Position Of Measuring Points Research Articles

Enhanced hydrofoil pressure field reconstruction through phase-informed sensing and sensor optimization

This study proposes a cost-effective mixed-learning method for the cavitation behavior on hydrofoils, aiming to predict the global pressure field on the hydrofoil surface with sparse placed pressure sensors and the variation of the cavity outline captured by the high-speed cameras. The method integrates CS (compressed sensing) and deep learning techniques. Firstly, within the framework of CS, the Particle Swarm Optimization (PSO) algorithm is utilized to identify the best measuring points and achieve the initial reconstruction accordingly. Subsequently, in combination with the phase information, the final reconstructions are obtained within the deep learning framework. Through validation, the proposed mixed-learning model demonstrates significantly improved reconstruction performance compared to a single source of pressure information and an unoptimized measurement point position and significantly reduces the number of sensors. This provides a novel and effective approach for accurately predicting the pressure field on hydrofoil surfaces. Furthermore, the study evaluates the robustness concerning hydraulic sensor measurement errors, sensor placement deviations, missing measuring points, reconstruction performance under various cases and hydrofoil surface regions, and the contribution of optimized measuring points to the global field. Results show that the mixed-learning model exhibits robustness against typical measurement errors, position deviations of hydraulic sensors and missing measuring points on the suction side. Additionally, a positive correlation exists between the average pressure gradient under different conditions and regions and the reconstruction error, with the pressure side region significantly contributing to global field reconstruction. These findings underscore the mixed model's superiority in predicting pressure fields on hydrofoils and offer guidance for rational sensor installation.

A Measurement Method for Body Parameters of Mongolian Horses Based on Deep Learning and Machine Vision

The traditional manual methods for measuring Mongolian horse body parameters are not very safe, have low levels of automation, and cannot effectively ensure animal welfare. This research proposes a method for extracting target Mongolian horse body parameters based on deep learning and machine vision technology. Firstly, Swin Transformer is used as the backbone feature extraction network of Mask R-CNN model, and the CNN-based differentiated feature clustering model is added to minimize the loss of similarity and spatial continuity between pixels, thereby improving the robustness of the model while reducing error pixels and optimizing the rough mask boundary output. Secondly, an improved Harris algorithm and a polynomial fitting method based on contour curves are applied to determine the positions of various measurement points on the horse mask and calculate various body parameters. The accuracy of the proposed method was tested using 20 Mongolian horses. The experimental results show that compared with the original Mask R-CNN network, the PA (pixel accuracy) and MIoU (mean intersection over union) of the optimized model results increased from 91.46% and 84.72% to 98.72% and 95.36%, respectively. The average relative errors of shoulder height, withers height, chest depth, body length, croup height, shoulder angle, and croup angle were 4.01%, 2.98%, 4.86%, 2.97%, 3.06%, 4.91%, and 5.21%, respectively. The research results can provide technical support for assessing body parameters related to the performance of horses under natural conditions, which is of great significance for improving the refinement and welfare of Mongolian horse breeding techniques.

Research on Vibration Characteristic Analysis and Fault Diagnosis Method of Oil‐Immersed Transformer Based on Multi‐Physics Coupling

The oil‐immersed transformer is a crucial power equipment in power system, which is prone to abnormality or failure under long running. In order to improve the level of operation and maintenance and ensure the safe and reliable operation of the power grid, it is necessary to diagnose the fault of the oil‐immersed transformer in time. Most of the traditional fault vibration methods are off‐line detection, and are easily disturbed by mechanical deterioration. In order to solve the above problems, this paper established a 3D electromagnetic and stress coupling simulation model of oil‐immersed transformer by finite element simulation software. The vibration characteristics of oil‐immersed transformer core winding are obtained, and at the same time, this paper obtained the vibration signals at different positions on transformer core winding and oil tank. According to the vibration signal of the transformer, the best measuring point position of the tank wall is proposed to accurately reflect the vibration characteristics of the transformer. The signal data is preprocessed by CEEMDAN decomposition method, and the signal data is classified by GWO‐BP composite classification algorithm. This paper proposed an online transformer fault diagnosis method based on vibration signal, which achieves the purpose of accurately diagnosing oil‐immersed transformer faults. The test results show that in the case of oil‐immersed transformers the accuracy of the proposed transformer fault diagnosis method is more than 93%, which provides important guiding significance for the safe and stable operation of the transformer. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A novel method for evaluating stack pressure in real high-rise buildings: Optimization of measurement points

Excessive stack pressure in a high-rise building always causes various problems, and the most direct way to understand the extent of the stack pressure across building components is to derive pressure distribution profiles for the entire buildings. As an alternative to measurement process, which is generally time-consuming and labor-intensive, an optimal field measurement method is proposed to investigate the real-time pressure distribution in operating high-rise buildings. The absolute pressure at unmeasured points can be accurately predicted from measured values at selected key points, both horizontally and vertically. Robust linear regression and the MARS model are applied to develop the related prediction models, and the number and positions of measurement points in the horizontal and vertical directions are optimized sequentially. Two high-rise buildings were selected to validate the applicability of the method, and five and six key pressure measurement points were finally determined, respectively. The measured and predicted absolute pressure values at unmeasured points were further compared, and the results showed a high goodness of fits as the R2 values were higher than 0.98 and the average MAE values were less than 2.0 Pa for all models. It is observed that the proposed method is suitable for engineering applications and demonstrates a practical method for investigating stack pressure distribution within high-rise buildings.

Inverse Estimation of Thermal Contact Resistance Between Two Layers of Cylindrical Wall

This study presents a method for estimating the space-dependent thermal contact resistance between the two-layer walls of a furnace using the boundary element method (BEM) and conjugate gradient method (CGM) for the heat conduction problem. The global solution equation in matrix form is derived using the interface conditions, and the BEM is used to solve the direct problem. The CGM minimizes the objective function and calculates the sensitivity coefficients with the complex variable derivation method (CVDM). Comparative results show that the present approach is more accurate, stable, and efficient than the conventional CGM, which is attributed to the calculation of the sensitivity coefficients by CVDM. The effects of the value of thermal contact resistance, thermal conductivity ratio, Biot number, initial guess, measurement error, and the number and position of measurement points on the inversion results are also analyzed. Finally, the effectiveness of this approach is demonstrated through numerical examples, and the inversion results show its stability, efficiency, and accuracy in identifying different and complex distributions of thermal contact resistance. Furthermore, this approach is feasible for nonintrusive measurement, which is very meaningful in practical applications.

Channel Selection for Gesture Recognition Using Force Myography: A Universal Model for Gesture Measurement Points.

Gesture recognition has emerged as a significant research domain in computer vision and human-computer interaction. One of the key challenges in gesture recognition is how to select the most useful channels that can effectively represent gesture movements. In this study, we have developed a channel selection algorithm that determines the number and placement of sensors that are critical to gesture classification. To validate this algorithm, we constructed a Force Myography (FMG)-based signal acquisition system. The algorithm considers each sensor as a distinct channel, with the most effective channel combinations and recognition accuracy determined through assessing the correlation between each channel and the target gesture, as well as the redundant correlation between different channels. The database was created by collecting experimental data from 10 healthy individuals who wore 16 sensors to perform 13 unique hand gestures. The results indicate that the average number of channels across the 10 participants was 3, corresponding to an 75% decrease in the initial channel count, with an average recognition accuracy of 94.46%. This outperforms four widely adopted feature selection algorithms, including Relief-F, mRMR, CFS, and ILFS. Moreover, we have established a universal model for the position of gesture measurement points and verified it with an additional five participants, resulting in an average recognition accuracy of 96.3%. This study provides a sound basis for identifying the optimal and minimum number and location of channels on the forearm and designing specialized arm rings with unique shapes.

Study on vibration characteristics of structural components based on virtual reality technology

In order to more effectively and intuitively obtain the modal characteristics of steel structural components, a graphical analysis scheme is proposed based on dynamic testing, finite element simulation, and virtual reality technology. Based on the external excitation method, the vibration sensors and measurement point positions were reasonably arranged according to the number of nodes in the structural components. Through the data processing of the detection unit, the vibration response can be effectively obtained and provide a verification basis for the simulation results. The finite element software Midas Civil was used to model and analyze structural component models, and the results of the first five modes and natural frequencies were obtained. The structural data of modal analysis will be compared and integrated into the virtual environment model through VRML. Due to the ease of modifying the parameters of the virtual model, compared to traditional methods, it is faster and more accurate in detection and scientifically reasonable in control.

Geometric inverse estimation for the inner wall of a furnace under transient heat conduction based on dual reciprocity boundary element method

A boundary shape estimation problem for the furnace’s inner surface is solved using the dual reciprocity boundary element method (DRBEM) and the conjugate gradient method (CGM) under transient heat conduction. The DRBEM is utilized to eliminate the drawbacks of traditional numerical methods and classical boundary element method of discretizing the entire computational domain, with only boundary discretization. The inversion results are obtained by applying the CGM to minimize the objective function, in which the sensitivity coefficients are calculated with the complex variable derivation method (CVDM), making the calculation precise and independent of step size. To verify the accuracy of the DRBEM in solving the transient heat conduction problem, the influencing factors including radial-basis function, the number of internal collocation points, and time step size are investigated. The influences of measurement time interval, future time step, initial guess, measurement error, and the number and position of measurement points on the inversion results are analyzed. Meanwhile, the effectiveness of the proposed approach is tested by numerical examples, and the inversion results show that it is stable, accurate, and efficient for identifying different and complicated unknown boundary shapes of the furnace.

Design of Temperature Monitoring System for Air Cooled Radiators Based on LoRa

Air cooling technology is widely used in the north. When the winter temperature in the north is too low, the cooling radiator is prone to freezing, leading to the inability of air cooling units to operate normally and causing huge economic losses. In response to the above issues, this article designs a monitoring system for air cooled radiators based on LORA communication, based on Internet of Things technology. Analyze the A-type tower structure and heat dissipation principle of the air-cooled condenser, reasonably select the temperature measurement point position, and achieve temperature data transmission through LoRa communication and Ethernet communication technology. The monitoring software is programmed in C # language, which can visually display the temperature distribution information of the air-cooled condenser and store data in real time. The experimental results indicate that the system has certain guiding significance for preventing the freezing phenomenon of air cooled condensers and optimizing operation.

Theoretical derivation and experimental investigation of dynamic displacement reconstruction based on data fusion for beam structures

Accurately obtaining the dynamic displacement response of the beam structure is of great significance. However, it is difficult to directly measure the dynamic displacement for large structures due to the low measurement accuracy or the installation difficulty of the sensor. Therefore, it is urgent to develop an indirect measurement method for displacement based on measurable physical quantities. Since acceleration and strain contain high and low frequency displacement information respectively, this paper proposes a displacement reconstruction algorithm that can realize the data fusion of the two, which is very helpful for the research of structural health monitoring. Firstly, the stochastic subspace identification (SSI) method is adopted to calculate the strain mode, and then the displacement is derived via the mode shape superposition method. Afterwards, the strain-derived displacement and acceleration are combined by the proposed algorithm to reconstruct the dynamic displacement. Both the numerical simulation and model experiment are conducted to verify the effectiveness of the proposed algorithm. Furthermore, the influences of noise, sampling rate ratio and measurement point position are analyzed. The results show that the proposed algorithm can accurately reconstruct both high-frequency and pseudo-static displacements, and the displacement reconstructed error in the model experiment is within 5%.

Wavelet bispectral analysis and nonlinear characteristics in waves generated by submerged jets

Nonlinear interaction dominates the whole process from wave deformation to breaking, and is one of the significant factors that may cause many unstable changes in waves. This study aims to indicate the nonlinear characteristics of waves generated by submerged jets. The time series of those waves under five dimensionless upstream water depths (l = 3.0, 4.0, 4.5, 5.0, 5.5) are measured experimentally. Moreover, the wave surface asymmetry, skewness, spectrum, and wavelet-based bicoherence are obtained. The results show that the amplitude of wave surface varies with the variation of measuring point position and upstream water depth. From the perspective of asymmetry and skewness, the wave surface varies from tilt forward to tilt backward along the propagation path, and the vertical tilt amplitude decreases gradually; meanwhile, except for few measuring points, it is basically negative skewness. It can be seen from the energy spectrum that the wave energy increases with the increase of upstream water depth and decreases with the propagation of wave surface. In addition, the wavelet-based bicoherence distribution shows that the energy transfer caused by the interaction between waves is weak. The findings can provide an improved understanding of the nonlinearity characteristics of waves generated by submerged jets.

Flutter Boundary Prediction Based on Structural Frequency Response Functions Acquired from Ground Test

Establishing an accurate, fast, and low-risk flutter boundary prediction method is of great significance for flight vehicle design. In this paper, a ground flutter boundary prediction method (GFBP) based on experimental structural frequency response functions (FRFs) is proposed. A low-order multi-input multi-output (MIMO) aeroelastic system is established by combining the structural FRFs acquired from a ground test and the calculated unsteady aerodynamic FRFs in physical coordinates. The multivariable Nyquist criterion is used to predict the flutter boundary. A fixed-root aluminum plate wing is selected as the research model. A GFBP experiment is carried out for the wing’s normal state, leading-edge clump weight state, and trailing-edge clump weight state. The feasibility and accuracy of the proposed method are verified by comparison with theoretical flutter results, in which the errors of flutter speed and frequency in the test statistics are no more than 1.7%. In a simulation model established by the proposed method, Monte Carlo simulation is used to study the influence of deviations in the mode frequency and damping of the structural FRFs and deviations in the positions of excitation and measurement points in the ground test. The experiment and simulation results show that the proposed method can predict the flutter boundary accurately with accurate positions of excitation and measurement points, and it has good robustness to deviations in the mode frequency and amplitude of the structural FRFs.

Optimization Method of Temperature Measuring Point Layout for Steel-Concrete Composite Bridge Based on TLS-IPDP

An optimization method of temperature measurement point layout for steel-concrete composite bridges based on the total least squares improved piecewise Douglas–Peucker (TLS-IPDP) algorithm was proposed to solve the problem that the traditional temperature measurement data cannot reflect the actual temperature gradient (TG) due to the position of measurement points on different paths is not reasonable. The characteristic curves of TG for the most unfavorable period and annual period are extracted from the finite element model. The rationality of the proposed method is illustrated by two typical steel-concrete composite beams with steel plates and steel boxes. By improving the classical Douglas–Peucker (DP) algorithm, the TLS-IPDP algorithm proposed in this paper has a better approximation effect on the original data. Compared with the traditional temperature measuring point arrangement method, the TLS-IPDP algorithm optimized arrangement in this paper realized the measuring point arrangement with different variable spacing under different paths; the temperature gradient curve obtained was closer to the real temperature distribution, and had higher accuracy in the region with a large gradient. In addition, the proposed method has the function of manually specifying the location of feature points and reserving the required number. The optimized arrangement of measuring points can meet the requirements of measuring points number and measurement accuracy. The method presented in this paper can provide a useful reference for temperature data acquisition and sensor layout for health monitoring of steel-composite bridges.

Development and justification of universal designs for energy tests in flow paths of hydroelectric power plants

Introduction. The authors present one of methods for measuring water flows through the intake of a hydroelectric power plant. The new structure has a metal frame and a folding rotary row. The authors analyzed the advantages of the proposed, and mase strength and hydraulic analyses. Computational studies of the stress-strain state, made with account taken of the actual hydrodynamic pressure, allow choosing the optimal position of measurement points, designing a frame structure, and making highly accurate measurements of energy characteristics.
 Materials and methods. Top international publications, as well as archived materials, were analyzed to select the universal frame structure. The most promising directions were identified; the advantages and disadvantages of the proposed solutions were taken into account. Complex computational studies were performed using ANSYS Mechanical, a universal industrial software package, and ANSYS CFX, a specialized module for modeling flows of liquids and gases with account taken of turbulence.
 Results. The position of measurement points that ensure the least distortion of the flow and tilt angles of hydraulic turntables were determined during the hydraulic simulation. The flow loads were taken into account when the stress-strain state of the universal frame structure was calculated; optimal design solutions were selected to ensure the strength and reliability of metal elements. Stress concentration zones were identified for monitoring purposes during installation.
 Conclusions. Given the mathematical modeling data and experimental field studies, a universal frame structure for energy tests was substantiated. The new design ensures a measurement error of ±0.67 %, which corresponds to the leading world standards.

Identification of start of combustion based on contribution level of principal component in vibration signal of cylinder head surface

The surface vibration signal measured from diesel engine cylinder head is caused by in-cylinder pressure (ICP) excitation and non-combustion excitations. These excitation response signals are coupled together, which increases the difficulty of identifying combustion information from the vibration signal. Furthermore, there always exists a phase deviation between the identified phase combustion parameter and the theoretical value, which decreases the recognition accuracy of combustion parameter. To accurately extract the combustion information, this paper firstly simulates the vibration signal caused by the combine of ICP, piston side pressure, piston slap and reciprocating inertial force of an SD2100TA diesel engine. Then, the principal component analysis (PCA) is introduced to analyze the contribution level of the combustion excitation response signal in the vibration signal under different working conditions. Meanwhile the influence of measuring point position and number on the contribution level is also discussed. Results show that the contribution level decreases gradually with the increase of the rotation speed and slightly increases with the increase of the torque. In addition, the contribution level is higher when the measuring point is farther away from adjacent cylinder. Finally, the relationship between the contribution level and the identified phase deviation of the start of combustion (SOC) is analyzed, and the deviation correction method is proposed. Results show that the maximum error of SOC after correction is within ± 1 deg.

Investigation of Functional Dependency between the Characteristics of the Machining Process and Flatness Error Measured on a CMM

Abstract Numerous studies have shown that the choice of measurement strategy (number and position of measurement points) when measuring form error on a coordinate-measuring machine (CMM) depends on the characteristics of the machining process which was used to machine the examined surface. The accuracy of form error assessment is the primary goal of verification procedures and accuracy is considered perfect only in the case of the ideal verification operator. Since the ideal verification operator in the “point-by-point” measuring mode is almost never used in practice, the aim of this study was to examine a relationship which had not been examined in earlier studies, namely how the machining process, surface roughness and a reduced number of points in the measurement strategy affect the accuracy of flatness error assessment. The research included four most common cutting processes applied to flat surfaces divided into nine different classes of roughness. In order to determine functional dependency between the observed input variables and the output, statistical regression models and neuro-fuzzy logic (artificial intelligence tool) were used. The analyses confirmed the significance of all three input parameters, with surface roughness being the most significant one. Both the statistical regression models and neuro-fuzzy models proved to be adequate, matching the experimental results. The use of these models makes it possible to determine flatness error measured on a CMM if input variables considered in the paper are known.

Five dimensional movement measurement method for rotating blade based on blade tip timing measuring point position tracking

As a non-intrusive blade vibration measurement technology, Blade Tip Timing (BTT) has been widely studied and developed in recent years. However, there are still many potential uncertainties in BTT technique, which reduce the correlation of the BTT measurements against the Finite Element (FE) predictions and the Strain Gauge (SG) measurements. One of the main ones is that the BTT sensor only measures the displacement of the blade tip in the direction of rotation. But blade tip movements other than rotation direction including axial, lean, bend, untwist, and sweep will change the position of the measuring point relative to the blade tip. In this paper, a method for measuring the five dimensional blade tip movements is proposed. This method uses BTT data, blade tip profile equation, and the initial measuring point position to track the actual measuring point position relative to the blade tip during blade rotation, so as to determine the blade axial, lean, bend, untwist, and sweep. Simulation results have proven the effectiveness and accuracy of the proposed method. Experiments were carried out on the test rig, industrial axial fan, and engine fan, results show that the method can effectively and accurately measure these types of blade tip movements. The relative error of blade untwist angle measurement is 3.6%. The work in this paper can improve the reliability of BTT measurements, and is of great significance to the comprehensive perception of operation state and accurate evaluation of dynamic stress of rotating blades.

Analysis of microstructure and wear resistance of NM400 thick plate

The hardness distribution and microstructure morphology of NM400 low alloy wear resistant steel plate with thickness of 50mm and 60mm were tested and analyzed, and the sliding wear performance of typical measuring points was also analyzed. The results show that the depth of the NM400 (50mm) hardening layer is about 35mm, accounting for about 70% of the plate thickness. The depth of the NM400 (60mm) hardening layer is about 22mm, accounting for about 37% of the plate thickness. With the position of the measuring point gradually away from the surface of the steel plate, the martensite content in the microstructure of NM400 (50mm) and NM400 (60mm) gradually decreased, and the contents of pearlite, bainite and other microstructure gradually increased. The hardness of NM400 (50mm) gradually decreased from 410HV to 335HV, and the hardness of NM400 (60mm) gradually decreased from 410HV to 305HV. The micro-wear morphologies of NM400 (50mm) and NM400 (60mm) at each measuring point are mainly furrow and contact fatigue spalling, and the wear types are mainly abrasive wear and contact fatigue wear. The furrow depth and the number of furrows on the worn surface of the sample at the corresponding measuring point position gradually increased as the measuring point position moved away from the steel plate surface, and the spalling area and the depth of the spalling pit gradually increased. The wear type gradually changed from particle wear to contact fatigue wear.

선박 곡부재 계측 포인트에 대한 곡면 모델링 방법

Forward and afterward parts of ship and offshore structures are composed of curved plates with a thick thickness. It takes mush time to process the curved plate. If the fabricated curved plate has some dimensional errors, the additional cost is incurred in the assembling process. So, it is important to assess accurate dimensions for fabrication. Recently, photometric systems and 3D laser scanners have been used for this purpose. The dimensional assessment for fabrication is evaluated by comparing the surface created from the measurement points with the design surface. A B-spline surface interpolation is generally used to create the measurement surface, but there may be errors depending on the number and position of measurement points. In this study, we propose a method to improve the generation of the measurement surface. The mesh refinement method is used to obtain an accurate curved surface using a small number of measurement points. A Hermite curve interpolation is applied to reduce the error of the final generated surface. For accurate interpolation, the exact tangential direction is calculated at the vertex of an element, and errors are evaluated for various calculation methods.

Comprehensive fatigue estimation and fault diagnosis based on Refined Generalized Multi-Scale Entropy method of centrifugal fan blades

Fan is widely used in industry as a very important component of pressure transmission and system cooling. Fan blade is the key part which decides the service life of fan and the whole system. In this paper, the research focuses on the centrifugal fan of high-speed train cooling system and the blades on it. The experimental system and research method of fatigue analysis and fault diagnosis are built. The fan can work in both steady and unsteady state conditions controlled and driven by the test system. At the same time, the signals of strain and acceleration at different positions can be obtained accurately. Based on the static tension and dynamic fatigue test of the material, the accurate simulation of the fan is carried out. By simulation analysis, steady-state test, long-time start-stop test and metallographic test, the prediction of fatigue life of fan blades is conducted with in-depth analysis and comprehensive evaluation. The results show that fan meets the requirements of actual working life. As the actual working state is more diverse and severe than the above test process, some phenomena of damage also appear in the blades after service. Based on real situation, the blade faults are classified. After determination of measuring point position, the vibration signals of four fault states of blade under rated speed are collected. This paper proposes a new fault feature extraction method – the Refined Generalized Multi-Scale Entropy (RMSEσ2) combined with Support Vector Machine (SVM). According to the results of the empirical mode decomposition (EMD), the effective intrinsic mode function (IMF) components are selected by energy distribution and correlation coefficient. The comparative analysis of multi-scale entropy (MSE), generalized multi-scale entropy (MSEσ2) and RMSEσ2 between the original signal and 1–8 IMF components are carried out. Results show the RMSEσ2 of IMF1 component of Z direction of point 1 is the best choice. And it is imported into SVM for pattern recognition with scale factor 20. The optimal SVM model is obtained by choosing kernel function and parameters that are optimized by Particle Swarm Optimization (PSO) method. The results show the faults of centrifugal fan blades can be classified accurately. A set of research flow and method is set up, which provides important reference for fatigue and fault pattern recognition of fan blades in the future.