查看更多>>摘要：Pavement texture evaluation is important for driving both skid resistance and pavement maintenance. Limited by the requirements of automation, efficiency and data coverage requirements, most pavement methods focus on damaged areas and static measurement environment. However, maintenance work is practically performed on the entire pavement rather than only the damaged areas, thus a gap between theory and practice is observed. In this study, we designed an efficient texture decomposition method based on the proposed step signal extraction algorithm, which can overcome road fluctuations and accurately extract pavement texture. The Pavement Transformer is introduced for fine texture evaluation, and can better serve pavement maintenance in practice. We conducted experiments on 22,800 pieces of 3D laser scanning data. The results demonstrate that our decomposition method has improved accuracy and stability. Moreover, the classification accuracy of texture level evaluation is 95.2%, which is better than that of the Vision Transformer.
查看更多>>摘要：In recent years, optical fiber sensors based on helical fibers have attracted more and more interest and have developed into various sensors used in many practical applications. This review focuses on introducing the sensing principles, measurement methods, and sensing characteristics of different kinds of helical optical fiber sensors. These Sensors can be classified into three categories from the perspective of their sensing principles, namely, helical fiber sensors based on mode coupling, interference, and circular birefringence. Finally, we compared the research results with outstanding sensing characteristics in recent years and we pointed out the future development direction of helical structure optical fiber sensors. This work shows that the helical structure has a wide range of applications in fiber gratings, all-fiber interference devices, and all-fiber polarization control devices, and it will contribute great value to scientific research and industrial applications.
查看更多>>摘要：This paper proposes a fault diagnosis method for multi-sensor vibration signals under few computing resources, called multi-level feature fusion convolution neural network based on multi-layer pooling classifiers (MLPCCNN). First, MLPC-CNN introduces the single-sensor-to-single-channel (STSSC) convolution to comprehensively extract features from multi-sensor data grayscale image that integrates all sensor information. This design can adopt more targeted filtering strategies for the samples from different sensors, and avoid the risk of extracting conflicting evidence. Second, MLPC-CNN uses a bypass branch structure based on average pooling layer. This design fuses different levels of signal features extracted by different layers without increasing the network learning parameters, which can extract high-level features while retaining more information from lowdimensional features. Third, MLPC-CNN introduces a multi-layer pooling classifier to replace the fully connected layer in traditional CNN. The pooling layers with different scales are used to achieve multiple functions, which greatly reduces the number of network parameters and the risk of overfitting. The measured data collected by the fault simulation test stand and the bearing fault dataset produced by case western reserve university are used to verify the performance of MLPC-CNN. Experimental results show that MLPC-CNN has reached 100% accuracy on both two datasets. In addition, to explore the fault diagnosis mechanism of MLPC-CNN, this paper uses multiple visualization methods to analyze the function of the convolution kernel in the STSSC convolution layer, the maximum activation feature signal of different convolution channels, and the evolution process of features generated from different fault samples.
查看更多>>摘要：Vehicle load is the main variable load of highway bridges, which significantly affects the security and duration of bridges. Highly precise vehicle load models are the basis for studying vehicular load responses and providing reliability assessments of highway bridges, whose core is the spatio-temporal distribution of vehicular load on a bridge deck. Vehicle type, axle weight, axle distance and position are the main parameters of spatio-temporal simulation of traffic flow. Weigh-in-motion (WIM) systems can obtain these original vehicle parameters efficiently. However, there are two main problems about how to accurately simulate the above traffic flow parameters based on WIM data: (1) A large number of WIM systems have low accuracy for vehicle classification and an efficient classification method for vehicles from massive WIM data has not been proposed; (2) The most popular MC simulation method ignored the statistical dependence of parameters (vehicle sequence, axle weight, axle distance, etc.), which results in the deviation between traffic flow simulation samples and measured data. In this study, to obtain traffic flow simulation results closer to measured data, a multi-lane traffic flow simulation method via fusion of efficient vehicle classification and the statistical dependence of parameter was proposed. First, the overall framework of the traffic simulation was introduced. Subsequently, the vehicle classification process and traffic flow simulation were introduced respectively. For the vehicle classification, the abnormal vehicles were removed based on the box diagram method, and then the vehicle classification was completed based on the improved K-means++ clustering algorithm. The vehicle sequence and the axle weight/distance samples were obtained by MCMC simulation method and Copula function theory respectively. Finally, a case study was presented using WIM measured data of an 8-lane expressways. The results showed that the optimal number of clusters determined based on the K-means++ clustering algorithm was reasonable. The vehicle simulation samples considering the statistical dependence of parameter were closer to the measured data than previous methods and the normal copula function was slightly better than the t-copula function.
查看更多>>摘要：In the area of measuring the environmental noise the equivalent sound pressure level LA,eq is adopted and compared with legal thresholds in order to characterize the site of interest. The paper describes an innovative Sound Level Meter (SLM) able to provide information about the measurand contribution to the measurement uncertainty estimation. This measurement technology is made possible thanks to an approach based on bootstrap method for selecting the suitable measurement episode for an estimation of LAeq. The firmware implementation of the developed SLM is disclosed with reference to a low-cost platform for real-time execution of the proposed methodology. Finally, a metrological characterization of the prototype performed in laboratory is reported as well as the performance comparison with a class 1 SLM in a real scenario. As a result, the smart features of the new SLM may be easily implemented by including commercial devices into the instrument schematics.
查看更多>>摘要：The monitoring of back bead penetration has always been an important topic in welding field, the vision inspection and arc sound recognition are main methods to monitor welding penetration state. In this paper, an innovative method based on temperature sensing and deep learning is proposed to monitor weld bead penetration. Firstly, it is verified that the distribution of welding temperature field is separable under various welding penetration states. Secondly, a region of interest (ROI) in the welding heat affected zone near molten pool is identified for temperature field detection. Taking the temperature field image of ROI as the input, the prediction model for weld bead penetration is built on the basis of deep residual network. Finally, the generalization performance of the proposed method is verified. The experimental results indicate that detection precision of the model for weld bead penetration is higher than 99%, and the generalization performance is strong.
查看更多>>摘要：The electro-mechanical impedance technique (EMI) is one of the best methods for continuous structural health monitoring (SHM) by embedding a smart piezoelectric ceramic Lead Zirconate Titanate (PZT) sensor or pasting it over the surface of the structure. The smart PZT transducer is intensely affected by the adhesive bonding con-dition between the PZT patch and the host structure. The current paper studies the damage-diagnosis process of the coupled electro-mechanical behavior of an adhesively bonded smart PZT transducer. The inverse of impedance signatures is used for investigating pre-damage diagnosis and analysis of surface-mounted PZT transducer. A parametric study on the variation of adhesive property is carried out to overcome the shear lag effect. The novel contribution of this paper discusses a methodology for an early diagnosis of damage in the PZT transducer and adhesive bond layer during the SHM process. Firstly, the damage is introduced to the PZT patch and bonding layer, and the corresponding EMI signatures are obtained using numerical modeling and simula-tions. The damage detection using susceptance is discussed for lower frequencies from 0 to 20 kHz. It is observed that the change in susceptance signature is not large enough to detect adhesive debonding. Subsequently, the conductance signatures are utilized for the investigation. It is observed that damage in the adhesive layer and PZT patch cause upward or downward shift with no alteration along the horizontal direction. It helps to detect sensor breakage and adhesive debonding effectively. Secondly, the simulation results are verified using the theoretical analysis for the single piezo configuration by improved continuum-based impedance equations. A modified dual piezo configuration is investigated to validate the proposed method. Experimental investigation on PZT-bonded aluminum plate involving perfectly bonded, adhesive debonding, and sensor breakage conditions are conducted to verify the process. It is found that the real part of the admittance signature is reliable and critical in sensor diagnosis, and sensor faults of debonding and breakage can be identified and differentiated. It also validates the semi-analytical work results. Therefore, the proposed methodology for pre-diagnosing damage present in the smart PZT sensor and adhesive layer using the EMI technique can be utilized for effective SHM.
查看更多>>摘要：This work investigates the efficiency of electrostatic precipitation for ultrafine particulate matter (PM) emissions from small-scale heating units with solid fuel combustion. The removal efficiency for particles smaller than 50 nm was of research interest. The effect of electric parameters and operation conditions of precipitation was studied. Some appropriate models considering the impact of turbulent diffusion have been used to predict the efficiency of a specific electrostatic precipitator (ESP). The electric field in studied ESP was intensified from 1.8 x 10(5) V/m to 2.8 x 10(5) V/m, and Nt-product (the product of number concentration of ions and charging time) was varied from 1.1 x 10(14) ions x s/m(3) to 7.1 x 10(14) ions x s/m(3). The prediction correctness is considered by comparing to experimental measurements carried out at the ESP used to control emissions from a 160-kW boiler. The changes in number particle concentration were measured with two technics simultaneously. It was found that an increase in field strength in mentioned range affects the ESP removal efficiency strongly and evenly. In contrast, the effect of growing Nt-product has a limit on precipitating efficiency of 4 x 10(14) ions x s/m(3). The results of the present work could be helpful in the practical engineering of electrostatic precipitation.
查看更多>>摘要：We have proposed new methods to reduce the LNG mass flow measurement uncertainty using a Coriolis mass flowmeters (CMF). Since the uncertainty in the corrected Young's modulus of meter tube is the dominating contribution factor, it is proposed to derive the Young's modulus at LNG temperature using the calculated LNG density and the natural bending frequency measurement. The expanded uncertainty of the calculated Young's modulus is evaluated to be 0.071%, which will enable the LNG mass flow measurement uncertainty of a straighttube CMF to be reduced from 0.50% to 0.20% (k=2). This approach has the potential to provide more accurate LNG mass flow measurement in comparison to conventional methods which use the corrected Young's modulus at LNG temperature. We have analysed the error in flow measurement using a U-tube CMF. An extra mass flow factor is shown to be the dominating mass flow measurement uncertainty factor due to the high uncertainty in the measured Poisson's ratio of the tube. A new method is proposed to calculate the Poisson's ratio from the torsional frequency and bending frequency measurements, with expanded uncertainty of 0.14%, 13 times lower than that of the measured values. The LNG mass flow measurement uncertainty of a U-tube CMF is estimated to be to 0.24% (k=2) using the calculated Poisson's ratio and Young's modulus. Our theoretical analysis shows that accurate estimation of Young's modulus and Poisson's ratio can significantly reduce the LNG mass flow measurement uncertainty using a CMF.
查看更多>>摘要：Wind tunnel tests are carried out upon a NACA0021 airfoil subjected to transitional Reynolds numbers. Transitionally-operating airfoils show a high sensitivity to external conditions and pose relevant measurement issues for capturing the physical processes adequately. On the global side, the employed set of techniques measures lift forces directly and uses the momentum-deficit method for drag coefficients. Locally, the development of transitional structures is acknowledged via surface pressure measurements carried out by pressure taps together with oil-flow visualizations. The coupling of such techniques with a well-founded uncertainty analysis shows two relevant aspects of the measurement protocolization: on the one hand, the limitations of either the global or local methods for completely accounting for all transitional phenomena. On the other hand, the fact that combining the proposed set of different measurement techniques with a systematic protocol is a mandatory requirement for achieving a holistic characterization of transitionally-operating airfoils.
NSTL
Elsevier