Measurement最新文献

Fabric crease, a common structure in the manufacturing of smart textiles, has a direct impact on the function and application of the product. Based on the self-designed fabric crease testing device, this paper shows that a prediction model of crease energy change is established to evaluate the mechanical properties of fabrics crease during the slow elastic deformation. During the test of the fabric crease, the relevant parameters of the crease energy model are determined by analyzing the falling speed and height of the fabric, and the bending degree of the fabric on both sides, including the impact coefficient in the kinetic and the bending energy models, as well as the corner multiplier and corner coefficient in the gravitational potential energy model. The experimental results indicate that in the process of the slow elastic deformation, the energy changes of crease recovery are mainly derived from changes in gravitational potential energy. In addition, in the verification experiment of the crease energy model, the energy results of the finite element simulation and video image calculation prove that the accuracy of the developed energy model is over 0.8. Compared with the existing fabric crease testing methods, the stability of energy model test has increased by 1.76%.

In this paper, a dual-core fiber optic sensor has been proposed for dynamic monitoring of temperature and humidity. The side core is polished into a D-type optical fiber, and a layer of nano-silver film and a layer of polydimethylsiloxane (PDMS) are deposited to construct a surface plasmon resonance (SPR) temperature compensator. The middle core is constructed as the long-period fiber grating (LPFG) sensing channel. The mode conversion of LPFG is achieved by TiO₂ film, and its refractive index (RI) sensitivity is increased by about 4 times. Meanwhile, a layer of moisture sensitive MIL-96(Al) film is grown outside the TiO₂ film. Due to the particular pore structure for water molecules, it is possible to change the effective RI of MIL-96(Al) film by absorbing water molecules. Research has shown that the LPFG sensor can achieve a relative humidity sensitivity of −0.411 nm/%RH. Finally, the sensor successfully achieved real-time dynamic monitoring of respiratory rate.

Damage identification of composite propeller blades is critical to the operational safety of unmanned aerial vehicles (UAVs). The transmissibility function (TF) was employed to characterize the damage and its probabilistic distance was used to deal with the uncertainty problem. The damage indicator fusing multiple TFs in a specific frequency band was proposed to detect the blade damage. In addition, to localize the damage in the blade, an attentional bidirectional temporal convolutional network (ABiTCN) model was developed, in which the Squeezeand-Excitation (SE) attention module was introduced to enhance the model’s capability to learn critical features. The proposed method was investigated by numerical and experimental cases. The results showed that the proposed damage indicator has more sensitivity to weak damage and better robustness than the common indicator. The established ABiTCN model predicted the positions of the damages with 91.89 % accuracy, which outperforms other methods in terms of accuracy and convergence speed.

In the paper, we experimentally presented vector bending sensor based on the multimode-dual core- multimode fiber cascaded Fiber Bragg grating (FBG) structure, which owns the ability of dual-parameter detection. These two multimode fibers (MMFs) function as a mode beam expander and a mode coupler, separately, and the FBG is used to eliminate the temperature crosstalk. Because of the central symmetry of the dual-core fiber (DCF) and its two cores arranged in a linear array, the sensor can achieve recognition directional bending. The experiment of results demonstrates our sensor exhibits a robust response to vector bending response with the max bending sensitivities of 18.259 nm/m⁻¹ in the bending direction of 0°. The temperature sensitivity of the resonance wavelength generated by the Mach-Zehnder interferometer (MZI) and FBG can reach −0.285 nm/°C and 0.010 nm/°C, separately. Through the dual wavelength matrix method, we can achieve simultaneous measurement of vector bending response and temperature response. Featuring notable strengths such as high vector bending sensitivity, ease of fabrication, low cost, and direction recognition, the sensor can be applied in various fields of vector bending measurement.

In this paper, a rotationally distributed slot type long period fiber grating torsion sensor is proposed. The sensor employs CO₂ laser to etch grooves on the fiber surface in a structure helically distributed slot around the fiber axis, which breaks the circular symmetry of the fiber and introduces a residual twisting force, thus introducing a large amount of elliptical birefringence. By investigating the effect of different periods on the torsional sensitivity, the torsional sensitivity is effectively improved. The experimental results indicate that the torsion sensitivity of the sensor is up to 0.42 nm/(rad/m) in the measurement range of −13.08 rad/m to 13.08 rad/m, which is 10 times more sensitive than that of the traditional torsion sensor, and the direction of torsion can be detected. The temperature characteristics of the sensor are also investigated, and the temperature sensitivity in the detection range of 30 °C to 120 °C is 76 pm/°C, with a temperature crosstalk of 0.18(rad/m)/°C. This sensor provides a new solution for LPFG torsion sensing enhancement, which can be used in the field of engineering structure monitoring with high torsion sensitivity requirements.

The measurement of the nonlinear dynamics of sandwich rotary sector plates is crucial for measurement engineers as it enables the precise analysis and understanding of the behavior of advanced composite structures under dynamic conditions. These measurements help in identifying and mitigating potential issues related to vibration, stability, and fatigue, which are critical in industries like aerospace, automotive, and civil engineering. Accurate data on nonlinear dynamics aids in the optimization of design, enhancing performance, durability, and safety. Furthermore, it supports the development of predictive maintenance strategies, reducing downtime and operational costs, and contributing to the advancement of engineering materials and techniques. So, in the current work, for the first time, nonlinear dynamics of sandwich rotary sector plate via a mathematical modeling and machine learning algorithm is presented. First, due to a lack of information on the nonlinear dynamics of sandwich rotary sector plates, a dataset for training, testing, and validating the machine learning algorithm is presented. For this purpose, using Hamilton’s principle, Von-Karman nonlinearity, and first-order shear deformation theory (FSDT), the nonlinear governing equations are obtained. Also, due to increasing stiffness and stability, an auxetic concrete foundation covers the sandwich structure. After that using the two-dimensional generalized differential quadrature method (GDQM) and Newmark’s time integration method (NTIM), the nonlinear equations are solved. This research provides valuable insights for engineers in designing advanced composite structures with improved dynamic properties. The results also contribute to the broader understanding of nonlinear dynamic interactions in complex material systems, paving the way for innovative applications in various engineering fields.

To investigate the effect of aggregate size on pore structure and pore evolution in water saturation of manufactured sand aggregate (MSA) mortars, five MSA mortars with small range of aggregate sizes were prepared and their pore characterization were monitored over 365 days. The findings showed that an increased surface volume ratio (SVR) leads to an increase in total and macroporous porosity, and three pores with different connectivity were quantified using double T₂ cutoff method. Moreover, continued hydration by unhydrated cement caused an increase in micropores during the initial stage of service in a water-saturated environment for 365 days, and an increase in macropores at the later stage as a result of enhanced hydraulic solvation, furthermore, solvation was more significant in the mortar with larger SVR to the point where it eventually produced pores larger than 100 μm. This research would provide guidance for the durability evaluation of cement mortars.

A ice force monitoring system based on fiber optic sensing technology is proposed to address the limitations of traditional ice force monitoring methods in harsh marine environments. The design requirements of the ice force sensor, the design calculations of the elastic element, and the selection and arrangement of fiber optic sensors are elaborated. The sensitivity coefficient of the designed sensor is determined to be 0.044 nm/KN, with an R² value of 0.999. The performance and reliability of the sensor are validated through calibration tests and indoor loading experiments. Field measurements of ice force are conducted, with ice force amplitudes measured to be around 2KN. Additionally, Temporal Convolutional Network (TCN) is introduced for sequential prediction of extreme static ice force data, aiming to provide more comprehensive support for long-term safety assessment and risk warning of marine platforms.

This study presents a calibration method for telecentric line-scan cameras, incorporating a dynamic imaging model and initial estimates derived from direct linear transformation. The enhanced dynamic imaging model includes an inclination parameter instead of velocities in three orthogonal spatial directions. It is applicable to any direction of motion, considers lens distortion, and accommodates the line sensor at an arbitrary position relative to the optical axis. Employing the enhanced camera model, the initial values of telecentric line-scan camera parameters are determined derived from direct linear transformation. Sensitivity of our method with respect to the noise level of image points and the number of pattern planes are assessed through numerical simulations, while effectiveness and robustness are validated through experiments using real-world data, achieving a reprojection error of 0.6386 pixels and a standard deviation of 0.0160 pixels. The versatility of the proposed method can be extended to measurement applications utilizing flatbed scanners.

With the large-scale access of new energy generation and electric vehicle charging piles to the grids, power generation and loads are characterized by random fluctuations and intermittency, which affect the stable operation of the distribution network. μM-PMU is capable of realizing the dynamic process monitoring of the grids. In order to solve the function and performance testing problems of multiple application scenarios of μM-PMU, this thesis develops a portable master station based on synchronous phasor measurement system to realize zero drift, voltage amplitude measurement accuracy, voltage phase angle accuracy, current amplitude measurement accuracy, current phase angle measurement accuracy, frequency measurement accuracy, voltage amplitude change with frequency accuracy, and voltage phase angle change with frequency accuracy, Accuracy of current amplitude with frequency change, accuracy of current phase angle with frequency change, active power accuracy, reactive power accuracy, amplitude step response, phase angle step response, frequency step response, a total of 15 test items. It can automatically calculate test results and generate test reports according to different equipment models, reducing the mistakes and calculation errors that may be caused by manually recording test data and improving the accuracy of test results and calculation precision.