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Research on a synchronised classification method for loose particle detection signals of aerospace-sealed electronic components Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-27 Pengfei Li, Guofu Zhai, Guotao Wang, Zhigang Sun, Qiang Wang, Leizhen Gao
Aerospace-sealed electronic components exhibit favourable anti-interference capability and high reliability and are widely utilised in satellites, rockets, and missiles. Loose particle detection is crucial to ensure high reliability. However, the classification problem of loose particle detection signals based on the particle impact noise detection (PIND) method has been a challenge for the high reliability
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A review: the application of generative adversarial network for mechanical fault diagnosis Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-27 Weiqing Liao, Ke Yang, Wenlong Fu, Chao Tan, Baojia Chen, Yahui Shan
Mechanical fault diagnosis is crucial for ensuring the normal operation of mechanical equipment. With the rapid development of deep learning technology, the methods based on big data-driven provide a new perspective for the fault diagnosis of machinery. However, mechanical equipment operates in the normal condition most of the time, resulting in the collected data being imbalanced, which affects the
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Dynamic error modeling and analysis of articulated arm coordinate measuring machine with integrated joint module Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-27 Mei Shen, Hongtao Yang, Di Chang, Xixiang Jiang, Yi Hu
The self-driven articulated arm coordinate measuring machine (AACMM) is a new non-orthogonal flexible coordinate measuring equipment providing automatic positioning and measurement with integrated joint modules introduced to its rotary joints. The dynamic measuring accuracy is the crucial indicator of the self-driven AACMM performance. However, the part manufacturing assembly error and structural dynamic
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PPP-AR reference satellite selection based on the observation quality factors Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-27 Mengyuan Li, Guanwen Huang, Le Wang, Wei Xie
Precise point positioning ambiguity resolution (PPP-AR) can effectively improve positioning accuracy and convergence time. In PPP-AR, the double-difference ambiguity between satellite pairs must be fixed. Therefore, it requires the selection of one satellite as a reference to conduct single-difference observations. Usually, the satellite with the highest elevation is selected as the reference satellite
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A digital twin library of mechanical transmission system for the application of small sample fault diagnosis problem Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-27 Xianglong Meng, Tianliang Hu, Jinfeng Li, Yan Zhang, Songhua Ma
Timely and accurate fault diagnosis of transmission systems is crucial to ensuring the systems’ reliability, safety, and economic viability. However, intelligent fault diagnosis algorithms require a lot of labeled data for training, which may not be available and accessible, especially for many critical devices. This hinders the application of some excellent diagnosis methods in real industry. Digital
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Investigation on the influence of temperature-variation clearance on the frequency of rolling bearing defects Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-27 Wenjie An, Yanling Gu, Changzheng Chen, Hao Zheng, Miao Tian
The clearance between the inner ring (IR) of rolling bearing and rotor is directly proportional to the temperature rise. The increased friction between the IR and rotor, as well as changes in the frequency of rolling bearing defects, and the accuracy of bearing health monitoring decreases. Existing research cannot effectively solve this problem. In response to the above issues, in this paper, the clearance
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The pioneer of intelligent and sustainable construction in tunnel shotcrete applications: a comprehensive experimental and numerical study on a self-sensing and self-heating green cement-based composite Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-27 Mohammadmahdi Abedi, Federico Gulisano, Baoguo Han, Raul Fangueiro, António Gomes Correia
In this study, a self-sensing and self-heating natural fibre-reinforced cementitious composite for the shotcrete technique was developed using Kenaf fibres. For this purpose, a series of Kenaf fibre concentrations were subjected to initial chemical treatment, followed by integration into the cement-based composite containing hybrid carbon nanotubes (CNT) and graphene nanoplatelets (GNP). The investigation
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A novel gearbox local fault feature extraction method based on quality coefficient and dictionary learning Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-27 Zhongze Liu, Huibin Lin, Li Ding, Jipu Li, Bin Zhang, Fei Jiang, Zhuyun Chen
The performance of sparse decomposition is directly determined by the similarity between impact atoms and the actual fault impact waveform. The shift-invariant K-singular value decomposition (K-SVD) dictionary learning algorithm is capable of training impact patterns from vibration signals collected by sensors to construct impact atoms, thereby extracting fault impact components from the vibration
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Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-27 Jingyu Li, Lei Wang, Liying Sun, Xin Zou
The appearance of unmanned aerial vehicle photogrammetry and airborne lidar makes it possible to obtain measurement data for complex terrains such as gullies and mountainous regions. However, extracting ground points from these abundant and massive measurement datasets is challenging. In traditional extractions, their essence is to determine the surfaces that can describe the terrain from the seed
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Novel baseline-free ultrasonic Lamb wave defect location method based on path amplitude matching Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-27 Qinfei Li, Zhi Luo, Shaoping Zhou
Ultrasonic Lamb wave detection technology constitutes a non-destructive evaluation approach extensively employed for the identification of flaws within plate-like structures. The conventional method for detecting and localizing defects in isotropic plate-like structures using ultrasonic Lamb waves relies on baseline signal data. However, the reliability of baseline data as a reference value is diminished
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Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-26 Zhijie Lu, Xiaoan Yan, Zhiliang Wang, Yuyan Zhang, Jianjun Sun, Chenbo Ma
The intricate nature of compound fault diagnosis in rolling bearings during nonstationary operations poses a challenge. To address this, a novel technique combines adaptive variational mode decomposition (AVMD) with improved multipoint optimal minimum entropy deconvolution adjustment (IMOMEDA). The compound fault signal is isolated through AVMD, with internal parameters obtained via a new indicator
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MSCS-ICP: point cloud registration method using multi-view spatial coordinate system–ICP Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-26 Limei Song, Jipeng Zhang, Jing Luo, Yuxiaolong Zhao, Zhi Qiao
The effectiveness of point cloud registration critically determines three-dimensional (3D) reconstruction accuracy involving multi-view sensors. We introduce a multi-view point cloud registration method based on multi-view spatial coordinate system–ICP to solve the problem of 3D point cloud registration from different viewpoints. By integrating a spatial rotation axis line, our method successfully
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Crack identification method for magnetic particle inspection of bearing rings based on improved Yolov5 Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-26 Yun Yang, Jinzhao Zuo, Long Li, Xianghai Wang, Zijian Yin, Xingyun Ding
The fluorescent magnetic particle inspection technique is often used for surface crack detection of bearing rings due to its advantages of simple operation and high sensitivity. With the development of computer vision technology, more and more visual algorithms are used in magnetic particle inspection for defect detection. However, most of these current algorithm models have low detection accuracy
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Method of inclined hole axis adjustment based on geometric optimization and minimum zone theory Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-26 He Zhang, Shixiong Yan, Li Wang, Chenghui Sun, Jiwen Cui
The machining accuracy of the axis position and spatial angle of the array microholes with high aspect ratio on the components of the high-end equipment manufacturing industry will directly affect the performance of the whole machine. A method of tilt hole axis adjustment is proposed in this paper. It utilizes geometric optimization and minimum zone theory to measure the position and spatial angle
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An adaptive robust strategy based on hypothesis testing for satellite clock bias short-term forecasting Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-26 Jia Su, Mengjia Gao, Qingwu Yi, Binbin Wang, Zhiwei Ma
The study of short-term forecasting of satellite clock bias (SCB) is important to promote the development of real-time precise point positioning, and the Kalman filter model has certain advantages over other models in the single forecast model. However, the filtering performance will be degraded by the model bias, and a fading factor is constructed to control the effect brought by the model bias. Considering
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A method to build feature descriptor for GNSS spoofing detection by carrier phase double difference measurement Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-26 Thuan Nguyen Dinh, Truong Trinh Huu, Hien Nguyen Van, Vinh La The, Hung Pham Ngoc, Tung Ta Hai, Hiep Hoang-Van
GNSS spoofing is a type of attack that aims to deceive GNSS receivers by transmitting fake signals that imitate the authentic ones. To detect such attacks, a possible solution is to calculate the double difference (DD) of carrier phase measurements between two antennas of two separated receivers. This DD measurement represents the angle of arrival (AoA) information of the signal. In the case of authentic
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A small sample bearing fault diagnosis method based on novel Zernike moment feature attention convolutional neural network Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-26 Yunji Zhao, Jun Xu
Bearings are one of the core components of rotating machine machinery. Monitoring their health status can ensure the safe and stable operation of rotating machine equipment. The limited nature of bearing fault samples makes it difficult to meet the demand for sufficient samples based on deep learning methods. Therefore, how to solve the problem of small- samples is the key to achieving intelligent
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A novel robust moving horizon estimator for discrete-time linear systems subject to measurement outliers Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-26 Zhongxin Wang, Zhilin Liu, Shouzheng Yuan, Yingkai Ma, Simeng Song
State estimation is a crucial problem in modern industries and has been widely applied across various fields. The performance of the estimator depends on the quality of the measurement data. Measurements being corrupted by outliers is becoming an unavoidable phenomenon that leads to degradation of estimator performance. It is critical to develop estimators with outlier suppression capabilities to mitigate
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Dynamic rail wear measurement: integration of RTK GNSS, IMU, and laser Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-26 Yongjiang Li, Qile Zhao, Shengxiang Huang, Jingnan Liu
Currently, laser sensors are widely utilized in railway systems for monitoring rail wear. However, the lack or insufficiency of rail waist data poses a challenge to accurately match profiles and calculate wear. In this paper, we propose a novel approach for dynamic rail wear monitoring that comprises three major modules: a filtering methodology to smooth rail profile data, fine compensation to calibrate
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An online color and shape integrated detection method for flexible packaging surface defects Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-26 Yi Sun, Jiahong Wei, Jinhua Li, Qin Wei, Weiwei Ye
It is difficult for the spectrophotometer to meet the requirement of real-time color defect detection for flexible packaging prints. The false of shape defect detection is caused by artifact interference and insufficient classification accuracy of defect classification network. A color defect detection method for flexible packaging is proposed, which realizes the adaptive adjustment of the correction
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GNSS-R snow depth retrieval algorithm based on PSO-LSTM Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-26 Yuan Hu, Wei Qu, Wei Liu, Xintai Yuan
The global navigation satellite system (GNSS)-interferometric reflectometry technique has been applied to retrieve snow depth, which has a high potential for application. The GNSS reflectometry classical algorithm retrieves the snow depth by extracting the frequency of the multipath signal and substituting it into an empirical formula. However, the retrieval errors of high and low snow depths are large
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The optimal algorithm for eliminating nonlinear error in phase measurement profilometry based on global statistical phase feature function Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-25 Zhenmin Zhu, Xiaokai Xu, Wenqing Long, Lifa He, Jing Zhang, Haoran Liu, Jianru Jiang
In a digital fringe projection structured light system, the nonlinear phase error is generated by the gamma effect of both the projector, camera, and other electronic devices. One of the existing nonlinear correction methods is active correction by projecting ideal fringes as far as possible, and the other is passive compensation after capturing aberrant fringes. The former has higher accuracy but
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Multiple species imaging from CFD fused H2O absorption spectral tomography and transfer learning Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-25 Jinting Wen, Zhang Cao, Xiaoqian Zhang, Lijun Xu
Laser absorption spectroscopy (LAS) tomography is well-proved in combustion diagnosis but has difficulty especially in the simultaneous imaging of multi-species concentrations. A multiple species imaging method from single species LAS tomography was proposed on the basis of computational fluid dynamics (CFDs) and transfer learning. CFD simulation of the methane/air flat flame was conducted to reveal
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An omnidirectional spatial monocular visual localization and tracking method for indoor unmanned aerial vehicles based on the two-axis rotary table Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-25 Jun Wu, Haoshuang Wang, Tengfei Shan, Runxia Guo, Jiusheng Chen
Aiming at the complexity and poor adaptability of the calibration process in the traditional unmanned aerial vehicles (UAV) indoor visual positioning, this paper proposes an omnidirectional spatial tracking and localization method for indoor UAV based on the two-axis rotary table. Firstly, the position of the UAV fuselage feature points in the camera coordinate system of the turntable camera is computed
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ODDformer: odd–even de-stationary and decomposition techniques transformer for aircraft engine remaining useful life prediction Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-22 Shuang Yi, Xiaodong Han, Binbin Liang, Guoxin Huang, Wei Li
In the aerospace industry, accurately predicting the remaining useful life (RUL) of aircraft engines is critical to reduce maintenance costs and increase safety. Existing RUL prediction algorithms fail to account for global temporal factors, overlook the non-stationary nature of monitored data, and neglect critical trends and seasonal characteristics. These factors directly affect the sensitivity of
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A high-sensitive anisotropic magnetoresistive sensor based on hybrid Ta/NiFe/Ta/Al multilayer structure Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-22 Jiayue Zhuo, Peiyuan Liu, Yongjian Feng, Jianhuan Zhang, Chentao Zhang
High sensitivity is crucial for anisotropic magnetoresistive (AMR) sensors in industrial applications. In this paper, a high- sensitive AMR sensor based on magnetoresistive thin films with Ta/NiFe/Ta/Al four-layer structure is proposed and fabricated. Firstly, the structural parameters were optimized by finite element analysis. Secondly, thin film samples and AMR sensors were prepared. Through the
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An adaptive anisotropic bilateral filtering method for mesh data in scale space Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-22 Ling-Jie Su, Wen-Long Li, Yu-Qi Cheng, Dong-Fang Wang, Cheng Jiang, Wen-Tao Yang, Hai-Wen Zhang, Wei Xu
Three-dimensional mesh data of parts, such as blades and engine bodies, have been widely used in industrial fields. Due to the different kinds of noise during mesh acquisition and the machining deficiency of parts, the mesh quality tends to be insufficient for subsequent operations. Therefore, mesh denoising is a necessary and critical procedure to improve mesh quality. Existing methods commonly apply
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Power transformer fault diagnosis using dynamic multiscale graph modeling and M2SGCN network based on statistical fusion Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-22 Xiaoyan Liu, Yigang He
Power equipment fault diagnostics hold significant importance for the stability of power grid systems. In pursuit of this objective, this paper proposes a fault diagnosis method that utilizes dynamic multiscale graph (DMG) modeling and the multiscale multi-stream GCN(M2SGCN) network, incorporating statistical fusion. Specifically, a novel DMG modeling method is proposed to derive visibility graph data
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Particle image based simultaneous velocity and particle concentration measurement Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-21 Abhilash Sankaran, Rainer Hain, Christian J Kähler
The aim of this study is the expansion of the application of particle image velocimetry (PIV) to include the determination of particle concentration within the visualized area, in addition to velocity analysis. The assessment of particle concentration is valuable in various lab-scale experiments involving particle dispersion. Additionally, it plays a crucial role in evaluating the quality of PIV images
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A new LSTNet-based temperature prediction model for permanent magnet Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-21 Fuyan Guo, Jiao Chen, Yue Wang, Qi Cui, Weijiang Fu
Permanent magnet synchronous motors (PMSMs) can effectively protect against demagnetization by accurate permanent magnet (PM) temperature prediction; nevertheless, due to the nonlinear properties and intricate internal structure of PMSMs, accurate PM temperature prediction methods still encounter difficulties. This paper proposes a new PM temperature prediction model (LSTNet-Improved) based on long-
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Vision transformer-based electronic nose for enhanced mixed gases classification Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-21 Haiying Du, Jie Shen, Jing Wang, Qingyu Li, Long Zhao, Wanmin He, Xianrong Li
The classification of mixed gases is one of the major functions of the electronic nose. To address the challenges associated with complex feature construction and inadequate feature extraction in gas classification, we propose a classification model for gas mixtures based on the vision transformer (ViT). The whole-process signals of the sensor array are taken as input signals in the proposed classification
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Domain-alignment multitask learning network for partial discharge condition assessment with digital twin in gas-insulated switchgear Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-21 Jing Yan, Yanxin Wang, Wenjie Zhang, Jianhua Wang, Yingsan Geng, Dipti Srinivasan
Deep-learning-driven methods have made great progress in the condition assessment of partial discharge (PD) which including diagnosis and location in gas-insulated switchgear (GIS). However, these methods perform diagnosis and location as two separate tasks and ignore the coupling relationship. In addition, these methods all require obtaining sufficient samples to develop models, and the model becomes
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Gaussian process regression based inspection robot for predicting and locating pipeline anticorrosion coating defects Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-21 Shunxin Tian, Zhenning Wu, Dexin Zhang
The direct current voltage gradient (DCVG) technology is adept at identifying defects and corrosion issues within the anti-corrosion layer of buried pipelines by measuring changes in voltage gradient above the ground. Its widespread adoption in the field of anti-corrosion layer defect detection for its high precision and accuracy. However, the current DCVG inspection process relies on experienced operators
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Speckle noise reduction on aligned consecutive ultrasound frames via deep neural network Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-21 Mahsa Mikaeili, Hasan Şakir Bilge, İsa Kılıçaslan
Despite the benefits of ultrasound (US) imaging systems for medical diagnosis and treatment, US images are prone to low resolution and contrast due to US’s inherent attributes, as well as affected by speckle noise that directly influences their quality. In retrospective studies, diverse filters have been applied to minimize the effects of speckle noise and enhance the quality of US images. In this
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Damped least squares method for nonlinear mixed additive and multiplicative errors model Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-20 Leyang Wang, Weifeng Zhao
Measurement data in the field of modern geodesy contains not only additive errors but also multiplicative errors related to signal strength. The existing models for dealing with mixed additive and multiplicative errors are mainly based on the linear form of unknown parameters and observations, and there are few studies on the nonlinear form of the two. In the parameter estimation method of the nonlinear
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A dual-weighted adversarial network for partial domain fault diagnosis of machinery Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-20 Xue Ding, Aidong Deng, Minqiang Deng, Yaowei Shi, Konstantinos Gryllias
Domain adaptation provides a promising approach to cross-domain fault diagnosis of rotating machinery. While many current methods focus on scenarios where the source and target domains share identical label spaces, a prevalent situation in industrial production involves the target domain being a subset of the source domain, known as partial domain adaptation (PDA). The main challenge in PDA is the
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Features extraction of point clouds based on Otsu’s algorithm Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-20 Yibo He, Zhenqi Hu, Rui Wang, Haizhong Zhu, Guilin Fu
Currently, a point cloud extraction method based on geometric features requires the configuration of two essential parameters: the neighborhood radius within the point cloud and the criterion for feature threshold selection. This article addresses the issue of manual selection of feature thresholds and proposes a feature extraction method for 3D point clouds based on the Otsu algorithm. Firstly, the
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Multicomponent collaborative time-frequency state-space model for vibration signal decomposition under nonstationary conditions Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-20 Xin Huang, Wenwu Chen, Dingrong Qu, Xiaojin Liu, Huajin Shao
The implementation of prognostics and health management strategies is essential for enhancing the safety and maintenance of rotating equipment in chemical plants. The examination of vibration signal behaviours under variable-speed conditions and the development of signal decomposition methods in such contexts are of substantial theoretical and practical relevance. This paper proposes a novel multicomponent
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Matching strategy and skip-scale head configuration guideline based traffic object detection Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-20 Yi Shi, Xin Zhang, Changyong Xie, Jiawen Lu, Lvfan Yuan, Hongmei Yan, Yong Liu, Shulin Liu
The configuration of the detection head has a significant impact on detection performance. However, when the input resolution or detection scene changes, there is not a clear method for quantitatively and efficiently configuring the detection head. We find that there is a rule of matching degrees between the object scale and the detection head across different input resolutions or detection scenes
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ASG-HOMGAT: a high-order multi-head graph attention network with adaptive small graph structure for rolling bearing fault diagnosis Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-20 Shanshan Ding, Renwen Chen, Hao Liu, Fei Liu, Junyi Zhang
Traditional Euclidean spatial data processing is difficult to capture the inherent relationships of unstructured data such as bearing vibration signals. Representing vibration signals in graphical form helps to preserve their topological structure and temporal information. Secondly, most existing graph convolutional network methods are based on large graph structured data, which incurs certain memory
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ECT image reconstruction based on sensitive field expansion and optimization Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-20 Guoxing Huang, Chao Li, Jingwen Wang, Juntao Sun, Yu Zhang
Electrical capacitance tomography (ECT) is an efficient method for addressing the issue of two-phase flow monitoring. Most current methods result in low image reconstruction accuracy due to soft field issues. This paper propose an ECT image reconstruction method based on sensitive field expansion and optimization, which improve reconstruction efficiency and accuracy. Firstly, a sensitivity field optimization
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Temporal and spatial flow field reconstruction from low-resolution PIV data and pressure probes using physics-informed neural networks Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-20 Bozhen Lai, Yingzheng Liu, Xin Wen
In this paper, we present an innovative approach using physics-informed neural networks to reconstruct high-frequency, full-field flows, including the pressure field, by integrating sparse, noisy, low-temporal-resolution particle image velocimetry (PIV) data with high-temporal-resolution pressure probe data. This method effectively leverages the spatial richness of PIV data and the temporal abundance
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Symmetric circulant matrix decomposition-based multivariable group sparse coding for rolling bearing fault diagnosis Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-20 Xing Yuan, Hui Liu, Fu Yang, Huijie Zhang
Singular value decomposition technique proves its effectiveness in mechanical signal analysis by decomposing the test signal into a series of singular spectral components of different frequency bands. Nevertheless, how to adapt this technology to the needs of cyclo-nonstationary signal and how to set the decomposition number while maintaining detailed features to obtain the optimal component containing
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Few-shot condition diagnosis of rolling bearing using adversarial transfer network with class aggregation-guided Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-19 Shaoning Tian, Dong Zhen, Guohua Sun, Xiaoang Liu, Guojin Feng, Fengshou Gu
For the challenge of fault identification under limited labeled data in engineering applications, a novel adversarial transfer network with class aggregation-guided (ATN-CA) is proposed for few-shot condition diagnosis of bearings. The ATN-CA can focus on the discrepancy features of bearings by the proposed local discrepancy feature representation, which avoids that the features extracted by a single
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Robust transfer subspace learning based on low-rank and sparse representation for bearing fault diagnosis Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-19 Fuchao Yu, Xianchao Xiu, Xinrong Li, Jingjing Liu
With the development of industrial intelligence, data-driven fault diagnosis plays an important role in prognostics and health management. However, there is usually a large amount of unlabeled data from different working conditions, making cross-domain fault diagnosis unstable and inflexible. To deal with this issue, we propose two novel transfer subspace learning methods based on the low-rank sparse
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An improved tracking method for bearing characteristic frequencies in the time-frequency representation of vibration signal Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-18 Bin Chen, Chang Qi, Zexuan Yun, Hongyu Wang
Rolling bearing is one of the most critical components for support and energy conversion in machines. The fault characteristic frequency (FCF) of time–frequency representation has received increasing attention in bearing diagnosis under variable speed conditions. However, FCF-extracted methods have poor adaptability to amplitude attenuation and noise interference due to local distortions or even transitions
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Research on sensing characteristics of microfluidic sensor based on photonic crystal fiber Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-18 Zhan Wang, Shuai Wang, Yanhua Luo, Qi Xue, PengFei Wang, XiaoHong Sun
To address the challenges associated with sample injection into the air hole of photonic crystal fiber (PCF) and collimation, in this paper, we assemble a single-mode photonic crystal single-mode fiber structure sensor chip based on the Mach–Zehnder interference principle using microfluidic chip processing technology. The sensing principle is analyzed mathematically and the sensing characteristics
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Rolling bearing degradation trend prediction based on composite multiscale grey entropy and dynamic particle filter Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-18 Li Cheng, Wensuo Ma, Zuobin Gao
High accuracy prediction of degradation trend provides valuable information in establishing reasonable maintenance decision-making with the goal of improving the maintenance efficiency and avoiding sudden downtime. The extraction of degradation features and the prediction algorithm are the key factors in degradation trend prediction. In this work, based on composite multiscale grey entropy (CMGE) and
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Dynamic calibration method for track geometry measurement system-a case study in China Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-18 Zhi Han, Lei Han, Chunlei Chen, Qiang Han, Guoqing Jing, Zhaoyang Cheng
With the rapid development of railway construction, the mileage of railway detection has increased dramatically, and railway companies have higher requirements for the repeatability and accuracy of track geometric dynamic detection data. Therefore, the track geometry measurement system needs to be calibrated to improve the measurement accuracy to ensure the safety of railway operation. However, the
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A method of applying deep learning based optical flow algorithm to river flow discharge measurement Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-18 Jianping Wang, Xiaopeng Liu, Xin Ouyang, Guo Zhang, Ya Zhang
River flow discharge monitoring is one of the critical tasks performed at hydrological stations. The large-scale particle image velocimetry (LSPIV) method widely used in hydrological stations is often limited by a lack of floating objects and has a high computational complexity. The space-time image velocimetry method is susceptible to noise interference and requires high stability of the flow over
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Small data-driven semantic segmentation of wear debris in ferrography images Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-18 Yinhu Xi, Nan Zhang, Bo Li
The segmentation of wear debris images is a prerequisite for ferrographic analysis, and uncertainties and errors in wear debris segmentation will inevitably affect the subsequent analysis. In this work, a small-data semantic segmentation model of wear debris images is constructed based on HRNetv2 for ferrography images acquired by using an online visual ferrography. A major advantage of the current
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Type A standard uncertainty evaluation in one measurement through uncertainty propagation from voxel values’ distribution for computed tomography metrology Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-18 Dongsheng Ou, Yongshun Xiao, Dini Lan, Yingxin Wang
According to the guide to the expression of uncertainty in measurement, ‘type A evaluation’ generally requires repeated measurements, which are time-consuming for CT scans. To solve this problem, we developed a method for estimating the standard deviation of measurement results in one measurement through uncertainty propagation, which can be regarded as repeatability standard deviation to evaluate
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Measuring gloss using spectral reflectance Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-18 Clarence J Zarobila, Maria E Nadal, C Cameron Miller
Gloss is historically measured at three angles of incidence, 20°, 60°, and 85°, using a light source filtered to replicate Illuminant C and a colored glass filter designed to reproduce the CIE 1931 photopic luminous efficiency function. Herein an alternate method that relies on the spectral reflectance of a gloss standard and the sample, both measured with a new reference gonio-spectrometer and in
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Seepage prediction model of the earth-rock dam based on TCN considering rainfall lag effect Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-15 Manli Qu
Renewable energy has the highest conversion efficiency, is the most flexible in regulating peak power in the grid, and has the potential to significantly reduce emissions. Hydropower is one of the main ways to optimize power energy structure by building earth-rock dams that block water and generate electricity. Seepage is a physical quantity that characterizes the safety of earth-rock dams. Studying
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Measurement of the water-to-liquid ratio of oil–water two-phase flow for low flow rates and high water content Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-15 En Huang, Bing Chen, Luchao Song, Yi Li, Lihui Peng
Oil–water two-phase flow is widely present in the petroleum industry, and the vast majority of oilfields have entered the period of exploitation under high water content. Accurate measurement of water-to-liquid ratio (WLR) is crucial to oil production. Currently, there are fewer works related to the WLR measurement at high water content and low flow rates. In order to get a complete picture of WLR
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A TLOT train gearbox fault diagnosis method based on ridge extraction under variable speed conditions Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-15 Zhongshuo Hu, Qiang Li, Jianwei Yang, Dechen Yao, Jinhai Wang
Owing to the rapidly varying working conditions of urban rail trains, the rotational speed conditions constantly shift in a short time span. As a key component of the running gear, the gearbox generates non-stationary vibration signals, making it challenging to monitor its health status. To address this challenge, a tacholess order tracking method (TLOT) based on ridge extraction method is proposed
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Assessment of an NTP service calibration over a Local Area Network Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-14 Carmen Vélez, Javier Díaz, Alfonso Osuna, Héctor Álvarez-Martínez, Héctor Esteban
Nowadays, timekeeping is an essential component in modern computing. The Network Time Protocol (NTP) is a distributed service based on a hierarchical network protocol used to synchronize computer clocks over a network in an easy and scalable manner. Currently, this protocol is the main mechanism used on the internet to provide a common notion of time to all computers. The Real Instituto y Observatorio
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Acoustic tunnel lining cavity detection using cepstral coefficients with optimized filter bank Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-14 Ting Wu, Xiaobin Cheng, Zhaoli Yan, Jun Yang, Xuesong Chai, Xiaojing Dai
Tunnels are an essential component of modern transportation infrastructure, and their structural health is critical to traffic safety, which can be seriously affected by tunnel lining cavities. In this paper, an acoustic-based detection approach for assessing the integrity of tunnel linings is studied. By tapping the tunnel lining surface, acoustic signals are sampled and analyzed using a novel feature
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An improved waveguide method for accurate complex permittivity measurement of medium/high-loss material Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-14 Weijie Wang, Wei Jiang, Yelei Yao, Jianxun Wang, Guo Liu
In this paper, we propose an improved method for accurately measuring medium/high-loss material permittivity to overcome the air gap problem in the conventional waveguide method. This method improves the sample fixture and has a high tolerance on the wide side air gap, which is a dominant factor affecting the accuracy of the conventional waveguide method. Meanwhile, the proposed method avoids the effects
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Improving the environmental temperature adaptability of an electric temperature measurement subsystem by matching temperature coefficients of substitutable resistors Meas. Sci. Technol. (IF 2.4) Pub Date : 2024-03-14 Lingyun Gu, Houyuan Chen, Chen Ling, Zening Sun, Zhu Li, Yanwei Ding
The electrical temperature measurement subsystem in space gravitational wave detectors requires micro-Kelvin precision in the submillihertz band. However, the low-frequency stability of the measurement circuit, excluding the sensor, is susceptible to environmental temperature fluctuations, closely related to the residual temperature coefficient of the circuit. This paper proposes a method to minimize