Pub Date : 2024-08-09DOI: 10.1088/1361-6501/ad66fa
Libing Du, Zirui Li, Xinrong Liu, Zhongping Yang
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.
{"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}
Pub Date : 2024-08-08DOI: 10.1088/1361-6501/ad6924
Wen Lai, Guanwen Huang, Le Wang, Zhiwei Qin, Run Li, Shichao Xie, Haonan She
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.
模糊分辨率（AR）大大提高了精确轨道测定（POD）的精度。对不同形式的 POD 进行了大量研究：针对全球导航卫星系统（GNSS）或低地球轨道（LEO）的双差分（DD）、单差分（SD）和非差分（UD）AR 方法。然而，区域地面站的全球导航卫星系统和低地轨道综合 POD（IPOD）仍面临挑战。这些挑战包括 DD 方法中频繁选择双接收器-卫星对，以及 UD 方法中低地球轨道接收器的时变硬件偏差。此外，SD AR 方法尚未在 IPOD 中进行探索，导致模糊性无法解决。在本研究中，我们利用模拟地面观测和星载观测，研究了北斗导航卫星系统（BDS）和低地轨道 IPOD 在区域地面站条件下使用自增益方法的可行性和性能提升。首先，我们介绍了适用于 BDS 和 LEO IPOD 的 AR 模型，并分析了不同 AR 模型对区域地面站下 IPOD 的适用性。我们设计了一项研究，利用消除低地轨道接收端时变硬件偏差的自毁模糊性来估计卫星端的未校准相位延迟（UPD）。此外，我们设计了 BDS-3 和 LEO 星座，在中国建立了 24 个区域地面站，并模拟了七天的观测。随后，分析了窄线（NL）UPD质量和AR性能，得到了稳定性和残差分布令人满意的解决方案，从而实现了SD AR。宽车道含混点的每日固定率超过 99%，而窄车道含混点的每日固定率超过 86%。在解决了模糊问题之后，BDS-3 轨道的沿轨和跨轨分量得到了显著改善。同时，低地轨道解决方案在所有三个方向上都提高了 20% 以上。总之，使用自毁模糊度的 UPD 估计模型取得了令人满意的 UPD 结果，实现了 AR 并大大提高了 GNSS 和 LEO 的轨道精度。
{"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}
Pub Date : 2024-08-08DOI: 10.1088/1361-6501/ad69b0
Qianqian Zhang, Zhongwei Lv, Caiyun Hao, Haitao Yan, Yingzhi Jia, Yang Chen, Qiuxia Fan
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.
{"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}
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.
{"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}
Pub Date : 2024-07-16DOI: 10.1088/1361-6501/ad63c3
Wei Pan, binfeng jiang, wenming tang, Fupei Wu, shengping li
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.
{"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}
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.
{"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}
Pub Date : 2024-07-15DOI: 10.1088/1361-6501/ad633c
Kangping Gao, Xinxin Xu, Shengjie Jiao
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.
{"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}
Pub Date : 2024-07-15DOI: 10.1088/1361-6501/ad633d
Hongfeng Tao, Yuechang Zheng, Yue Wang, Jier Qiu, Stojanovic Vladimir
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.
{"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}
Pub Date : 2024-07-15DOI: 10.1088/1361-6501/ad6341
Shengxue Tang, Jinze Zhao, Liqiang Tan, Jinjing Yan
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.
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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.
电阻抗断层扫描（EIT）已成为医学成像技术中不可或缺的组成部分，特别是由于其非侵入性和实时成像功能。尽管电阻抗断层扫描具有很大的潜力，但由于缺乏专用的电极探头，它在微创手术（MIS）中的应用一直受到阻碍。现有的设计往往在侵袭性和空间灵敏度之间折衷：对于微创手术来说，足够小的探头往往无法提供详细的成像，而那些灵敏度更高的探头又太大，无法通过手术套管使用。为了应对这一挑战，我们的研究在 EIT 探头设计方面取得了突破性进展。我们开发的开放式电极探头由 16 个电极组成，这些电极经过精心排列，既能满足 MIS 的空间要求，又能满足精确成像的需要。我们的探针采用先进的 EIT 重建算法，不仅能捕捉到反映组织电特性的图像，还能确保准确反映测试材料的均匀性。我们探针的多功能性体现在它能生成表面下解剖结构的高分辨率图像，这在 MIS 过程中特别有价值，因为直接观察受到限制。外科医生可以依靠术中 EIT 成像为其复杂解剖结构的导航提供信息，从而提高手术的安全性和有效性。通过使用体外组织模型进行严格的实验验证，我们确定了该探针的能力。实验证实了该系统的高灵敏度和高精确度，尤其是在表皮下组织检测和手术边缘划定等关键任务中。这些功能表明，我们的探针有可能彻底改变外科成像领域，为 MIS 手术提供以前无法实现的细节和保证。
{"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}
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