频率分量的包络。然后，对包络进行快速傅里叶变换，以获得其主频，并与故障模式频率相匹配，从而实现旋转机械的故障诊断。最后，使用平均峰值比（MPR）来评估不同方法在各种运行条件下的性能。结果表明，所提出的方法获得了最大的 MPR，显示了其更强的抗噪声和解调能力。
{"title":"Adaptive rotating machinery fault diagnosis method using MKIST","authors":"Jiliang Yi, Huabing Tan, Jun Yan, Xin Chen","doi":"10.1088/1361-6501/ad1c49","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1c49","url":null,"abstract":"An adaptive fault diagnosis method for rotating machinery based on maximum kurtosis incomplete S-transform is proposed in this paper. Firstly, the incomplete S-transform is performed on the fault frequency band of the vibration signal, and the module vector group is obtained through module calculation. Subsequently, the kurtosis of all the modulus vectors are calculated and the vector corresponding to the maximum kurtosis is located to adaptively determine the envelope of the fault frequency component in the vibration signal. Then, fast Fourier transform is performed on the envelope to obtain its main frequency, which is matched with the fault mode frequency to achieve fault diagnosis of rotating machinery. Finally, the mean peak ratio (MPR) was used to evaluate the performance of different methods under various operating conditions. The results show that the maximum MPR is obtained by the proposed method, demonstrating its stronger noise resistance and demodulation ability.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"49 20","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139447233","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 : 2024-01-08DOI: 10.1088/1361-6501/ad1c4b
Wenkai Pang, Zhi Tan
Steel is an indispensable raw material in the construction industry. To avert catastrophic events such as building collapse, it is essential to detect minute defects on steel surfaces during production. However, this has been a persistent challenge due to the minuscule and dense nature of these defects. To this end, we propose an efficient defect detector called Vision Grapher with Hadamard (ViGh) , which employs a novel attention mecha-nism (HDmA) to establish local-to-local relationships within an image and integrates global relationships by graph convolution. With the HDmA module, we can not only fuse information under the same field of view, but also under different fields of view, which significantly enhances the richness of the acquired features. In addition, com-pared to convolutional neural networks, graph neural networks can utilize the contextual information in the image more effectively and resulting in better performance. We eval-uate our model on the NEU-DET and GC-10 benchmark datasets, which encompass six and ten types of defects on the surfaces of hot-rolled and cold-rolled steel, and our mod-el achieves a mean Average Precision (mAP) of 79.04% and 66.93% on the two datasets, respectively. The results demonstrate that our model significantly improves the accuracy of defect detection compared to existing methods.
{"title":"A Steel Surface Defect Detection Model Based on Graph Neural Networks","authors":"Wenkai Pang, Zhi Tan","doi":"10.1088/1361-6501/ad1c4b","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1c4b","url":null,"abstract":"\u0000 Steel is an indispensable raw material in the construction industry. To avert catastrophic events such as building collapse, it is essential to detect minute defects on steel surfaces during production. However, this has been a persistent challenge due to the minuscule and dense nature of these defects. To this end, we propose an efficient defect detector called Vision Grapher with Hadamard (ViGh) , which employs a novel attention mecha-nism (HDmA) to establish local-to-local relationships within an image and integrates global relationships by graph convolution. With the HDmA module, we can not only fuse information under the same field of view, but also under different fields of view, which significantly enhances the richness of the acquired features. In addition, com-pared to convolutional neural networks, graph neural networks can utilize the contextual information in the image more effectively and resulting in better performance. We eval-uate our model on the NEU-DET and GC-10 benchmark datasets, which encompass six and ten types of defects on the surfaces of hot-rolled and cold-rolled steel, and our mod-el achieves a mean Average Precision (mAP) of 79.04% and 66.93% on the two datasets, respectively. The results demonstrate that our model significantly improves the accuracy of defect detection compared to existing methods.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"50 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139447229","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 : 2024-01-05DOI: 10.1088/1361-6501/ad1b9e
Xavier Lefebvre, Antonella Succar, E. Bédard, Michèle Prévost, E. Robert
Measuring aerosol size distribution with precision is critical to understand the transmission of pathogens causing respiratory illnesses and to identify risk mitigation strategies. It is however a challenging task as the size of pathogen-carrying particles evolves over time due to evaporation. Although measurement techniques well established in the field of aerosol science are often used to characterize bioaerosols, their performance is seldom assessed with respect to evaporation and deposition in sampling lines. Four instruments providing aerosol size distribution were compared using oil and water-based particles. They each rely on different measurement principles: phase doppler anemometry, light scattering, electrical mobility and aerodynamic impaction. Size distributions of oil-based particles showed consistency across different measurement instruments, but significant discrepancies arose for water-based particles undergoing evaporation. These larger differences result from both evaporation and particle deposition in transit between the sampling point and the measurement inside the instrument. Phase doppler anemometry was best suited for precise size distribution measurement, as it eliminates the need for a sampling line, thereby preventing particle loss or evaporation during transit. With this instrument as a reference, empirical correction factors for evaporation and deposition were derived from dimensionless numbers and experimental data, enabling quantitative assessment of bioaerosol size distribution using different instruments. To obtain the size distribution at the source of the aerosol generation, complete drying of a salt solution was performed. Using the complete drying technique and accounting for losses, sampling instruments can reliably provide this critical information and allow for thorough risk assessment in the context of airborne transmission.
{"title":"Comparison of aerosol spectrometers : Accounting for evaporation and sampling losses","authors":"Xavier Lefebvre, Antonella Succar, E. Bédard, Michèle Prévost, E. Robert","doi":"10.1088/1361-6501/ad1b9e","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1b9e","url":null,"abstract":"\u0000 Measuring aerosol size distribution with precision is critical to understand the transmission of pathogens causing respiratory illnesses and to identify risk mitigation strategies. It is however a challenging task as the size of pathogen-carrying particles evolves over time due to evaporation. Although measurement techniques well established in the field of aerosol science are often used to characterize bioaerosols, their performance is seldom assessed with respect to evaporation and deposition in sampling lines. Four instruments providing aerosol size distribution were compared using oil and water-based particles. They each rely on different measurement principles: phase doppler anemometry, light scattering, electrical mobility and aerodynamic impaction. Size distributions of oil-based particles showed consistency across different measurement instruments, but significant discrepancies arose for water-based particles undergoing evaporation. These larger differences result from both evaporation and particle deposition in transit between the sampling point and the measurement inside the instrument. Phase doppler anemometry was best suited for precise size distribution measurement, as it eliminates the need for a sampling line, thereby preventing particle loss or evaporation during transit. With this instrument as a reference, empirical correction factors for evaporation and deposition were derived from dimensionless numbers and experimental data, enabling quantitative assessment of bioaerosol size distribution using different instruments. To obtain the size distribution at the source of the aerosol generation, complete drying of a salt solution was performed. Using the complete drying technique and accounting for losses, sampling instruments can reliably provide this critical information and allow for thorough risk assessment in the context of airborne transmission.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"32 42","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139382562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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