Measurement Science and Technology最新文献_第3页

Dual Temporal Attention Mechanism-based Convolutional LSTM Model for Industrial Dynamic Soft Sensor 基于双时态注意机制的卷积 LSTM 模型用于工业动态软传感器

Measurement Science and Technology Pub Date : 2024-07-24 DOI: 10.1088/1361-6501/ad66f7

Jiarui Cui, Yuyu Shi, Jian Huang, Xu Yang, Jingjing Gao, Qing Li

{"title":"Dual Temporal Attention Mechanism-based Convolutional LSTM Model for Industrial Dynamic Soft Sensor","authors":"Jiarui Cui, Yuyu Shi, Jian Huang, Xu Yang, Jingjing Gao, Qing Li","doi":"10.1088/1361-6501/ad66f7","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66f7","url":null,"abstract":"\u0000 Deep learning is an appropriate methodology for modeling complex industrial data in the field of soft sensors, owing to its powerful feature representation capability. Given the nonlinear and dynamic nature of the process industry, the key challenge for soft sensor technology is to effectively mine dynamic information from long sequences and accurately extract features of relevance to quality. A dual temporal attention mechanism-based convolutional long short-term memory network (DTA-ConvLSTM) under an encoder-decoder framework is proposed as a soft sensor model to acquire quality-relevant dynamic features from serial data. Considering different influences of process variables for prediction at multiple time steps and various locations, ConvLSTM and temporal self-attention mechanism are utilized as the encoder to adaptively fuse spatiotemporal features and capture long-term dynamic properties of process in order to capture the trends of industrial variables. Furthermore, a quality-driven temporal attention mechanism is employed throughout the decoding process to dynamically select relevant features to more accurately track quality changes. The encoder-decoder model meticulously analyses the interactions between process and quality variables by incorporating dual-sequence dynamic information to improve the prediction performance. The validity and superiority of the DTA-ConvLSTM model was validated on two industrial case studies of the debutanizer column and sulfur recovery unit. Compared to the traditional LSTM model, the proposed model demonstrated a substantial improvement with the accuracy R2 up to 97.3% and 94.9% and the root mean square error reducing to 0.122 and 0.022.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"44 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU 基于 BWO-CNN-LSTM-GRU 的盾构机位置姿态多步智能预测

Measurement Science and Technology Pub Date : 2024-07-24 DOI: 10.1088/1361-6501/ad6176

Xuanyu Liu, Wenshuai Zhang, Mengting Jiang, Yudong Wang, Lili Ma

{"title":"Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU","authors":"Xuanyu Liu, Wenshuai Zhang, Mengting Jiang, Yudong Wang, Lili Ma","doi":"10.1088/1361-6501/ad6176","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6176","url":null,"abstract":"\u0000 Realizing automatic control of shield machine tunneling attitude is a challenging problem. Realizing multi-step intelligent prediction for attitude and position is an important prerequisite for solving this problem in the tunneling process with complex and varied geological environments. In this paper, a multi-step intelligent predictive scheme based on beluga whale optimization-convolutional neural network-Long Short-term memory-gated recurrent unit (BWO-CNN-LSTM-GRU) is proposed for shield machine position attitude. First, Pearson correlation analysis is utilized to determine the input feature variables from the construction data and temporalize the input features. Subsequently, CNN-LSTM-GRU predictive models are established for the six positional parameters, separately. Among them, CNN performs feature extraction on the input variables, and LSTM-GRU realizes the predictions for the target positional parameters. In the end, the optimization of the convolutional layer dimension, the number of convolutional layers, iterations, the learning rate, the number of neurons in the LSTM layer and GRU layer of each position predictive model is performed on the basis of BWO, separately, and the best hyperparameters found are built into a BWO-CNN-LSTM-GRU position predictive model, which realizes the multi-step intelligent predictions for the shield machine’s position. The proposed approach is examined by utilizing the Beijing Metro Line 10. The results show that the predictive deviation of the position predictive model is within 3 mm, and the positional trajectory points obtained on the basis of the predicted values and the 3D coordinate system are highly coincident with the actual trajectory points. Therefore, the approach provides a more accurate predictive result for shield attitude and position and can provide a decision-making scheme for further realizing the coordinated autonomous control of shield machine.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"59 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EFS-YOLO: A Lightweight Network Based on Steel Strip Surface Defect Detection EFS-YOLO：基于钢带表面缺陷检测的轻量级网络

Measurement Science and Technology Pub Date : 2024-07-24 DOI: 10.1088/1361-6501/ad66fe

Beilong Chen, Mingjun Wei, Jianuo Liu, Hui Li, Chenxu Dai, Jinyun Liu, Zhanlin Ji

{"title":"EFS-YOLO: A Lightweight Network Based on Steel Strip Surface Defect Detection","authors":"Beilong Chen, Mingjun Wei, Jianuo Liu, Hui Li, Chenxu Dai, Jinyun Liu, Zhanlin Ji","doi":"10.1088/1361-6501/ad66fe","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66fe","url":null,"abstract":"\u0000 With the advancement of deep learning technologies, industrial intelligent detection algorithms are gradually being applied in practical steel surface defect detection. Addressing the issues of high computational resource consumption and poor detection performance faced by existing models in large-scale industrial production lines, this paper proposes an EFS-YOLO model based on improved YOLOv8s architecture. Firstly, the EfficientViT is employed as the feature extraction network, effectively reducing the model's parameters and calculations while enhancing its capability to represent defect features. Secondly, the designed lightweight C2f-Faster-EffectiveSE Block (CFE-Block) was integrated into the model neck, accelerating feature fusion and better preserving detailed defect features at lower levels. Finally, the model detection head was reconstructed using the concept of shared parameters to address the high computational cost of the original detection head. Experimental results on the NEU-DET and GC10-DET datasets demonstrate that compared to the baseline model, the proposed model achieves a reduction in parameters, calculations and size by 49.5%, 62.7% and 46.9% respectively. It also exhibits an improvement in accuracy by 2.4% and 2.3% on the two datasets. The EFS-YOLO model effectively enhances precision in steel surface defect detection while maintaining lightweight characteristics, offering superior performance.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"52 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parallel-beams magnetic actuator for in-situ transmission electron microscope observation of mechanical testing 用于机械测试原位透射电子显微镜观察的平行梁磁力传动器

Measurement Science and Technology Pub Date : 2024-07-24 DOI: 10.1088/1361-6501/ad66f4

Matthieu Denoual, Nicolas Lobato-Dauzier, Luis Saluden, L. Jalabert, Takaaki Sato, Hiroyuki Fujita

{"title":"Parallel-beams magnetic actuator for in-situ transmission electron microscope observation of mechanical testing","authors":"Matthieu Denoual, Nicolas Lobato-Dauzier, Luis Saluden, L. Jalabert, Takaaki Sato, Hiroyuki Fujita","doi":"10.1088/1361-6501/ad66f4","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66f4","url":null,"abstract":"\u0000 Understanding the microscopic mechanisms behind mechanical fractures is essential for enhancing material properties and increasing reliability through fatigue suppression. Conventional mechanical testing methods, such as indentation tests that press a sharp needle into a specimen or tensile tests using hydraulic pumps, are unable to capture nanoscale deformations under applied forces. As a result, the microscopic mechanisms that influence mechanical properties are often inferred indirectly, and material design largely depends on the engineer’s intuition and occasional serendipity To overcome these challenges, in-situ observation techniques utilizing transmission electron microscopes (TEMs) have been developed to enable the observation of sample deformations at the nanoscale. However, despite their high resolution, conventional TEMs are limited by a small available space -often just a few millimeters- that restricts the application of sufficient force to fracture specimens. Traditional actuation methods, such as thermal expansion, electrostatic force, and piezoelectric actuators, fail to generate significant forces within such confined spaces. In response to these limitations, our research involved the development of a micromachine with multiple parallel beams. This device leverages the Laplace force generated by an electric current passing through the beams and the magnetic field of the TEM. We demonstrated the capability to produce significant force using the magnetic field from the microscope’s magnetic lens. The actuator developed in our study successfully generated forces exceeding 50 µN, marking a significant advancement in the in-situ observation capabilities for mechanical testing.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"4 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online Detection Method for Magnetic Suspension Concentration Based on Machine Vision 基于机器视觉的磁性悬浮液浓度在线检测方法

Measurement Science and Technology Pub Date : 2024-07-24 DOI: 10.1088/1361-6501/ad66f3

Yun Yang, Baohu Han, Jinzhao Zuo, Long Li, Kenan Li

{"title":"Online Detection Method for Magnetic Suspension Concentration Based on Machine Vision","authors":"Yun Yang, Baohu Han, Jinzhao Zuo, Long Li, Kenan Li","doi":"10.1088/1361-6501/ad66f3","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66f3","url":null,"abstract":"\u0000 With the intelligent development of magnetic particle inspection, the quality of magnetic\u0000indications formed at cracks is closely related to the accuracy of magnetic particle inspection\u0000image analysis results. The concentration of magnetic suspension is a key process parameter\u0000affecting the quality of magnetic indication formation. Hence, this study presents an online\u0000detection method based on machine vision for measuring magnetic suspension concentration.\u0000The method initially enhances the contrast of images of the pear-shaped measuring tube\u0000containing magnetic suspension and then extracts scale lines through feature analysis and\u0000morphological processing. A method for extracting the magnetic particle sedimentation area of\u0000magnetic suspension based on a dual-threshold segmentation algorithm is proposed. The\u0000contour filtering algorithm and pixel calibration method are used to obtain the magnetic particle\u0000concentration of the non-estimation and estimation areas based on scale line extraction,\u0000ultimately forming an online accurate detection method for magnetic suspension concentration\u0000values. Experiments were conducted to validate the method against different concentrations,\u0000turbidity levels, tilting angles of the pear-shaped measuring tube, and ambient brightness. The\u0000results show that the error in magnetic suspension concentration detection based on this method\u0000is within 5%. This has certain reference value for the stable control of magnetic suspension\u0000concentration and for enhancing the reliability of intelligent decision-making results in\u0000magnetic particle inspection.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"23 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-modality Hierarchical Attention Networks for Defect Identification in Pipeline MFL Detection 多模态分层注意力网络用于管道 MFL 检测中的缺陷识别

Measurement Science and Technology Pub Date : 2024-07-24 DOI: 10.1088/1361-6501/ad66f8

Gang Wang, Ying Su, Mingfeng Lu, Rongsheng Chen, Xusheng Sun

{"title":"Multi-modality Hierarchical Attention Networks for Defect Identification in Pipeline MFL Detection","authors":"Gang Wang, Ying Su, Mingfeng Lu, Rongsheng Chen, Xusheng Sun","doi":"10.1088/1361-6501/ad66f8","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66f8","url":null,"abstract":"\u0000 Magnetic flux leakage (MFL) testing is widely used for acquiring MFL signals to detect pipeline defects, and data-driven approaches have been effectively investigated for MFL defect identification. However, with the increasing complexity of pipeline defects, current methods are constrained by the incomplete information from single modal data, which fails to meet detection requirements. Moreover, the incorporation of multimodal MFL data results in feature redundancy. Therefore, the Multi-Modality Hierarchical Attention Networks (MMHAN) are proposed for defect identification. Firstly, stacked residual blocks with Cross-Level Attention Module (CLAM) and multiscale 1D-CNNs with Multiscale Attention Module (MAM) are utilized to extract multiscale defect features. Secondly, the Multi-Modality Feature Enhancement Attention Module (MMFEAM) is developed to enhance critical defect features by leveraging correlations among multimodal features. Lastly, the Multi-Modality Feature Fusion Attention Module (MMFFAM) is designed to dynamically integrate multimodal features deeply, utilizing the consistency and complementarity of multimodal information. Extensive experiments were conducted on multimodal pipeline datasets to assess the proposed MMHAN. The experimental results demonstrate that MMHAN achieves a higher identification accuracy, validating its exceptional performance.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"63 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid algorithms in path planning for autonomous navigation of Unmanned Aerial Vehicle: A comprehensive review 无人驾驶飞行器自主导航路径规划中的混合算法：综合评述

Measurement Science and Technology Pub Date : 2024-07-24 DOI: 10.1088/1361-6501/ad66f5

Minh Tuyet Dang, Dung Ba Nguyen

引用次数: 0

Domain Generalized Open-Set Intelligent Fault Diagnosis Based on Feature Disentanglement Meta-Learning 基于特征分解元学习的领域泛化开放集智能故障诊断

Measurement Science and Technology Pub Date : 2024-07-24 DOI: 10.1088/1361-6501/ad66ff

Xiangdong Zhou, Xiao Deng, Zhengwu Liu, Haidong Shao, Bin Liu

{"title":"Domain Generalized Open-Set Intelligent Fault Diagnosis Based on Feature Disentanglement Meta-Learning","authors":"Xiangdong Zhou, Xiao Deng, Zhengwu Liu, Haidong Shao, Bin Liu","doi":"10.1088/1361-6501/ad66ff","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66ff","url":null,"abstract":"\u0000 Existing domain generalization-based intelligent fault diagnosis methods mainly focus on learning domain-invariant features. However, in practical scenarios, these features are difficult to extract and effectively distinguish from class-related features. Moreover, these methods often assume identical label distributions between the source and target domain, making it challenging to handle scenarios where unknown classes exist in the target domain. To address these issues, this paper proposes a domain generalized open-set intelligent fault diagnosis method based on feature disentanglement meta-learning. A binary mask feature disentanglement module is constructed to overcome the information loss caused by feature reconstruction, enabling the separation of domain-specific and class-related features. Additionally, a meta-purification loss function is defined, incorporating a correlation loss term to remove impurity features from the class-related features, and further purifying class information through feature combination pairing. The method is trained on multiple source domains using a meta-learning strategy and generalized to target domains with unknown classes. The method is utilized for bearing fault diagnosis, designing multi-task experimental scenarios under different rotational speeds, and compared with existing domain generalization methods. Experimental results show that the proposed method exhibits excellent generalization ability and effectively addresses the issue of domain generalized open-set fault diagnosis.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"5 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensor-based intelligent tool online monitoring technology: Applications and progress 基于传感器的智能工具在线监测技术：应用与进展

Measurement Science and Technology Pub Date : 2024-07-24 DOI: 10.1088/1361-6501/ad66f1

Jiashuai Huang, Guang-qiang Chen, Hong Wei, Zhuang Chen, Yingxin Lv

{"title":"Sensor-based intelligent tool online monitoring technology: Applications and progress","authors":"Jiashuai Huang, Guang-qiang Chen, Hong Wei, Zhuang Chen, Yingxin Lv","doi":"10.1088/1361-6501/ad66f1","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66f1","url":null,"abstract":"\u0000 With the continuous development of the aerospace, defense, and military industry, along with other high-end fields, the complexity of machined parts has gradually increased. Consequently, the demand for tool intelligence has also strengthened. However, traditional tools are prone to wear during cutting due to high cutting forces, high temperatures, and vibrations. Intelligent tools, in contrast to traditional ones, integrate sensors into their design, allowing for real-time monitoring of the cutting status and timely prediction of tool wear. The application of intelligent tools in machining significantly enhances machining quality, increases productivity, and reduces production costs. In this review, first, the tool wear monitoring methods were classified and discussed. Second, the intelligence and innovation of sensors in monitoring cutting force, temperature, and vibration were introduced, and the commonly used types of sensors for online monitoring of cutting force were detailed. Furthermore, different types of sensors in tool wear were discussed, and the advantages of multi-sensor monitoring were summarized. Some urgent issues and perspectives that need to be addressed were proposed, providing new ideas for the design and development of intelligent tools.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Adaptive Weighted Integration Method for Multipath Ultrasonic Flowmeter 用于多径超声波流量计的自适应加权积分法

Measurement Science and Technology Pub Date : 2024-07-24 DOI: 10.1088/1361-6501/ad6700

Suna Guo, Jiawen Han, Baonan Li, L. Fang, Tao Zhang, Fan Wang

引用次数: 0