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Measurement
Elsevier BV
Measurement

Elsevier BV

0263-2241

Measurement/Journal MeasurementISTPSCIAHCI
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    Fit-for-purpose risks in conformity assessment of a substance or material - A case study of synthetic air

    Pennecchi, Francesca R.Kuselman, IlyaHibbert, D. BrynnSega, Michela...
    10页
    查看更多>>摘要：A technique is described for evaluation of the fit-for-purpose risks in conformity assessment of the chemical composition of a substance or material, based on a multivariate Bayesian approach. The approach takes into account measurement uncertainty, correlation and the mass balance constraint. Two datasets related to synthetic air (provided as electronic supplementary material to this paper) were studied. The first dataset was from an industrial factory producing routinely medicinal synthetic air according to the European Pharmacopoeia. The second dataset was from the National Metrology Institutes which participated in key comparison CCQM-K120 "Carbon dioxide at background and urban level". The fitness for purpose of the preparation of synthetic air was interpreted as total risks of false decisions on the conformity of the air composition to the tolerance limits of the contents of its main components. Calculations of these risks were performed with code written in the R programming environment.
      原文链接:
    • NSTL
    • Elsevier

    An image recognition method for the deformation area of open-pit rock slopes under variable rainfall

    Li, QihangSong, DanqingYuan, CanmingNie, Wen...
    17页
    查看更多>>摘要：Due to human mining action, relatively fragile open-pit mine rock slopes are prone to instability induced by heavy rain. Accurately identifying the information and area of deformation features on rock slopes is a key step for landslide disaster warning and prevention. This study simulates the deformation process of a large and steep rock slope in Dexing City, Jiangxi Province, China, under a variable rainfall event. The test results show that 1) the increased deformation region has positive relationships with increasing pore water pressure and water content values for the open-pit mine rock slope affected by heavy rainfall. 2) The infiltration of variable rainfall on the rock slope softens the weak interlayers and leads to failure of the slope toe first. Due to the action of gravity, the middle and upper parts slide and fail in sequence. In addition, an improved region growing segmentation method (IRGSM) is proposed based on the combination of multipixel seed points and point cloud coordinates for the image recognition of the deformation area of open-pit mine rock slopes. An error comparison with the original region growing segmentation method (ORGSM) shows that the average identification error in the X and Y directions by the method is reduced significantly (6.56% and 5.32% in IRGSM; 13.37% and 11.29% in ORGSM). This method may be applied to identify rock slope deformation in complex scenes with high precision.
      原文链接:
    • NSTL
    • Elsevier

    Fault diagnosis based on SPBO-SDAE and transformer neural network for rotating machinery

    Du, XianjunJia, LiangliangUl Haq, Izaz
    25页
    查看更多>>摘要：Fault diagnosis for rotating machinery requires both high diagnosis accuracy and time efficiency. A rotating machinery fault diagnosis method based on intelligent feature self-extraction and transformer neural network is proposed. Firstly, the proposed method employs the student psychology based optimization (SPBO) algorithm to adaptively select hyper parameters, including the number of hidden layer nodes, sparsity coefficient and input data zeroing ratio, of the denoising auto encoder (DAE) network to determine the optimal structure of the stacked denoising auto encoders (SDAE) network. Secondly, the optimized SPBO-SDAE network is used to extract features from high-dimensional original data layer by layer. On this basis, the weight parameters of self-extracted features of SPBO-SDAE network are optimized through the self-attention mechanism of transformer deep neural network. The target features are retained, and the redundant features are filtered. Finally, in order to further validate the performance of the proposed model in the complex conditions, by adding Gaussian noise to the original data, the diagnosis performance of the proposed method is verified through four open data sets. The simulation results indicate that compared with the existing common shallow learning and deep learning methods, the proposed method has great advantages in generalization performance, fault diagnosis accuracy and time efficiency.
      原文链接:
    • NSTL
    • Elsevier

    Modeling and experimental validation of a quantitative bar-type corrosion measuring probe using piezoelectric stack and electromechanical impedance technique

    Wang, JianjunLi, WeijieLuo, WeiWu, Jianchao...
    10页
    查看更多>>摘要：Corrosion coupon method has been widely used to estimate the corrosion rate in multiple industries. However, in this method, the weights of the coupons are measured periodically, which limit the application for on-line monitoring. In this paper, a novel type of quantitative bar-type corrosion measuring probe using piezoelectric stack and electromechanical impedance (EMI) technique was proposed. The probe consists of a piezoelectric stack and a metal bar. The multilayer models were used to derive the solution of the probe in longitudinal vibration mode. Five probe prototypes with designated probe length were fabricated to simulate uniform corrosion induced mass loss and investigate the EMI response with probe length. The relationship between the corrosion induced probe length loss and the first and second resonant and anti-resonant frequencies were analyzed. The measured results agreed well with the theoretical predictions. In addition, the accelerated corrosion tests were also performed to induce corrosion to the probe in a realistic setup and further validate the efficacy of the proposed method. The present study proved the feasibility of using the proposed bar-type corrosion measuring probe to quantitatively assess the corrosion amount by introducing the longitudinal vibration with piezoelectric stack and EMI.
      原文链接:
    • NSTL
    • Elsevier

    Experimental and numerical model for mechanical properties of concrete containing fly ash: Systematic review

    Fasihihour, NazaninAbad, Javad Mohebbi NajmKarimipour, ArashMohebbi, Mohammad Reza...
    30页
    查看更多>>摘要：Increasing use and need for cement have forced researchers to build alternative building materials that are environmentally friendly in nature and help manage less waste. Using waste materials to produce the structural concrete needs theoretical models for implement application for construction. Fly ash as a byproduct of coal-fired electric generating plants showed many advantages in improving the properties of concrete due to its pozzolanic feature. Therefore, this study intends to review previous studies and propose new models to determine the elastic moduli, compressive and tensile strengths of concrete produced by fly ash as a replacement of cement. For this aim, wide-range experimental results were evaluated and provided from previous studies. The disadvantage of the previous study is not covering all elastic moduli, compressive and tensile strengths together. Therefore, a total of 263 concrete mixtures were also produced in this study and the gap of the previous investigations was filled. Therefore, highly accurate machine learning models were used in MATLAB software to predict the mechanical properties of concrete produced by fly ash including radial basis function, multilayer perceptron, support vector regression, adaptive-network-based fuzzy inference system and deep neural network. Additionally, the experimental results were compared with existing models and new highly accurate models were developed to determine the modulus of elasticity, compressive and tensile strengths of concrete produced by various fly ash contents. Experimental results showed that the optimal content for fly ash was obtained by 10% in terms of the maximum mechanical properties of concrete. In addition, used Artificial Neural Networks, particularly deep neural networks showed a highly accurate prediction. Moreover, the proposed models by a high agreement with experimental results (R-2 > 0.98) could be used as highly efficient and accurate tools to determine the mechanical properties of fly ash concrete.
      原文链接:
    • NSTL
    • Elsevier

    Quantitative analysis method for measuring dead zone of non-orthogonal shafting structure

    Zhang, ZhenKang, JiehuSun, ZefengWu, Bin...
    7页
    查看更多>>摘要：Non-orthogonal shafting structure composed of rotary tables and collimated laser or camera has emerged as a cost-effective measurement technique. However, the non-orthogonal shafting structure leads to measuring dead zone. This paper presents a quantitative analysis method to calculate the measuring dead zone. The laser axis is rotated 360 degrees around horizontal axis and the movement trajectory is an uniparted hyperboloid. The uniparted hyperboloid is rotated 360 degrees around vertical axis to generate the measuring dead zone. Based on the relative position relationship of the three axes, the position information of measuring dead zone is calculated. The simulation and experimental results demonstrate that the proposed method is highly-accurate and practical for determining the measuring dead zone of non-orthogonal shafting structure.
      原文链接:
    • NSTL
    • Elsevier

    Enabling a low-resistance high-accuracy flowmeter for the diagnosis of chronic obstructive pulmonary disease

    Li, YueqiQiu, XinXia, PanZhao, Rongjian...
    10页
    查看更多>>摘要：We aimed to develop a low-resistance and high-accuracy way to measure expiratory volume for the accurate classification of chronic obstructive pulmonary disease (COPD). In this paper, using computer-aided design (CAD), a ball-blocking differential pressure flowmeter (BBDPF) has been developed and then fabricated using 3D printing. Ball blocking is used for the designed flowmeter to replace the traditional restriction of the differential pressure flowmeter for lower flow resistance, and special pressure tapping is selected for high accuracy. The BBDPF is theoretically and experimentally characterized, using ANSYS fluent (R) software with turbulent model simulations. Then, we validate the flowmeter, using pulmonary waveforms generator with flow resistance tests and the standard spirometry tests (ATS24/26). The results demonstrate that in comparison with other type differential pressure flowmeters, the structure of BBDPF effectively reduces flow resistance (144.41 Pa/L/s at 14 L/s) with accuracy(+/- 3% of reading or +/- 0.050 L,whichever is greater) in the range of 0 - 17L/s with a resolution of 0.01 L/s. This is confirmed by the application in expiratory volume measurement of the reported work functions well.
      原文链接:
    • NSTL
    • Elsevier

    Predicting rock displacement in underground mines using improved machine learning-based models

    Bui, Xuan-NamPradhan, BiswajeetLi, NingHoang Nguyen...
    18页
    查看更多>>摘要：Displacement of rock mass in tunnels and underground mines is considered one of the most hazardous phenomena that can cause the collapse of the structures. In this study, the rock properties, such as the depth of the tunnels (H), the angle of rock layers (alpha), anti-bending moment (Wc), the width of the tunnels (b), the tensile strength of rock layers (Rn), and monitoring distance (Lb), and observation time (t), were investigated to predict rock displacement in tunnels and underground mines. Two novel soft computing models, namely Harris Hawks optimization algorithm (HHOA)-based support vector machine (SVM) model (i.e., HHOA-SVM) and Grasshopper optimization algorithm (GOA)-based SVM model (i.e., GOA-SVM), were developed for this aim based on the field measurements. A total of 12 measurement stations and 63 observations of vertical rock mass displacement, rock properties, and observation time in some underground coal mines in the Donbas region (Ukraine) were compiled as the dataset for developing soft computing models. In addition, a constraint was also added to the proposed HHOA-SVM and GOA-SVM models to prevent the model from offering negative results in predicting rock displacement. The conventional models, such as SVM (without optimization) and artificial neural network (ANN), were also investigated to compare favorably with the two proposed HHOA-SVM and GOA-SVM models. Furthermore, linear and nonlinear equations were also established to predict rock displacement and compared to the soft computing models. The results showed that the novel HHOA-SVM and GOA-SVM models provided better performances than conventional SVM and ANN models. Besides, the sensitivity of the input variables was also analyzed to discover the certain characteristics of the rock displacement phenomenon through the properties of rock and observation time. The findings show that H, Lb, t, and alpha are the most influential parameters for predicting rock displacement in tunnels and underground mines. In contrast, the contribution of b in rock displacement is tiny, and Wc did not relate to the rock displacement in tunnels and underground mines.
      原文链接:
    • NSTL
    • Elsevier

    Predicting the nutrition deficiency of fresh pear leaves with a miniature near-infrared spectrometer in the laboratory

    Jin, XiuWang, LianglongZheng, WenjuanZhang, XiaoDan...
    12页
    查看更多>>摘要：Nutrient deficiencies often occur during the growth of pear trees; therefore, rapid, cost-effective monitoring of the nutritional deficiency status of pear leaves is of great value for effective cultivation management. The nitrogen, phosphorus and potassium contents of nutrient-deficient pear leaf samples were analysed with a handheld miniature near-infrared (NIR) spectrometer operating at a reflectance spectrum of 900-1700 nm. Combined with different pre-treatment and feature extraction methods, 42 recognition models were established by random forest (RF), support vector machine (SVM), gradient boosting decision tree (GBDT) and extreme gradient boosting (XGBoost). Finally, the best accuracy and F1-score of the SVM with the testing dataset, with standard normal variate (SNV) pre-processing and genetic algorithm (GA) feature extraction, were 82.06% and 80.25%, respectively. The proposed method using a miniature NIR spectrometer can quickly predict the nutrient deficiency status of pear leaves during the cultivation period.
      原文链接:
    • NSTL
    • Elsevier

    A review of Air-Core coil sensors in surface geophysical exploration

    Lin, TingtingZhou, KunCao, YimingWan, Ling...
    16页
    查看更多>>摘要：In the field of surface geophysical detection, electromagnetic technologies represented by transient electromagnetic methods (TEMs) and surface nuclear magnetic resonance (SNMR) use air-core coil sensors as magnetic detection devices because of their high sensitivity, simple fabrication, robustness, and low cost. To clarify the application of air-core coil sensors in these fields, we review the design process and research directions of these devices. First, we introduce the detection principles and parameters of induction coils. Second, we summarize the structure, equivalent model, amplitude-frequency characteristics, and noise level of air-core coil sensors. Third, the characteristics of the air-core coil sensors used in TEMs and SNMR are introduced respectively, and the optimization of parameters such as the noise level, sensitivity, and size of these magnetic detection devices according to actual application conditions is then discussed in detail. Finally, we propose directions for future research based on existing problems in the use of air-core coil sensors, and provide ideas for their further development in geophysical detection.
      原文链接:
    • NSTL
    • Elsevier