This paper proposes a normal tracking differential confocal measurement method for the inside and outside surface profiles, shell thickness uniformity, and central asymmetry of inside and outside surfaces of the hemispherical shell resonator (HSR). A differential confocal technique with high-transmittance focusing ability is used to measure a single point on the inside and outside surfaces of the HSR. The normal alignment measurement technique is used to accurately measure the inside and outside surfaces and shell thickness of the HSR with a common reference in one measurement process. The HSR is step-rotated to synchronously collect information on the inside and outside surfaces, and using the differential confocal sensor to measure the different normal-section profiles. The experimental results indicate successful measurement of HSR central asymmetry. The repeated measurement accuracy for the inside and outside surface profiles and thickness uniformity is better than 30 nm.
{"title":"A normal tracking differential confocal measurement method for multiple geometric parameters of hemispherical shell resonator with a common reference","authors":"Yuhan Liu, Xiaocheng Zhang, Yuan Fu, Yun Wang, Zhuxian Yao, Weiqian Zhao","doi":"10.1088/1361-6501/ad1a85","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1a85","url":null,"abstract":"\u0000 This paper proposes a normal tracking differential confocal measurement method for the inside and outside surface profiles, shell thickness uniformity, and central asymmetry of inside and outside surfaces of the hemispherical shell resonator (HSR). A differential confocal technique with high-transmittance focusing ability is used to measure a single point on the inside and outside surfaces of the HSR. The normal alignment measurement technique is used to accurately measure the inside and outside surfaces and shell thickness of the HSR with a common reference in one measurement process. The HSR is step-rotated to synchronously collect information on the inside and outside surfaces, and using the differential confocal sensor to measure the different normal-section profiles. The experimental results indicate successful measurement of HSR central asymmetry. The repeated measurement accuracy for the inside and outside surface profiles and thickness uniformity is better than 30 nm.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"59 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451094","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 : 2024-01-03DOI: 10.1088/1361-6501/ad1a86
Beiyan He, Chunli Zhu, Zhongxiang Li, Chun Hu, Dezhi Zheng
Sensors equipped on the high-speed train provide large amounts of data which contributes to its state monitoring. However, it is challenging to distinguish whether the fault originates from the mechanical component or the sensors themselves. The main difficulties lie in the biased amount of normal and fault data as well as the deficiency of multi-source data’s inherent correlation. In this paper, we propose a Bayesian Convolutional neural networks (CNN)-based fusion framework to enhance the ability to identify sensor errors. The framework utilizes wavelet time-frequency maps to extract abnormal features, employs a Bayesian CNN to obtain spatial features from a single sensor, integrates multi-source features via Bidirectional Long Short-Term Memory Network (Bi-LSTM) and enhances the acquired spatial and temporal features using an attention mechanism. The enhanced information finally generated leads to precise identification of the sensor faults. The proposed feature-level fusion framework and the associated attention mechanism facilitate discovering the inherent correlation and filtering of irrelevant information. Results indicate that our proposed method achieves 95.4% in terms of accuracy, which outperforms methods relying on feature extraction with single-source sensors by 7.8%.
{"title":"A Bayesian CNN-based Fusion Framework of Sensor Fault Diagnosis","authors":"Beiyan He, Chunli Zhu, Zhongxiang Li, Chun Hu, Dezhi Zheng","doi":"10.1088/1361-6501/ad1a86","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1a86","url":null,"abstract":"\u0000 Sensors equipped on the high-speed train provide large amounts of data which contributes to its state monitoring. However, it is challenging to distinguish whether the fault originates from the mechanical component or the sensors themselves. The main difficulties lie in the biased amount of normal and fault data as well as the deficiency of multi-source data’s inherent correlation. In this paper, we propose a Bayesian Convolutional neural networks (CNN)-based fusion framework to enhance the ability to identify sensor errors. The framework utilizes wavelet time-frequency maps to extract abnormal features, employs a Bayesian CNN to obtain spatial features from a single sensor, integrates multi-source features via Bidirectional Long Short-Term Memory Network (Bi-LSTM) and enhances the acquired spatial and temporal features using an attention mechanism. The enhanced information finally generated leads to precise identification of the sensor faults. The proposed feature-level fusion framework and the associated attention mechanism facilitate discovering the inherent correlation and filtering of irrelevant information. Results indicate that our proposed method achieves 95.4% in terms of accuracy, which outperforms methods relying on feature extraction with single-source sensors by 7.8%.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"97 23","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139387927","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 : 2023-12-29DOI: 10.1088/1361-6501/ad19bf
Huaxiang Pu, Ke Zhang, Haifeng Li
To improve fault diagnosis performance in complex noise environments, effective signal denoising techniques are necessary. However, traditional denoising methods have proven inadequate for multivariate fault signal denoising, neglecting the correlation among these signals. To this end, we propose a novel denoising module, inspired by traditional signal decomposition and reconstruction methods. Furthermore, to enhance the performance of proposed denoising module, we consider the influence of the receptive field and develop a receptive field transfer strategy using layer-aligned distillation learning. The experiments demonstrate that our approach effectively balances the denoising performance and computational load, offering a novel strategy for developing high-performance denoising networks. What's more, our strategy reduces the difficulty for fault diagnosis tasks under complex noise environments.
{"title":"A receptive field transfer strategy via layer-aligned distillation learning for fault signal denoising","authors":"Huaxiang Pu, Ke Zhang, Haifeng Li","doi":"10.1088/1361-6501/ad19bf","DOIUrl":"https://doi.org/10.1088/1361-6501/ad19bf","url":null,"abstract":"To improve fault diagnosis performance in complex noise environments, effective signal denoising techniques are necessary. However, traditional denoising methods have proven inadequate for multivariate fault signal denoising, neglecting the correlation among these signals. To this end, we propose a novel denoising module, inspired by traditional signal decomposition and reconstruction methods. Furthermore, to enhance the performance of proposed denoising module, we consider the influence of the receptive field and develop a receptive field transfer strategy using layer-aligned distillation learning. The experiments demonstrate that our approach effectively balances the denoising performance and computational load, offering a novel strategy for developing high-performance denoising networks. What's more, our strategy reduces the difficulty for fault diagnosis tasks under complex noise environments.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":" 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139142656","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 : 2023-12-29DOI: 10.1088/1361-6501/ad19c2
Xueyi Li, Kaiyu Su, Daiyou Li, Qiushi He, Zhijie Xie, Xiangwei Kong
Bearings are crucial components in rotating machinery equipment. Bearing fault diagnosis plays a significant role in the maintenance of mechanical equipment. In practical industrial settings, equipment conditions often vary continuously, making it challenging to collect data for all operating conditions for bearing fault diagnosis. This paper proposes a transfer learning approach for bearing fault diagnosis based on Adaptive Batch Normalization (AdaBN) and a combined optimization algorithm. Initially, a ResNet neural network is trained using source domain data. Subsequently, the trained model is transferred to the target domain, where AdaBN is applied to mitigate domain shift issues. Furthermore, a combined optimization algorithm is employed during model training to enhance fault diagnosis accuracy. Experimental validation is conducted using bearing data from the CWRU dataset and NEFU dataset. Comparison shows that AdaBN and the combined optimization algorithm improve bearing fault diagnosis accuracy effectively. On the NEFU dataset, the diagnostic accuracy exceeds 95%.
{"title":"Transfer Learning for Bearing Fault Diagnosis: Adaptive Batch Normalization and Combined Optimization method","authors":"Xueyi Li, Kaiyu Su, Daiyou Li, Qiushi He, Zhijie Xie, Xiangwei Kong","doi":"10.1088/1361-6501/ad19c2","DOIUrl":"https://doi.org/10.1088/1361-6501/ad19c2","url":null,"abstract":"Bearings are crucial components in rotating machinery equipment. Bearing fault diagnosis plays a significant role in the maintenance of mechanical equipment. In practical industrial settings, equipment conditions often vary continuously, making it challenging to collect data for all operating conditions for bearing fault diagnosis. This paper proposes a transfer learning approach for bearing fault diagnosis based on Adaptive Batch Normalization (AdaBN) and a combined optimization algorithm. Initially, a ResNet neural network is trained using source domain data. Subsequently, the trained model is transferred to the target domain, where AdaBN is applied to mitigate domain shift issues. Furthermore, a combined optimization algorithm is employed during model training to enhance fault diagnosis accuracy. Experimental validation is conducted using bearing data from the CWRU dataset and NEFU dataset. Comparison shows that AdaBN and the combined optimization algorithm improve bearing fault diagnosis accuracy effectively. On the NEFU dataset, the diagnostic accuracy exceeds 95%.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":" 72","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139144824","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 : 2023-12-29DOI: 10.1088/1361-6501/ad19c1
Yang Xiao, Tao Jiang, Guo-Wei Fan, Liu Zhang, Yu Gao, Le Zhang
Aiming at the problems of model errors, non-Gaussian noise and measurement anomaly in the spacecraft attitude estimation system, this article proposes an improved adaptive filtering method based on covariance matching, which solves the problems of simultaneous dynamics model error and measurement model error in the attitude estimation system, and at the same time, effectively reduces the effects of non-Gaussian noise and large outlier situations occurring in the vector measurement sensor. Firstly, an adaptive filtering algorithm based on the innovation sequence estimation covariance is investigated under the framework of multiplicative extended Kalman filtering (MEKF), which is used to correct process noise covariance, then the Sage-Husa adaptive Kalman filtering (SHAKF) method is combined to correct the measurement noise covariance, and finally the meticulous covariance adaptive multiplicative extended Kalman filter (MCA-MEKF) is designed. the proposed algorithm uses both innovation and SHAKF methods to correct the two covariance matrices simultaneously. Several attitude estimation simulation scenarios are set up to simulate the proposed algorithm in the presence of model errors, non-Gaussian noise, and large outlier. The simulation results demonstrate that the proposed algorithm outperforms the conventional algorithms in terms of estimation accuracy and robustness.
{"title":"A meticulous covariance adaptive Kalman filter for satellite attitude estimation","authors":"Yang Xiao, Tao Jiang, Guo-Wei Fan, Liu Zhang, Yu Gao, Le Zhang","doi":"10.1088/1361-6501/ad19c1","DOIUrl":"https://doi.org/10.1088/1361-6501/ad19c1","url":null,"abstract":"Aiming at the problems of model errors, non-Gaussian noise and measurement anomaly in the spacecraft attitude estimation system, this article proposes an improved adaptive filtering method based on covariance matching, which solves the problems of simultaneous dynamics model error and measurement model error in the attitude estimation system, and at the same time, effectively reduces the effects of non-Gaussian noise and large outlier situations occurring in the vector measurement sensor. Firstly, an adaptive filtering algorithm based on the innovation sequence estimation covariance is investigated under the framework of multiplicative extended Kalman filtering (MEKF), which is used to correct process noise covariance, then the Sage-Husa adaptive Kalman filtering (SHAKF) method is combined to correct the measurement noise covariance, and finally the meticulous covariance adaptive multiplicative extended Kalman filter (MCA-MEKF) is designed. the proposed algorithm uses both innovation and SHAKF methods to correct the two covariance matrices simultaneously. Several attitude estimation simulation scenarios are set up to simulate the proposed algorithm in the presence of model errors, non-Gaussian noise, and large outlier. The simulation results demonstrate that the proposed algorithm outperforms the conventional algorithms in terms of estimation accuracy and robustness.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":" 33","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139143988","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}
Variable speed is one of the common working conditions of mechanical equipment,which poses an important challenge to equipment fault diagnosis. The current solutions have the shortcomings of low computational efficiency and large diagnostic errors. The ability of attention mechanism to automatically extract useful features has begun to attract widespread attention in the field of mechanical intelligent fault diagnosis. Combining the advantages of attention mechanism and unsupervised learning, this paper proposes a squeeze-excitation attention guided sparse filtering (SESF) method for mechanical intelligent fault diagnosis method under variable speed. Firstly, the SE attention mechanism is embedded in SF algorithm to guide model training. Then, unsupervised feature extraction is carried out on the variable speed signal samples. The training results are adaptively screened and weighted to make the model pay more attention to the region with the most classify discrimination, so as to improve the feature extraction ability of the model to obtain useful information. Finally, two sets of gear and bearing tests under variable speed condition are adopted to testify the performance of the proposed method. The experimental results show that the SESF method can overcome the influence of variable speed to achieve accurate recognition of different mechanical faults and is superior to the other methods.
变速是机械设备常见的工作状态之一，这对设备故障诊断提出了重要挑战。目前的解决方案存在计算效率低、诊断误差大等缺点。在机械智能故障诊断领域，注意力机制自动提取有用特征的能力开始受到广泛关注。本文结合注意力机制和无监督学习的优势，提出了一种用于变速机械智能故障诊断方法的挤压激励注意力引导稀疏滤波（SESF）方法。首先，在 SF 算法中嵌入 SE 注意机制，引导模型训练。然后，对变速信号样本进行无监督特征提取。对训练结果进行自适应筛选和加权，使模型更加关注分类区分度最高的区域，从而提高模型的特征提取能力，获取有用信息。最后，采用两组变速条件下的齿轮和轴承试验来验证所提方法的性能。实验结果表明，SESF 方法可以克服变速的影响，实现对不同机械故障的准确识别，优于其他方法。
{"title":"Attention mechanism guided sparse filtering for mechanical intelligent fault diagnosis under variable speed condition","authors":"Rui Han, Jinrui Wang, Yanbin Wan, Jihua Bao, Xue Jiang, Zongzhen Zhang, Baokun Han, Shanshan Ji","doi":"10.1088/1361-6501/ad197a","DOIUrl":"https://doi.org/10.1088/1361-6501/ad197a","url":null,"abstract":"Variable speed is one of the common working conditions of mechanical equipment,which poses an important challenge to equipment fault diagnosis. The current solutions have the shortcomings of low computational efficiency and large diagnostic errors. The ability of attention mechanism to automatically extract useful features has begun to attract widespread attention in the field of mechanical intelligent fault diagnosis. Combining the advantages of attention mechanism and unsupervised learning, this paper proposes a squeeze-excitation attention guided sparse filtering (SESF) method for mechanical intelligent fault diagnosis method under variable speed. Firstly, the SE attention mechanism is embedded in SF algorithm to guide model training. Then, unsupervised feature extraction is carried out on the variable speed signal samples. The training results are adaptively screened and weighted to make the model pay more attention to the region with the most classify discrimination, so as to improve the feature extraction ability of the model to obtain useful information. Finally, two sets of gear and bearing tests under variable speed condition are adopted to testify the performance of the proposed method. The experimental results show that the SESF method can overcome the influence of variable speed to achieve accurate recognition of different mechanical faults and is superior to the other methods.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"3 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139148744","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 : 2023-12-28DOI: 10.1088/1361-6501/ad1977
Kun Hu, yuanbin Mo
Unmanned aerial vehicle(UAV) path planning plays an important role in UAV flight, and an effective algorithm is needed to realize UAV path planning. The sand cat algorithm is characterized by simple parameter setting and easy implementation. However, the convergence speed is slow, easy to fall into the local optimum. In order to solve these problems, a novel sand cat algorithm incorporating learning behaviors (LSCSO) is proposed. LSCSO is inspired by the life habits and learning ability of sand cats and incorporates a new position update strategy into the basic Sand Cat Optimization Algorithm, which maintains the diversity of the population and improves the convergence ability during the optimization process. Finally, LSCSO is applied to the challenging UAV 3D path planning with cubic B-spline interpolation to generate a smooth path, and the proposed algorithm is compared with a variety of other competing algorithms. The experimental results show that LSCSO has excellent optimization-seeking ability and plans a safe and feasible path with minimal cost consideration among all the compared algorithms.
无人驾驶飞行器（UAV）的路径规划在无人驾驶飞行器的飞行中发挥着重要作用，因此需要一种有效的算法来实现无人驾驶飞行器的路径规划。沙猫算法的特点是参数设置简单，易于实现。但收敛速度较慢，容易陷入局部最优。为了解决这些问题，我们提出了一种包含学习行为的新型沙猫算法（LSCSO）。LSCSO 借鉴了沙猫的生活习性和学习能力，在基本的沙猫优化算法中加入了新的位置更新策略，保持了种群的多样性，提高了优化过程中的收敛能力。最后，将 LSCSO 应用于具有挑战性的无人机三维路径规划，利用三次 B 样条插值生成平滑路径，并将所提出的算法与其他多种竞争算法进行了比较。实验结果表明，LSCSO 具有出色的寻优能力，在所有比较算法中能以最小的成本规划出安全可行的路径。
{"title":"A novel unmanned aerial vehicle path planning approach: Sand Cat Optimization Algorithm Incorporating Learned Behaviour","authors":"Kun Hu, yuanbin Mo","doi":"10.1088/1361-6501/ad1977","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1977","url":null,"abstract":"Unmanned aerial vehicle(UAV) path planning plays an important role in UAV flight, and an effective algorithm is needed to realize UAV path planning. The sand cat algorithm is characterized by simple parameter setting and easy implementation. However, the convergence speed is slow, easy to fall into the local optimum. In order to solve these problems, a novel sand cat algorithm incorporating learning behaviors (LSCSO) is proposed. LSCSO is inspired by the life habits and learning ability of sand cats and incorporates a new position update strategy into the basic Sand Cat Optimization Algorithm, which maintains the diversity of the population and improves the convergence ability during the optimization process. Finally, LSCSO is applied to the challenging UAV 3D path planning with cubic B-spline interpolation to generate a smooth path, and the proposed algorithm is compared with a variety of other competing algorithms. The experimental results show that LSCSO has excellent optimization-seeking ability and plans a safe and feasible path with minimal cost consideration among all the compared algorithms.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"56 10","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139150520","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 : 2023-12-28DOI: 10.1088/1361-6501/ad180d
Xiaoxia Sun, Hui Xiao, Wenjun Meng
The accurate non-contact tension measurement of steel cord conveyor belt, an important load bearing medium, is critical for long distance belt conveyors. It is necessary to establish the relationship between the conveyor belt transverse vibration and the tension, in order to analyse the conveyor belt tension changes through indirect measurement of transverse vibration. The paper analyses the existing models of transverse vibration in conveyor belts, and finds that these models can hardly directly and accurately calculate the tension of the conveyor belt. Therefore, modifications are needed. Firstly, the paper establishes a dynamic model of the belt conveyor and conducts simulation analysis using RecurDyn software. This allows the authors to obtain the belt tension and transverse vibration displacement of the conveyor belt. Fast Fourier transform is employed to determine the vibration frequency, which is used to evaluate the vibration characteristics of conveyor belts under different operating conditions. Then, the paper conducts simulation analysis on the frequency and tension of the belt conveyor with different idler spacing, and performs nonlinear least squares calculation in MATLAB software to modify the coefficients of the transverse vibration model. This process involves nonlinear fitting, resulting in an improved transverse vibration model. Finally, the modified transverse vibration model is compared with the original model. The modified transverse vibration model can more accurately calculate the tension of the conveyor belt based on its vibration frequency. The validity of the modified model is verified by different types of conveyor belts.
{"title":"Research on the tension of steel cord conveyor belts based on transverse vibration modelling","authors":"Xiaoxia Sun, Hui Xiao, Wenjun Meng","doi":"10.1088/1361-6501/ad180d","DOIUrl":"https://doi.org/10.1088/1361-6501/ad180d","url":null,"abstract":"The accurate non-contact tension measurement of steel cord conveyor belt, an important load bearing medium, is critical for long distance belt conveyors. It is necessary to establish the relationship between the conveyor belt transverse vibration and the tension, in order to analyse the conveyor belt tension changes through indirect measurement of transverse vibration. The paper analyses the existing models of transverse vibration in conveyor belts, and finds that these models can hardly directly and accurately calculate the tension of the conveyor belt. Therefore, modifications are needed. Firstly, the paper establishes a dynamic model of the belt conveyor and conducts simulation analysis using RecurDyn software. This allows the authors to obtain the belt tension and transverse vibration displacement of the conveyor belt. Fast Fourier transform is employed to determine the vibration frequency, which is used to evaluate the vibration characteristics of conveyor belts under different operating conditions. Then, the paper conducts simulation analysis on the frequency and tension of the belt conveyor with different idler spacing, and performs nonlinear least squares calculation in MATLAB software to modify the coefficients of the transverse vibration model. This process involves nonlinear fitting, resulting in an improved transverse vibration model. Finally, the modified transverse vibration model is compared with the original model. The modified transverse vibration model can more accurately calculate the tension of the conveyor belt based on its vibration frequency. The validity of the modified model is verified by different types of conveyor belts.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"86 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139151848","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 : 2023-12-28DOI: 10.1088/1361-6501/ad1979
Can Zhou, Can Zhou, Hongqiu Zhu, Tianhao Liu
Barrel distortions often exist in images captured by wide-angle lenses, and the presence of barrel distortions reduces the label-making accuracy of algorithms and the precision rate of final target detection and semantic recognition. To reduce the interference of barrel distortions on imaging, we have proposed a lightweight image rectification network AIR-CNN for barrel distortion. The network is based on a parameter sharing (PS) convolutional neural network structure, which is trained on the distorted image dataset to predict the pixel displacement field between the distorted image and the rectified image, and finally restores the rectified image based on the predicted pixel displacement field. The experimental results show that the AIR-CNN can greatly reduce the number of network parameters through the parameter sharing mechanism and implicitly learns the texture features by asymmetric convolution (AC) kernels to obtain higher rectification accuracy at a lower computational cost, and automatically obtain the distortion parameters of the camera without special calibration methods. The AIR-CNN outperforms previous image rectification methods in both intuitive and quantitative comparisons (EPE, PSNR, NRMSE, SSIM).
{"title":"AIR-CNN: A Lightweight Automatic Image Rectification CNN Used for Barrel Distortion","authors":"Can Zhou, Can Zhou, Hongqiu Zhu, Tianhao Liu","doi":"10.1088/1361-6501/ad1979","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1979","url":null,"abstract":"Barrel distortions often exist in images captured by wide-angle lenses, and the presence of barrel distortions reduces the label-making accuracy of algorithms and the precision rate of final target detection and semantic recognition. To reduce the interference of barrel distortions on imaging, we have proposed a lightweight image rectification network AIR-CNN for barrel distortion. The network is based on a parameter sharing (PS) convolutional neural network structure, which is trained on the distorted image dataset to predict the pixel displacement field between the distorted image and the rectified image, and finally restores the rectified image based on the predicted pixel displacement field. The experimental results show that the AIR-CNN can greatly reduce the number of network parameters through the parameter sharing mechanism and implicitly learns the texture features by asymmetric convolution (AC) kernels to obtain higher rectification accuracy at a lower computational cost, and automatically obtain the distortion parameters of the camera without special calibration methods. The AIR-CNN outperforms previous image rectification methods in both intuitive and quantitative comparisons (EPE, PSNR, NRMSE, SSIM).","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"19 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139152223","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 multi vision metro tunnel defect sensing system mainly consists of IRT and RGB cameras, which can automatically identify and extract small tunnel lining surface defects, greatly improving detection efficiency. However, the presence of various issues like train vibration, inconsistent lighting, fluctuations in temperature and humidity leads to the images showing inadequate uniformity in illumination, blurriness, and a decrease in the level of detail. The above issues have led to unsatisfactory fusion processing results for multiple visual images and increased missed detection rates. A multi visual images fusion approach for metro tunnel defects based on saliency optimization of pixel level defect image features is proposed. This method first takes the motion state of the train and the blurry image as constraints to eliminate dynamic blurring in the image. Secondly, Image weights are allocated based on the uniformity of visible light image illumination in the tunnel, as well as real-time temperature and humidity. Finally, image feature extraction and fusion are performed by a U-Net network that integrates channel attention mechanisms. The experimental results demonstrate that this approach improves the image pixel value variation rate by 39.7%, enhances the edge quality by 23%, and outperforms similar approach in terms of average gradient, gradient quality, and sum of difference correlation with improvements of 15.9%, 7.3%, and 26.6% respectively.
{"title":"Multi visual images fusion approach for metro tunnel defects based on saliency optimization of pixel level defect image features","authors":"Dongwei Qiu, Zhengkun Zhu, Xingyu Wang, Ke-liang Ding, Zhaowei Wang, Yida Shi, Wenyue Niu, Shanshan Wan","doi":"10.1088/1361-6501/ad197d","DOIUrl":"https://doi.org/10.1088/1361-6501/ad197d","url":null,"abstract":"The multi vision metro tunnel defect sensing system mainly consists of IRT and RGB cameras, which can automatically identify and extract small tunnel lining surface defects, greatly improving detection efficiency. However, the presence of various issues like train vibration, inconsistent lighting, fluctuations in temperature and humidity leads to the images showing inadequate uniformity in illumination, blurriness, and a decrease in the level of detail. The above issues have led to unsatisfactory fusion processing results for multiple visual images and increased missed detection rates. A multi visual images fusion approach for metro tunnel defects based on saliency optimization of pixel level defect image features is proposed. This method first takes the motion state of the train and the blurry image as constraints to eliminate dynamic blurring in the image. Secondly, Image weights are allocated based on the uniformity of visible light image illumination in the tunnel, as well as real-time temperature and humidity. Finally, image feature extraction and fusion are performed by a U-Net network that integrates channel attention mechanisms. The experimental results demonstrate that this approach improves the image pixel value variation rate by 39.7%, enhances the edge quality by 23%, and outperforms similar approach in terms of average gradient, gradient quality, and sum of difference correlation with improvements of 15.9%, 7.3%, and 26.6% respectively.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"102 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139149427","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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