Measurement Science and Technology最新文献

{"title":"Morphological characterization of concave particle based on convex decomposition","authors":"Libing Du, Zirui Li, Xinrong Liu, Zhongping Yang","doi":"10.1088/1361-6501/ad66fa","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66fa","url":null,"abstract":"\u0000 Particle morphology is an important factor affecting the mechanical properties of granular materials. However, it is difficult to quantify the morphology characteristics of the complex concave particle. Fortunately, complex particle can be segmented by convex decomposition, so a new shape index named convex decomposition coefficient (CDC) related to the number of segmentations is proposed. First, the pocket concavity was introduced to simplify the morphology hierarchically. Second, the cut weight linked to concavity was defined and convex decomposition was linearly optimised by maximizing the total cut weights. Third, the CDC was defined as the minimum block number where the block area ratio cumulatively exceeded 0.9 in descending order. Finally, the proposed index was used to quantify the particle morphology of coral sand. The results demonstrate that the CDC of coral sands mainly ranges from 2 to 6, with a positively skewed distribution. Furthermore, CDC correlates well with three shape indices: sphericity, particle size, and convexity. Larger CDC is associated with smaller sphericity, larger particle size, and smaller convexity. The index has certain scientific research value and practical significance.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922929","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":"Precise orbit determination of integrated BDS-3 and LEO satellites with ambiguity fixing under regional ground stations","authors":"Wen Lai, Guanwen Huang, Le Wang, Zhiwei Qin, Run Li, Shichao Xie, Haonan She","doi":"10.1088/1361-6501/ad6924","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6924","url":null,"abstract":"\u0000 The ambiguity resolution (AR) significantly enhances the accuracy of precise orbit determination (POD). There have been numerous studies of different forms of POD: double-difference (DD), single-difference (SD), and un-differenced (UD) AR methods for global navigation satellite systems (GNSS) or low earth orbit (LEO). However, challenges persist in the integrated POD (IPOD) of the GNSS and LEO at regional ground stations. These challenges include the frequent selection of dual receiver-satellite pairs in DD methods, and time-varying hardware biases in LEO receivers for UD methods. In addition, the SD AR method has not been explored in IPOD, resulting in unfixed ambiguities. In this study, we investigated the feasibility and performance enhancement of AR in the BeiDou Navigation Satellite System (BDS) and LEO IPOD under regional ground stations using simulated ground and onboard observations. First, we introduce AR models applicable to BDS and LEO IPOD and analyze the applicability of different AR models for IPOD under regional ground stations. We designed a study to utilize SD ambiguity, which eliminates the time-varying hardware bias of the LEO receiver end, to estimate the uncalibrated phase delay (UPD) of the satellite end. Furthermore, we designed the BDS-3 and LEO constellations with 24 regional ground stations in China and simulated seven days of observations. Subsequently, the narrow-lane (NL) UPD quality and AR performance were analyzed, and a solution with satisfactory stability and residual distribution was obtained, enabling the implementation of SD AR. The daily fixed rate for wide-lane ambiguities exceeded 99%, while for NL ambiguities it surpasses 86%. After fixing ambiguities, the BDS-3 orbit’s along-track and cross-track components significantly improved. Simultaneously, LEO orbit solutions improved by over 20% in all three directions. Overall, the UPD estimation model using SD ambiguities yielded satisfactory UPD results, enabling AR and significantly enhancing the orbit accuracy of GNSS and LEO.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929641","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":"TSMDA: intelligent fault diagnosis of rolling bearing with two stage multi-source domain adaptation","authors":"Qianqian Zhang, Zhongwei Lv, Caiyun Hao, Haitao Yan, Yingzhi Jia, Yang Chen, Qiuxia Fan","doi":"10.1088/1361-6501/ad69b0","DOIUrl":"https://doi.org/10.1088/1361-6501/ad69b0","url":null,"abstract":"\u0000 Fault diagnosis plays a critical role in ensuring the safe operation of machinery. Multi-source domain adaptation (DA) leverages rich fault knowledge from source domains to enhance diagnostic performance on unlabeled target domains. However, most existing methods only align marginal distributions, neglecting inter-class relationships, which results in decreased performance under variable working conditions and small samples. To overcome these limitations, two stage multi-source domain adaptation (TSMDA) has been proposed for bearing fault diagnosis. Specifically, wavelet packet decomposition is applied to analyze fault information from signals. For small sample datasets, Diffusion is used to augment the dataset and serve as the source domain. Next, multi-scale features are extracted, and mutual information is computed to prevent the negative transfer. DA is divided into two stages. Firstly, multikernel maximum mean discrepancy is used to align the marginal distributions of the multi-source and target domains. Secondly, the target domain is split into subdomains based on the calculated pseudo-labels. Conditional distributions are aligned by minimizing the distance from samples to the center of the non-corresponding domain. The effectiveness of the proposed method is verified by extensive experiments on two public datasets and one experimental dataset. The results demonstrate that TSMDA has high and stable diagnostic performance and provides an effective method for practical fault diagnosis.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926513","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":"High-accuracy and lightweight weld surface defect detector based on graph convolution decoupling head","authors":"Guanqiang Wang, Ming-Song Chen, Y.C. Lin, Xianhua Tan, Chizhou Zhang, Kai Li, Bai-Hui Gao, Yu-Xin Kang, Weiwei Zhao","doi":"10.1088/1361-6501/ad63c2","DOIUrl":"https://doi.org/10.1088/1361-6501/ad63c2","url":null,"abstract":"\u0000 The essence of the difficulties for weld surface detection is that there is a lot of interference information during detection. This study aims to enhance the detection accuracy while keeping great deployment capabilities of a detection model for weld surface defects. To achieve this goal, an improved Yolo-GCH model is proposed based on the stable and fast Yolo-v5. The improvements primarily involve introducing a graph convolution network combined with a self-attention mechanism in the head part (i.e., GCH). This component focuses on improving the insufficient recognition capability of CNN for similar defects in complex environments. Furthermore, to address the presence of potentially ambiguous samples in complex welding environments, the label assignment strategy of simOTA is implemented to optimize the anchor frame. Additionally, a streamlined structure, aiming to improve model detection speed while minimizing performance impact, has been designed to enhance the applicability of the model. The results demonstrate that the cooperation of GCH and simOTA significantly improves the detection performance while maintaining the inference speed. These strategies lead to a 2.5% increase in mAP@0.5 and reduce the missing detection rates of weld and 8 types of defects by 32.9% and 84.1% respectively, surpassing other weld surface detection models. Furthermore, the impressive applicability of the model is verified across four scaled versions of Yolo-v5. Based on the proposed strategies, the FPS increases by more than 30 frames in the fast s and n versions of Yolo-v5. These results demonstrate the great potential of the model for industrial applications.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641057","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":"Gap Measurement Method Based on Projection Lines and Convex Analysis of 3D Points Cloud","authors":"Wei Pan, binfeng jiang, wenming tang, Fupei Wu, shengping li","doi":"10.1088/1361-6501/ad63c3","DOIUrl":"https://doi.org/10.1088/1361-6501/ad63c3","url":null,"abstract":"\u0000 Accurate measurement of the gap between the lower surface of the relay and the ground is critical for ensuring the quality of the finished product. Traditional gap measurement methods have some shortcomings, such as low accuracy, poor robustness, and loss of depth clues in obscured areas. In this study, a novel gap measurement method based on computer vision is proposed, which includes a projection line model based on guided filtering and a 3D surface point cloud model based on a three dimensional plane reference.- The relay gap was measured by calculating the projection lines of the upper and lower surfaces of the gap with an error of ±0.016 mm. A 3D point cloud model captures the key features of the underside of the relay through image processing techniques, and combines convex hull and centroid estimation to construct a three-dimensional reference plane for the gap, which could achieve high-precision, real-time measurement of the gap (with an error less than ±0.0087 mm). The experimental measurement results show that the proposed method is better than the SelfConvNet method, which has a high measurement accuracy and strong anti-interference ability, and an accuracy rate of up to 99.5% in factory relay quality inspection experiments.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643700","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":"Remaining Useful Life Prediction Method Based on Stacked Autoencoder and Generalized Wiener Process for Degrading Bearing","authors":"Zhe Chen, Yonghua Li, Qi Gong, Denglong Wang, Xuejiao Yin","doi":"10.1088/1361-6501/ad633f","DOIUrl":"https://doi.org/10.1088/1361-6501/ad633f","url":null,"abstract":"\u0000 Remaining Useful Life (RUL) prediction using deep learning networks primarily produces point estimates of RUL, but capturing the inherent uncertainty in RUL prediction is difficult. The use of the stochastic process approach can reflect the uncertainty in RUL predictions. However, the amount of data generated during equipment operation cannot be effectively utilized. This paper aims to propose an adaptive RUL prediction method tailored for extensive datasets and prediction uncertainty, effectively harnessing the strengths of deep learning methods in managing massive data and stochastic process techniques in quantifying uncertainties. RUL prediction method, based on Stacked Autoencoder (SAE) combined with Generalized Wiener Process, employs SAE to extract profound underlying features from the monitoring signals. Principal Component Analysis (PCA) is then used to select highly trending features as inputs. The output of PCA accurately reflects health status. A Generalized Wiener Process is used to construct a model for the evolution of the health indicators. The estimation values for the model parameters are determined using the Maximum Likelihood Estimation method. Furthermore, an adaptive update is performed based on Bayesian theory. Utilizing the sense of the first hitting time concept, the Probability Density Function for RUL prediction is derived accurately. Finally, the effectiveness and superiority of the proposed method is verified using numerical simulations and experimental studies of bearing degradation data. The method improves the life prediction accuracy while reducing the prediction uncertainty.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141648140","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":"Tool wear prediction based on kernel principal component analysis and least square support vector machine","authors":"Kangping Gao, Xinxin Xu, Shengjie Jiao","doi":"10.1088/1361-6501/ad633c","DOIUrl":"https://doi.org/10.1088/1361-6501/ad633c","url":null,"abstract":"\u0000 To accurately predict the amount of tool wear in the machining process, a monitoring model of tool wear based on multi-sensor information feature fusion is proposed. First, by collecting the cutting force, vibration, and acoustic emission signals of the tool during the whole life cycle, the multi-domain characteristics of the signal are extracted; then, kernel principal component analysis is used to reduce the dimensionality of the extracted data, and the principal components whose cumulative contribution ratio exceeds 85% are obtained. The redundant features with little correlation with tool wear were removed from the feature vectors to generate the fusion features. Finally, the fusion features are input into the least squares support vector machine model optimized by particle swarm algorithm for regression prediction of tool wear. The non-linear mapping relationship between the physical signal and the tool wear is discovered, which effectively realizes the prediction of the tool wear. Compared with the existing tool wear prediction methods, the method proposed has higher prediction accuracy.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141644906","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":"Enhanced Feature Extraction YOLO Industrial Small Object Detection Algorithm based on Receptive-Field Attention and Multi-scale Features","authors":"Hongfeng Tao, Yuechang Zheng, Yue Wang, Jier Qiu, Stojanovic Vladimir","doi":"10.1088/1361-6501/ad633d","DOIUrl":"https://doi.org/10.1088/1361-6501/ad633d","url":null,"abstract":"\u0000 To guarantee the stability and safety of industrial production, it is necessary to regulate the behavior of employees. However, the high background complexity, low pixel count, occlusion and fuzzy appearance can result in a high leakage rate and poor detection accuracy of small objects. Considering the above problems, this paper proposes the EFE-YOLO (Enhanced feature extraction-You Only Look Once) algorithm to improve the detection of industrial small objects. To enhance the detection of fuzzy and occluded objects, the PSRFA (PixelShuffle and Receptive-Field Attention) upsampling module is designed to preserve and reconstruct more detailed information and extract the receptive-field attention weights. Furthermore, the MSE (multi-scale and efficient) downsampling module is designed to merge global and local semantic features to alleviate the problem of false and missed detection. Subsequently, the AFAF (Adaptive Feature Adjustment and Fusion) module is designed to highlight the important features and suppress background information that is not beneficial for detection. Finally, the EIoU loss function is used to improve the convergence speed and localization accuracy. All experiments are conducted on homemade dataset. The improved YOLOv5 algorithm proposed in this paper improves mAP@0.50 (mean average precision at a threshold of 0.50) by 2.8% compared to the YOLOv5 algorithm. The average precision and recall of small objects show an improvement of 8.1% and 7.5%, respectively. The detection performance is still leading in comparison with other advanced algorithms.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141646298","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":"Research on Transformer Based Online Condition Monitoring Method for IPM","authors":"Shengxue Tang, Jinze Zhao, Liqiang Tan, Jinjing Yan","doi":"10.1088/1361-6501/ad6341","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6341","url":null,"abstract":"\u0000 The Intelligent Power Module (IPM) has the integrated packaging, leading to the advantages of convenient use, safety and reliability. However, once it fails, it will cause the whole power supply system to be inoperative, and it is necessary to perform online Condition Monitoring (CM) of the IPM. In this paper, we extract the aging characteristics such as dynamic equivalent resistance, peak-to-peak value, switching frequency, and turn-off time only from the voltage and current of IPM drain-source port, and then propose a non-intrusive online CM method for the IPM based on the Transformer Neural Network (TNN). We analyse the internal aging mechanism of the IPM, the changing law of aging features, and construct multi-dimensional aging fusion features, and then the TNN model is used to monitor early parameters drift of multi-dimensional fusion feature vectors and realize the accurate online prediction of IPM health condition. The experimental analysis results show that the fault prediction accuracy reaches 96%, and can realize the health CM under the condition of noise interference, weak aging features and few external observable points.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645955","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":"EIT probe based intraoperative tissue inspection for minimally invasive surgery","authors":"Jing Guo, Baiyang Zhuang, Renkai Li, Zexuan Lin, Zhuoqi Cheng, Haifang Lou","doi":"10.1088/1361-6501/ad6345","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6345","url":null,"abstract":"\u0000 Electrical Impedance Tomography (EIT) has become an integral component in the repertoire of medical imaging techniques, particularly due to its non-invasive nature and real-time imaging capabilities. Despite its potential, the application of EIT in Minimally Invasive Surgery (MIS) has been hindered by a lack of specialized electrode probes. Existing designs often compromise between invasiveness and spatial sensitivity: probes small enough for MIS often fail to provide detailed imaging, while those offering greater sensitivity are impractically large for use through a surgical trocar. Addressing this challenge, our study presents a breakthrough in EIT probe design. The open electrode probe we have developed features a line of 16 electrodes, thoughtfully arrayed to balance the spatial demands of MIS with the need for precise imaging. Employing an advanced EIT reconstruction algorithm, our probe not only captures images that reflect the electrical characteristics of the tissues but also ensures the homogeneity of the test material is accurately represented.The versatility of our probe is demonstrated by its capacity to generate high-resolution images of subsurface anatomical structures, a feature particularly valuable during MIS where direct visual access is limited. Surgeons can rely on intraoperative EIT imaging to inform their navigation of complex anatomical landscapes, enhancing both the safety and efficacy of their procedures. Through rigorous experimental validation using ex vivo tissue phantoms, we have established the probe's proficiency. The experiments confirmed the system's high sensitivity and precision, particularly in the critical tasks of subsurface tissue detection and surgical margin delineation.These capabilities manifest the potential of our probe to revolutionize the field of surgical imaging, providing a previously unattainable level of detail and assurance in MIS procedures.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141648323","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}