Journal of Measurements in Engineering: Table of Contents

Numerical simulation of chloride ion transport in concrete based on a random aggregate model

Wen, Dongchang — 2025-08-24T00:00:00Z

Journal of Measurements in Engineering, Vol. 13, Issue 3, 2025, p. 581-595.
Dongchang Wen, Guohe Guo, Shangchuan Zhao, Longlong Liu
A three-dimensional stochastic aggregate model of concrete was established using the Monte Carlo method, and a numerical simulation of chloride ion diffusion at the microscopic level was conducted. The study investigated the migration behaviour of chloride ions in concrete regarding mixing proportions and temperature. The results showed that compared to the simulation results at an ambient temperature of 20 ℃, the chloride ion diffusion coefficient increased by 31 % and 70.5 % for concrete at 25 ℃ and 30 ℃ at 28 days, respectively. The chloride ion penetration depth increased by 17.3 % and 34.9 % for concrete at 25 ℃ and 30 ℃, respectively. With a slag content of 10.4 %, 20.8 %, and 27.1 %, the chloride ion diffusion coefficient at 28 days decreased by 1.4 %, 2.7 %, and 4.1 %, respectively. With a fly ash content of 8.3 %, 16.7 %, and 25 %, the chloride ion diffusion coefficient at 28 days decreased by 2.1 %, 5.4 %, and 9.2 %, respectively. Both slag and fly ash can reduce the chloride ion diffusion coefficient in concrete, with fly ash showing better effectiveness than slag. A water-to-binder ratio of 0.4, combined with 27.1 % slag and 25 % fly ash as cement replacements, can effectively improve the resistance of concrete to chloride ion attack. The micro-scale finite element model of concrete, developed through Monte Carlo simulation, offers enhanced visualization of chloride ion penetration processes under varying mix proportions and temperature conditions.

Development of methodology for monitoring of metalworking fluids quality

Kim, Anna — 2025-08-30T00:00:00Z

Journal of Measurements in Engineering, Vol. 13, Issue 4, 2025, p. 873-887.
Anna Kim, Jūratė Jolanta Petronienė, Andrius Dzedzickis, Vytautas Bučinskas
Efficient monitoring of metal-working fluids (MWFs) is crucial to maintaining optimal machining performance and ensuring the safety and health of workers in the metalworking industries. Knowledge of the performance of cutting fluids in the machining of various workpiece materials is very important to improve the efficiency of any machining process. Metal machining companies using MWS have the opportunity to choose the best product from the wide range offered, which can differ in physical parameters as it is designed to be best for the selected process. The unique adaptation to the manufacturing process poses certain challenges in monitoring MWS quality during machining. The importance of MWS quality is crucial, which can lead to costly defects and loss of workpieces. The monitoring only by the quality lab sometimes is insufficient. This article presents the development of a sensor for the indirect monitoring of MWFs, aiming to provide a cost-effective and nonintrusive solution to assess the quality and condition of these fluids. The measurement results are compared with those of other emulsion quality control protocols. Its implementation can significantly enhance the efficiency of MWF management, leading to improved machining performance, reduced downtime, and enhanced worker safety. The sensor's nonintrusive nature eliminates the need for frequent manual sampling, reducing costs and minimizing the environmental impact associated with traditional monitoring practices. Overall, the sensor described in this article offers a viable solution for indirect monitoring of MWFs, contributing to the advancement of smart manufacturing and the optimization of metalworking processes.

Study on stability of shaft surrounding rock under adjacent shafts mining disturbance in underground mine

Wang, Feifei — 2025-09-07T00:00:00Z

Journal of Measurements in Engineering, Vol. 13, Issue 4, 2025, p. 982-1004.
Feifei Wang, Anmin Jiang, Zhenjun Cui
The stability of mine shafts is crucial for safe production in underground mining. To elucidate the impact of adjacent shaft mining disturbance on shaft structural stability in underground mines, this study takes a Manganese Mine in Guizhou, China as a case study. A refined three-dimensional model at engineering scale was established by using the Rhino-FLAC3D coupled modeling method. This model can numerically simulate the mining of ore bodies at different stages of mining. The displacement, stress distribution, and plastic zone in both strata and shaft surrounding rock were systematically analyzed to reveal the response laws of shaft surrounding rock under mining disturbance. The results showed that during the first and second mining phases, no measurable deformation occurred in the surrounding rock of the main shaft, auxiliary shaft, or ventilation shaft. During the third mining phase, the maximum displacement observed in these shafts’ surrounding rock reached 0.048 m, which remains within the stability threshold of rock masses according to evaluation criteria. Regression analysis was conducted on the monitoring displacement of three mining stages, and power function fitting curves were obtained. Plastic zones (20-30 m range) developed along the periphery of goaf areas, maintaining a safe distance of 45-55 m from adjacent shafts. A stress gradient formed around goaf areas, with tension stresses up to 1.33 MPa exceeding the ultimate tension strength of roof strata. There was potential tension failure in the roof strata of the goaf. Although mining disturbance effects on main and auxiliary shafts intensified with depth progression, no substantial structural impacts were observed. This confirms that all shaft structures can maintain stability during operational phases. The findings provide theoretical guidance for shaft stability control in deep mining operations.

Development of a flexible piezoresistive sensor prototype using resin doped with magnetically oriented nanoparticles

Blanco Gómez, Rafael Hernando — 2025-09-13T00:00:00Z

Journal of Measurements in Engineering, Vol. 13, Issue 4, 2025, p. 1018-1028.
Rafael Hernando Blanco Gómez, Andres Felipe Rubiano Navarrete, Carlos Andrés Palacio Gómez, Leonel Paredes-Madrid, Yaneth Pineda Triana
High-performance flexible piezoresistive sensors are highly useful in areas such as biomedicine, soft robotics, and pressure change detection technology. However, they require complex designs and advanced manufacturing methods. In this study, the design and fabrication of a flexible piezoresistive sensor using a flexible resin matrix doped with magnetically oriented iron nanoparticles is presented. The sensor consists of a flexible polymer resin matrix as substrate, reinforced with iron nanoparticles in different concentrations (0.5 %, 0.7 % and 1 % by weight), oriented by a magnetic field during the manufacturing process. The nanoparticles significantly enhance the piezo-resistive properties of the sensor, increasing its sensitivity and electrical conductivity under compressive loads. The sensor demonstrated high sensitivity under loads greater than 100 N in samples with concentrations of 0.7 % and 1 % of nanoparticles, and exhibited stability during cyclic testing, demonstrating durability. Additionally, stability tests showed excellent durability in repeated load cycles. Scanning Electron Microscopy (SEM) and Confocal Laser Scanning Microscopy (CLSM) confirmed the effective alignment and distribution of the nanoparticles within the matrix, enhancing conductivity. This flexible piezoresistive sensor doped with nanoparticles has great potential for future applications in technologies such as soft robotics and electronic skins, where high sensitivity and durability in pressure detection are required.

Prediction of wharf subsidence deformation degree based on deep learning technology

Wang, Heng — 2025-09-18T00:00:00Z

Journal of Measurements in Engineering, Vol. 13, Issue 4, 2025, p. 845-853.
Heng Wang, Kai Li, Wenwu Cai
This paper presents an algorithm that combines a convolutional neural network (CNN) with a gated recurrent unit (GRU) to predict the wharf subsidence deformation. First, the digital elevation model (DEM) image features of the wharf area were extracted using the CNN, and then the patterns of change in wharf settlement were captured using the GRU. Moreover, the wharf in the Longtan Port area of Nanjing Port, located in Jiangsu Province, was analyzed. When the CNN comprised three convolutional layers and the activation function was set to sigmoid, the prediction performance of the proposed algorithm was the best. In both short-term and long-term scenarios, the CNN+GRU algorithm had better prediction performance than long short-term memory and GRU models.

A hybrid prediction method for short-duration traffic flow on urban roads based on integrated neural networks

Zhang, Xi — 2025-10-31T00:00:00Z

Journal of Measurements in Engineering, Vol. 14, Issue 1, 2026, p. 57-71.
Xi Zhang, Jian Yin, Baiying Yang
Urban road traffic flow is affected by many factors, such as road topology, road state and traffic control, among which there are complex spatial correlations and spatiotemporal dynamics, making it difficult to predict the results and accuracy. Therefore, a hybrid forecasting method for short-term traffic flow of urban roads based on integrated neural network is proposed. Collect and preprocess urban road traffic data to build a historical traffic flow dataset. Elman neural network is used to construct periodic sequence prediction sub network, long-term and short-term memory neural network is used to construct continuity prediction sub network, and ELM neural network is used to construct correlation prediction sub network. According to the traffic flow characteristics of the historical traffic flow data set, the related sequence set is constructed and allocated to each sub network. Through the global hybrid forecasting process, using the integration method based on parameter matrix, the outputs of three neural networks are mixed to obtain the final short-term traffic flow hybrid forecasting results of urban roads. Experiments have shown that the proposed algorithm can achieve relatively accurate traffic flow forecasting results, with an absolute percentage deviation of less than 1.6 % and high forecasting accuracy.

Three-dimensional trajectory planning for unmanned aerial vehicles based on the starfish optimization algorithm (SFOA)

Feng, Weiqi — 2025-12-13T00:00:00Z

Journal of Measurements in Engineering, Vol. 14, Issue 1, 2026, p. 39-56.
Weiqi Feng, Yujie Fu, Yong Yang, Changjian Gao, Kaijun Xu
When solving the path planning problem for Unmanned Aerial Vehicle (UAV) in a three-dimensional complex environment, traditional algorithms often face issues like falling into local optimum easily, insufficient global search ability, poor efficiency and defective optimization result. To address these issues, a three-dimensional path planning method is proposed based on the Starfish Optimization Algorithm (SFOA). This algorithm, inspired by the exploration, preying, and regeneration behaviors of starfish, balances global search and local exploitation, enhancing UAV trajectory planning in complex environments. The study constructs a complex three-dimensional environment model and designs a comprehensive optimization objective by covering constraints like trajectory length, safety, flight height, and smoothness. The trajectory planning framework proposed in this study is designed for pre-mission planning, generating UAV paths offline based on known static terrain and threat information. Comparative experimental results with Ant Colony Optimization and Particle Swarm Optimization show that the SFOA-based UAV trajectory planning achieves significant improvements in comprehensive cost and convergence speed, demonstrating superior global optimization performance. This offers an innovative solution for UAV efficiently and safe trajectory planning in complex environments.

Numerical simulation and spray test validation of the downwash airflow field for thermal fogging protection UAV

Song, Zhihua — 2025-12-14T00:00:00Z

Journal of Measurements in Engineering, Vol. 14, Issue 1, 2026, p. 1-20.
Zhihua Song, Heng Zhang, Qianwei Zhang, Lichao Liu
Aiming at the problem that the traditional plant protection UAV has poor droplet penetration due to the tall plants and overlapping leaves after the closure of maize in the middle and late stages, which leads to the difficulty of disease and pest control, this study combined the thermal atomization technology of pulsed smoke machine with the efficient plant protection UAV to design a smoke plant protection UAV that can meet the disease and pest control of maize and other densely planted tall stalk crops in the middle and late stages. A computational fluid dynamics method based on the Lattice Boltzmann Method (LBM) was used to numerically simulate the downwash airflow field of a smoke plant protection UAV (six-rotor) to explore the variation characteristics of the rotor flow field in different flight directions, at different flight speeds and at different flight times. The results show that: When the flight height of the UAV is 4 m and the flight speed is 2-5 m/s, with the increase of the speed, two distinct tail vortices gradually form behind the fuselage, and the height and diffusion distance of the tail vortices also increase with the increase of the speed. In addition, with the increase of the flight speed, the amount of droplets drifting up and to both sides will increase, and the intensity of the left side is greater than that of the right side. This can guide the nozzle position adjustment. The droplet deposition and diffusion distribution data of the smoke plant protection UAV were measured without plant cover through field test, The results showed that the median diameter of the droplet volume at each test point was about 50 μm, and the particle size range of the droplets met the requirements for the prevention and control of plant diseases and insect pests. The spatial distribution of droplets increased from top to bottom, and the trend of each layer in the horizontal direction was about the same, and at the same height and spray flow, the droplet particle size and sedimentation decreased with the increase of flight speed.

Numerical simulation study on safety thickness of boundary pillar and mining-induced stability during open-pit to underground transition

Wang, Feifei — 2025-12-14T00:00:00Z

Journal of Measurements in Engineering, Vol. 14, Issue 1, 2026, p. 151-165.
Feifei Wang, Anmin Jiang, Kuan Wu, Zhenjun Cui, Qi Yang, Yangbing Xue
To investigate the safe thickness and mining-induced stability of boundary pillars during the transition from open-pit to underground mining, a specific open-pit to underground mining project was selected as the research background. A three-dimensional fine-scale model of the mining area was constructed using coupled modeling technology with MIDAS-GTS/NX and FLAC3D. The deformation and displacement, stress distribution, and evolution of plastic zones of the open-pit slope, underground stope, and isolation pillars were studied under the full lifecycle conditions of the mining area. The stability of the open-pit slope, underground stope, and boundary pillars was clarified. The results showed that backfilling and remediation of the open-pit slope can effectively prevent slope disasters. The untreated open-pit slope exhibited deformation and displacement, with a maximum displacement of 1.0 cm to 1.2 cm. The maximum compressive stress in the open-pit area was 1.5 MPa to 3 MPa, and the maximum tensile stress was 0.24 MPa to 0.34 MPa, indicating a low likelihood of tensile failure in the surrounding rock. No plastic zone distribution was observed on the slope of No. 2 open-pit, while localized plastic zones were present in the backfilled waste rock of No. 1 open-pit. The maximum deformation and displacement of the surrounding rock in the underground stope were 7 mm to 9 mm, occurring at the stope roof. The maximum compressive stress in the underground stope area was 2 MPa to 4.5 MPa, and the likelihood of compressive failure in the surrounding rock was low. Localized plastic zones were observed in the stope fill, but no breakthrough occurred between adjacent stopes. The deformation and displacement of the isolation pillars were minimal, below the determination criteria. An isolation layer of 45 m between the open-pit and underground areas effectively isolated the mining-induced disturbances from underground operations, preventing the impact of underground mining activities on the upper open-pit area. The slopes of No. 1 and No. 2 open-pits are currently stable. Underground mining has not yet had a significant impact on the open-pit area, and the underground mined-out areas are in a stable state.

Research and design of a portable electrolyte conductivity meter based on eddy current effect

Wu, Shuai — 2025-12-14T00:00:00Z

Journal of Measurements in Engineering, Vol. 14, Issue 1, 2026, p. 166-182.
Shuai Wu, Zhe Huang, Jianping Zhu, Chunfeng Lv
A non-contact portable electrolyte solution conductivity detection method based on the eddy current effect is proposed to overcome the issues of electrode polarization and susceptibility to contamination inherent in traditional contact measurement methods for electrolyte solution conductivity. Initially, the analysis focuses on the PCB planar coil to investigate the impact of geometric parameters such as coil line width, spacing, and number of turns on detection sensitivity, aiming to determine the optimal parameter combination. Subsequently, the hardware system integrates the LDC1001 inductance-digital converter and MCU architecture with a built-in digital communication interface and display module. This setup enables conductivity measurement and data visualization within the 1-10 S/m range. The system calculates the electrolyte solution conductivity by monitoring changes in coil inductance and parallel resistance. Experiments are conducted using solutions of varying conductivity. The results confirm the feasibility of determining electrolyte solution conductivity based on variations in coil inductance and resistance. Through temperature characteristic and anti-interference experiments, the system's stability, effective temperature compensation function, and robustness against interference are demonstrated. The relative error in measurements is found to be less than 2.3 %, with a temperature compensation error below 1 %. The entire system design is realized through PCB design. In comparison with conventional electrode conductivity meters, this method enables non-contact measurements, eliminating issues related to electrode polarization and contamination. Additional benefits include flexible design, real-time display of measurement results, and portability.

Research on chatter monitoring of ultrasonic milling of thin-walled parts based on ICS-CNN network

Zhao, Na — 2025-12-14T00:00:00Z

Journal of Measurements in Engineering, Vol. 14, Issue 1, 2026, p. 117-135.
Na Zhao, Chao Zhang, Hui Yang, Wei Du, Futao Liu, Jiaming Li, Guanzhong Wu, Changfu Liu, Min Xia
In modern industry, thin-walled components have become critical elements in high-end manufacturing due to their unique structure and lightweight properties. However, their production process is prone to chatter, which severely impacts machining quality and efficiency. Although ultrasonic vibration grinding technology can partially suppress chatter, the issue remains unresolved. To address this, this paper proposes an online chatter monitoring method based on an improved convolutional neural network (ICS-CNN) and the Sparrow Optimization Algorithm (SSA). This approach enhances key information capture through multi-scale feature extraction and attention mechanisms, while incorporating residual connections and a feature pyramid structure to strengthen the model's ability to identify subtle chatter characteristics. Input signals undergo frequency domain analysis and filtering to improve data quality. The SSA algorithm is further employed to optimise network parameters, constructing the SSA-ICS-CNN intelligent monitoring model. Experimental results demonstrate an identification accuracy of 98.37 % with a decision time of merely 147 milliseconds, while visualisation techniques validate its discrimination precision. Compared to conventional convolutional neural networks, this approach achieves significant improvements in both recognition accuracy and response speed, effectively overcoming limitations inherent in traditional methods reliant on manual feature extraction and dynamic response delays.

Visual SLAM with motion consistency-constrained dynamic feature elimination

Zhou, Shan — 2025-12-14T00:00:00Z

Journal of Measurements in Engineering, Vol. 14, Issue 1, 2026, p. 72-87.
Shan Zhou, Shuangfeng Wei, Shangxing Wang, Ming Guo, Jianghong Zhao
Traditional visual SLAM methods are built on the strong assumption that the system operates in static environments, with limited consideration of moving objects. This assumption often leads to significant performance degradation when dynamic elements are present. To mitigate the impact of moving objects and enhance both localization accuracy and mapping quality, we propose a visual SLAM framework that explicitly removes dynamic object interference from the visual odometry and mapping modules. First, we refine the data association process in visual odometry by introducing motion consistency constraints, which reduce incorrect feature matches and thereby improve pose estimation accuracy. At the same time, depth information from RGB-D sensors is used to validate potentially dynamic feature points. Second, within the mapping module, we formulate keyframe selection as a vertex cover problem to ensure the local representativeness of keyframes. This approach not only reduces mapping artifacts but also enables the comprehensive detection and removal of dynamic objects. Finally, experiments conducted on the TUM RGB-D dataset demonstrate that our system achieves higher accuracy, robustness, and stability compared to baseline methods.

A comparative study of nano-SiO2 and nano-Al2O3 on the improvement of concrete properties under low temperature curing conditions (5 °C)

Wang, Yapeng — 2025-12-15T00:00:00Z

Journal of Measurements in Engineering, Vol. 14, Issue 1, 2026, p. 21-38.
Yapeng Wang, Guoyu Li, Chunqing Li, Jizhong Gan, Dun Chen, Hang Zhang, Miao Wang, Xu Wang, Yan Zhang, Liyun Tang
This study systematically investigates the efficacy of nano-silica (NS) and nano-alumina (NA) in mitigating the detrimental effects of low-temperature curing (5 °C) on concrete performance through a multi-scale experimental approach. Macroscopic tests revealed that an incorporation of 2 % NS or 1 % NA not only optimized compressive strength under standard curing but also effectively counteracted the strength reduction induced by low-temperature curing. Microstructural analysis using Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and Mercury Intrusion Porosimetry (MIP) revealed that both nanomaterials improve performance by densifying the pore system, speeding up the hydration process, and encouraging the generation of more calcium silicate hydrate (C-S-H) at the expense of calcium hydroxide (CH). Notably, 1 % NA yielded superior microstructural improvement compared to 2 % NS, achieving comparable mechanical enhancement at a 50 % lower dosage. The findings indicate that nano-alumina, owing to its higher efficiency and cost-effectiveness, presents an ideal admixture for producing durable concrete in cold-region applications.

Application of crested porcupine optimized-back propagation neural network under feature dimensionality reduction for rolling bearing fault diagnosis

Li, Qiang — 2025-12-20T00:00:00Z

Journal of Measurements in Engineering, Vol. 14, Issue 1, 2026, p. 183-212.
Qiang Li, Rundong Zhou, Xinyu Zhai, Xiancong Wu
To address the demands for precision and generalization in fault diagnosis of rolling bearings within resource-limited industrial settings, an intelligent diagnostic model utilizing Mean Impact Value (MIV), Crested porcupine optimizer (CPO) algorithm, and Backpropagation Neural Network (BPNN) (MCB-Net) is proposed. First, MIV ranks and filters features based on feature significance, thereby diminishing input dimensionality and enhancing model interpretability. Second, the CPO technique is implemented to improve BPNN parameters, thereby improving global search capabilities and expediting convergence, and addressing the conventional BPNN’s propensity to become trapped in local optima. Finally, MCB-Net was assessed utilizing rolling bearing fault datasets from Case Western Reserve University and Southeast University. Experimental results indicate that MCB-Net surpasses 97 % classification accuracy on three distinct datasets, exhibiting minimum performance variability compared to other approaches, confirming the model's efficacy and practicality.

Research on the influence of pile diameter on the pile-anchor support system for deep foundation pit adjacent to the existing structure

Liu, Sanxian — 2026-01-14T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Sanxian Liu, Yuchen Liu, Zhengfu Liu, Yongqing Zeng, Zuhui Long, Qiang Yin, Xiaohong Liu
The excavation of complex deep foundation pit poses significant risks to the safety of adjacent existing structures. To investigate the force and deformation characteristics of deep foundation pit, this study focuses on the pile-anchor support system of a deep foundation pit for a third-line ship lock on the Xiangjiang River. Model tests and numerical modeling analyses were conducted to evaluate the influence of pile diameters (1.0 m ≤d≤ 2.0 m) on the force and deformation characteristics of pile-anchor support system in deep foundation pit adjacent to buildings. The results indicate the following: (1) The variation pattern of horizontal displacement at the top of pile in the model test is consistent with that observed in the numerical simulation. The bending moment of the pile body exhibits an “S”-shaped distribution, confirming the reliability of numerical model. (2) The horizontal displacement curve of pile body presents a “bulging belly” shape, with smaller displacements at the top and bottom and larger displacements in the middle. The curvature of displacement decreases as the pile diameter increases. (3) Larger pile diameters result in greater bending moments in the pile body, smaller changes in the positions of positive and negative bending moment extremes, and reduced surface settlement. (4) The uplift at the bottom of foundation pit initially decreases and then increases, forming a “hooked” curve. The influence of pile diameter on bottom uplift is relatively minor. (5) Larger pile diameters promote a transition in the active failure mode from semi-infinite soil to finite soil, with d= 1.5 m serving as the critical value for finite soil conditions. (6) Based on economic considerations, d= 1.5 m is determined to be the optimal pile diameter. (7) The active deformation sliding surface of finite soil behind piles is a curve or polyline that is higher than the heel of wall and returns to the adjacent buildings. This study provides valuable experimental data for investigating finite soil deformation behind piles in the pile-anchor support system for deep foundation pit, offering both theoretical significance and practical engineering value.

Analysis of environmental driving mechanisms for vertical surface deformation in the permafrost section of the Qinghai-Tibet engineering corridor, China

Du, Qingsong — 2026-01-14T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Qingsong Du, Anhua Xu, Fei Wang, Huimin Luo, Shunshun Qi
Permafrost in the Qinghai-Tibet Plateau is highly susceptible due to thawing and degradation under the climate warming and extreme warming events, which can trigger surface subsidence and uplift phenomena. This study investigates the vertical surface deformation along the Qinghai-Tibet Engineering Corridor (QTEC) based on the dataset derived from interferometric synthetic aperture radar (InSAR) processing of the ascending and descending Sentinel-1 datasets. Using the geographical detector method, 24 potential factors influencing deformation was selected, including climatic, topographic, soil, hydrological, vegetation index, and cryospheric indicators to explore the driving mechanisms according to the q-value of factor detection processing. Results indicate that climatic factors (mean annual temperature and precipitation) and permafrost-related parameters (surface frost number, freezing index, thawing index) are the primary drivers of vertical deformation, with the q-value greater than 0.095. Topographic parameters (latitude, elevation, topographic relief, slope, longitude) also significantly influence deformation with the q-value between 0.054 and 0.086, followed by the east-west deformation rate and soil organic matter content with the q-value at 0.052, while other factors with the q-value less than 0.05. This study elucidates the intrinsic mechanisms driving surface subsidence and uplift along the QTEC, providing a theoretical foundation for the construction of future infrastructure projects and the maintenance of existing engineering facilities in permafrost regions.

Wide-band high-voltage cable current wireless measurement device based on TMR

Xiangyu, Zou — 2026-01-17T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Zou Xiangyu, Zhou Yunjie, Li Hai, He Yang, Wang Xiaodi, Yang Shuting
Wide-band electrical data contains abundant fault transient characteristics, but traditional transformers are difficult to accurately capture wide-band transient signals due to bandwidth limitations. To address this issue, this paper innovatively proposes a non-intrusive current measurement scheme based on an accurate time scale and develops a prototype device. A Tunnel Magnetoresistance (TMR) chip is used to detect the magnetic induction intensity generated by cable current, the ratio coefficient is derived from the spatial position of the high-voltage cable and the sensor, and data recording and real-time display are realized by a microprocessor. A low-noise adjustable gain sensing circuit and a transient signal wireless acquisition module are designed to improve the wide-band signal sensing capability; a mathematical model of the sensor installation position is established to achieve accurate reconstruction of the primary current. A validation platform is built to conduct measurement tests of Direct Current (DC), Industrial Frequency (IF), and transient processes, and a comparative experiment of ground fault current is carried out in a 110 kV high-voltage cable. The study identifies the key factors affecting the measurement accuracy of magnetoresistive sensors, and the experiments show that the measurement errors of DC and IF are controlled within 1 %, and the measurement errors of high-frequency signals do not exceed 3 %.This device adopts a combined power supply of solar cells and current transformer online power supply, which can provide up to 10 W of electrical power, with a lithium-ion polymer battery integrated inside as the energy storage module; the instrumentation op-amp is composed of three discrete operational amplifiers to meet the high bandwidth requirement for wide-band signal measurement.

A novel wind turbine fault diagnosis method based on improved TFMST and DSC-CNN-GRU model

Liu, Wenyi — 2026-02-06T00:00:00Z

Journal of Measurements in Engineering, Vol. 14, Issue 1, 2026, p. 88-116.
Wenyi Liu, Tongming Jian, Lei Meng, Di Song, Jianbin Cao
In wind turbines, rotating components serve as critical parts and are also prone to failures. The fault signals of wind turbines represent typical non-stationary and nonlinear signals susceptible to noise interference. Existing time-frequency analysis methods exhibit insufficient energy concentration when extracting time-varying non-stationary fault features, making feature extraction from signals more challenging. The primary drawbacks of single-data fault diagnosis methods lie in their limited information scope, poor robustness, lack of redundancy and fault tolerance, and difficulty in handling complex or multi-dimensional fault patterns. To address these issues, this paper proposed a model based on Improved TFMST and DSC-CNN-GRU. Firstly, the original Time-Frequency-Multisqueezing Transform (TFMST) technique was enhanced by optimizing its window function, introducing multi-scale adaptive thresholding to improve robustness, and relaxing the curvature criterion to enhance feature sensitivity. Furthermore, eps protection was incorporated throughout the algorithm to ensure numerical stability. Secondly, two datasets were constructed: one comprising two-dimensional data derived from the improved TFMST and the other containing one-dimensional raw data. Subsequently, a dual-input DSC-CNN-GRU model was developed, and both datasets were fed into it. Notably, the proposed model adopts a lightweight design. Finally, information from both data branches is fused and delivered to the classifier for the fault diagnosis task. To demonstrate the effectiveness of the proposed method, comparisons with other relevant methods were conducted on various datasets, indicating that the proposed method achieved desirable fault diagnosis accuracy.

Dual-source bridge circuits as signal converters for sensors of interrelated quantities - review of state of the art and directions of new research

Warsza, Zygmunt Lech — 2026-02-06T00:00:00Z

Journal of Measurements in Engineering, Vol. 14, Issue 1, 2026, p. 136-150.
Zygmunt Lech Warsza, Adam Idzkowski
The article discusses the fundamental dependencies and provides an example of a new type of signal converter for physical quantities that are related in the tested object. The review consolidates a relatively niche but critical area: dual-source bridge circuits, which are often scattered across instrumentation and sensor literature. Three key objectives are as follows: to systematically review dual-source bridge circuits used as signal converters, particularly in the context of sensors that measure interrelated physical quantities (e.g., strain and temperature); to analyze existing circuit topologies and conversion principles used in such systems; and to assess the applicability, limitations, and performance of various bridge configurations across different sensor applications. Performance metrics like sensitivity, linearity, and temperature compensation are often superior in dual-source setups when compared to single-source designs. Signal processing and calibration complexity can be reduced at the hardware level using these bridge configurations. Several circuit implementations offer trade-offs between complexity, power consumption, and accuracy. The literature review and bibliographic data on dual-source circuits, experimental studies, and prototype designs of dual-current DC bridges as prototype transducers of signals of associated quantities and their application are included. Further functions of these circuits in future systems are outlined.

Estimation of vehicle state based on improved dual layer UKF

Wang, Qianqian — 2026-02-06T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Qianqian Wang, Yingjie Liu, Dawei Cui
In order to address the issue of lower estimation accuracy of traditional methods, an adaptive dual layer unscented Kalman filter algorithm (ADLUKF) is proposed, which combines the dual layer unscented Kalman filter (DLUKF) with an improved Sage-Husa algorithm to estimate the states and reduce the error in vehicle driving state estimation. The Carsim and Matlab/Simulink for joint simulation is applied and real vehicle test is established to verify the effectiveness of the estimator, and compare it with the Unscented Kalman Filter (UKF) algorithm. The results indicate that the ADLUKF algorithm can improve the estimation accuracy of vehicle estimation effectively.

Voltage optimization control for small hydropower cluster connected to distribution power network in different spatiotemporal scenarios

Ren, Tinghao — 2026-02-07T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Tinghao Ren, Qican Dai, Jun Cai, Yiming Fan, Yizhao Bao, Ying Lu
China is vigorously developing distributed small hydropower in rural areas. However, the large-scale integration of small hydropower into distribution networks causes voltage fluctuations and over-limits due to seasonal variations in power output. To ensure the safe and stable operation of the distribution network, a voltage optimization strategy based on the adaptive shrinkage factor particle swarm optimization (ASCF-PSO) algorithm is proposed. The strategy involves dividing small hydropower into clusters and coordinating the optimization of reactive power compensation devices and cluster output, with the goal of minimizing node voltage deviation and network loss under different spatiotemporal scenarios. Simulations conducted on a 33-node system using actual output data from multiple small hydropower stations in the Meijiang River Basin. It shows that ASCF-PSO can keep all node voltages within the normal range and improve the rationality of power distribution. This study provides an effective solution for the safe and stable operation of distribution networks with high penetration of small hydropower.

Calculation and prediction of seepage from regulated reservoir in mudstone without seepage control measures

Niu, Jiayong — 2026-02-07T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Jiayong Niu, Shuai Zhang, Taiyun Li, Lixin Zhang
Correctly assessing seepage amount of the regulated reservoir is vital to water resources efficlent utilization and engineering safety. A regulated reservoir in mudstone area without seepage control measures was used as the engineering prototype. Based on the mutual verification of field monitoring and numerical simulation, the estimation formula of the seepage amount of the regulated reservoir was determined. In addition, the sensitivity analysis of the factors affecting the seepage amount of the regulated reservoir was carried out, and the seepage prediction models were established based on the different optimization algorithms. The research results indicate that the regulated reservoir without lying geomembranes exists in a certain amount of seepage every day, accounting for about 7 to 10 % of the total reservoir capacity. The sensitivity ranking of influencing factors in descending order is as follows: reservoir bottom width, water depth, hydraulic conductivity, and saturated volumetric water content. The prediction accuracy of Bayesian regression is significantly better than that of traditional regression models under small training samples. This research approach provides a highly accurate and strongly robust solution to the small sample engineering prediction problem.

Research on the remaining life assessment method of corrosive steel pipe structures in substations based on thickness measurement and testing

Wan, Yu — 2026-02-07T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Yu Wan, Junpeng Ma, Chi Wang
Strength degradation of steel structures due to corrosion is a significant durability concern globally, particularly in industrial environments where exposure to harsh conditions accelerates material deterioration. This study investigates the impact of long-term atmospheric corrosion on the mechanical properties and load-bearing capacity of circular hollow section (CHS) steel columns. The specimens dismantled from a substation structure exposed to an urban industrial environment for 30 years were subjected to tensile and axial compression experiments to analyze corrosion-induced deterioration. This research explores the correlation between corrosion rate and degradation of mechanical properties, such as yield strength, ultimate strength, and elasticity. A novel Corrosion-Mechanical Interaction Model is proposed to predict the remaining service life of corroded steel structures by integrating the effects of corrosion on these critical properties. Experimental results revealed a significant reduction in yield and ultimate strength due to uniform corrosion, with a direct linear relationship between the bearing capacity degradation and material loss. This study provides a crucial tool for engineers and infrastructure planners in managing the lifecycle of steel structures exposed to harsh environmental conditions.

Adaptive integral sliding mode control for attitude tracking of 6-DOF electro-hydraulic shaking tables

Liu, Yuan — 2026-02-07T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Yuan Liu, Lianpeng Zhang, Ruichen Wang, Litong Lyu, Jie Feng, Guangtao Ma
Accurate attitude tracking of six degree of freedom electrohydraulic shaking tables (EHSTs) is restricted by parameter uncertainty, nonlinearity, strong coupling, and external disturbances. Existing sliding mode control schemes are limited by fixed switching gains and the absence of integral compensation, which restrict steady-state accuracy and dynamic adaptability under nonlinear hydraulic effects and multi-axis coupling. An Adaptive Integral Sliding Mode Control (AISMC) scheme is developed to address these factors through two coordinated elements: an integral sliding surface that removes steady state deviation caused by static disturbances such as servo valve dead zones and hydraulic leakage, and an adaptive switching gain that regulates the reaching dynamics online without reliance on conservative bounds; a decay term in the gain update restrains parameter drift and keeps the adaptation bounded. Lyapunov analysis establishes closed loop stability and finite time convergence of the tracking error under bounded uncertainties and excitations. Simulation studies on a six degree of freedom EHST with a broadband random reference (0.1-10 Hz, 10 mm) compare AISMC with Sliding Mode Control (SMC) along X, Y, and Z. Pose tracking shows consistent gains, with the maximum value reduced by about 11.5-11.9 % and the root mean square (RMS) reduced by about 34.9-35.1 %. Pose error decreases from 0.392-0.396 mm to 0.035-0.036 mm in maximum value and from 0.175-0.177 mm to 0.015-0.016 mm in RMS. Acceleration tracking under AISMC approaches the reference in X and Z and improves in Y, while acceleration error decreases by about 83.5 % in X and Y and about 88 % in Z. The results indicate higher control precision, smoother transients with reduced chattering, and robust multi axis coordination suitable for practical vibration testing applications.

An explainable hybrid deep learning framework for offshore wind speed forecasting

Liu, Peifang — 2026-02-07T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Peifang Liu, Jiang Guo, Ye Zou, Nana Gao
Interpretability has become a critical requirement in modern deep learning applications for renewable energy forecasting, especially in complex and safety-critical contexts such as offshore wind power systems. To simultaneously improve predictive accuracy and model transparency, this study proposes an explainable hybrid deep learning framework – VFE-IVYA-CNN-BiGRU – for offshore wind speed forecasting. The model begins with feature selection via Pearson Correlation (PCs) to identify the most relevant meteorological variables from a full set of candidates, thereby enhancing input quality and reducing redundancy. The selected features are then passed through a robust preprocessing module (VFE), which integrates Variational Mode Decomposition (VMD) and Fuzzy Entropy (FE). VMD decomposes the original wind speed sequence into intrinsic mode functions (IMFs), capturing multi-scale temporal structures, while FE quantifies the complexity of each IMF to filter out noise-dominated components. The reconstructed sub-sequences are first processed through a Convolutional Neural Network (CNN) to capture temporal local dependencies, and then passed into a Bidirectional Gated Recurrent Unit (BiGRU), which effectively learns time dependencies in both forward and backward directions. To further enhance model performance, the Ivy Algorithm (IVYA) is employed to optimize hyperparameters adaptively, improving convergence and generalization. To improve interpretability, SHapley Additive exPlanations (SHAP) are utilized to quantify the contribution of each meteorological feature to the model's output, revealing both dominant drivers (e.g., gust speed) and interaction patterns across seasons. The proposed framework is evaluated using seasonal offshore wind datasets (spring, summer, autumn, and winter) sourced from a wind power site along the Guangdong coastline, China, and contrasted with six leading benchmark models. Empirical findings reveal that the proposed VFE-IVYA-CNN-BiGRU consistently outperforms existing methods in terms of accuracy, robustness, and interpretability. The integration of SHAP-based explanations ensures model transparency, making the approach a reliable tool for intelligent control and decision support in offshore wind farm operations.

Strawberry ripeness detection based on improved lightweight network in cluttered field environment

Wu, Renyuan — 2026-02-11T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Renyuan Wu, Xinyi Chen, Yuanmeng Wang, Ruoqi Wu, Shuangli Wang
To address inaccurate strawberry recognition caused by cluttered field environments such as varying illumination, occlusion and uneven distribution, an improved lightweight model YOLOv7-SSC for strawberry ripeness detection was proposed. First, the backbone network of YOLOv7 is replaced with ShuffleNetV2, a lightweight feature extraction network, to significantly reduce the number of model parameters. Second, the lightweight network Slim-neck is used as the neck structure to reduce model complexity while preserving high precision. Finally, the Content Perception Feature Recombination (CARAFE) upsampling is used to enlarge receptive field in the feature fusion network and fully leverage semantic information. Moreover, the pictures of the strawberry dataset with three common conditions (unripe strawberry, near ripe strawberry and ripe strawberry) were collected in real picking environment. The experimental results show that compared to the original YOLOv7 model, the improved model parameters are reduced by 69.0 %, the floating point number is decreased by 79.4 %, and the accuracy rate reaches 99.6 %. These results demonstrate that YOLOv7-SSC model can achieve fast recognition of strawberry maturity while maintaining high precision, making it more suitable for small target detection in complex field compared with other algorithms.

Measurement of gravitational constan t G between 30 t steel plate and 5 kg iron ball

Hu, Qinggui — 2026-02-15T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Qinggui Hu, Xinlong Zhang
In 2008, we proposed the “New Experimental Scheme for Measuring the Gravitational Constant G between Large-Mass Objects”. In 2021, this scheme was implemented, yielding a new value of G: 9.09×10-9 N·m2/kg2. By contrast, the currently internationally recognized value is 6.67259×10-11 N·m2/kg2. The discrepancy between these two values is so significant that it cannot be adequately explained by traditional theories. To address this issue, a permanent experimental platform was established in early June 2023 at the new campus of Neijiang Normal University, enabling a repeat of the experiment. Based on this platform, an additional experimental measurement was conducted, resulting in a new G value of 7.3827302×10-10 N·m2/kg2. Subsequently, we analyzed the causes of the significant difference between the latest measured value and the first one. And we also performed a comparative analysis between the new experiment and the traditional Cavendish torsion balance experiment. The results demonstrated that the new experimental system features fewer error sources and higher stability. These new G values suggest that the gravitational constant may not be a true constant; instead, it could be related to factors such as the shape and density of objects etc.

Study on the effect of laser welding rate on Q235 weld quality

Liu, Pinxiao — 2026-02-18T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Pinxiao Liu, Deping Liu, Guangyu Cai, Jisen Yan, Zhenyang Liu, Haonan Liu, Kai Li
Q235 low-carbon steel is widely used in construction, bridges, machinery manufacturing and other fields because of its good plasticity and weldability. To explore the optimized application of laser welding technology in Q235 steel processing, this study used an EEF-LWM-1500 welding machine and systematically investigated the effect of different welding speeds on the joint quality of Q235 steel under the condition of a constant laser power of 1200 W. The weld quality was systematically evaluated through multiple characterization methods, including metallographic microscopy, scanning electron microscopy, tensile testing, microhardness measurement, electrochemical corrosion tests, and friction-wear experiments. Experimental results indicated that a welding speed of 120 mm/min caused burn-through in the steel plate, while increasing the speed to 240 mm/min achieved good joint formability. Notably, the heat-affected zone gradually decreased with higher welding speeds. The hardness and tensile strength of the laser-welded zone both exceeded those of the base material. Specifically, the average hardness of the weld zone peaked at 177.56 HV when the welding speed was 500 mm/min. Below this speed, hardness increased with rising welding speed, while it tended to decrease above 500 mm/min. Tensile strength showed a similar trend, with the highest value of 424.98 MPa and the lowest value of 421.94 MPa. Electrochemical corrosion tests revealed that the welded joint at 500 mm/min exhibited the smallest self-corrosion current density (1.41×10⁻5 A/cm2) and the largest capacitive arc radius, confirming optimal corrosion resistance. This study identifies that the optimal laser welding speed for Q235 steel is 500 mm/min, which provides important technical references for improving the welding quality and production efficiency of Q235 steel in practical production and expanding its application scope.

Deformation mechanisms of open-pit high-steep slopes controlled by a fault: An integrated remote sensing, field investigation and numerical simulation study

Jiang, Anmin — 2026-02-19T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Anmin Jiang, Daobing Zhang, Feifei Wang, Yanchen Dong, Huadong Yin, Huzhi Wang, Sheng Zhang, Zhicheng Duan, Shaoxiang Xie
The engineering disasters caused by fault structural zones represent a persistent challenge in geotechnical engineering. This study employs an integrated approach, combining multi-temporal remote sensing, field investigation, and numerical simulation, to investigate the deformation mechanisms and stability of an open-pit slope controlled by the F15 fault. The results demonstrate that the collapse of the northern slope results from the combined effect of the internal F15 fault structure and external unloading due to underground mining. Remote sensing imagery reveals a four-stage failure process: initial rock deformation, local landslides, local surface subsidence, and final surface subsidence. Significantly, the F15 structural zone alters the deformation trend of the upper slope, channeling displacement towards the eastern valley, with a maximum simulated displacement of 5.4 cm. The safety factor of the slope, calculated using the strength reduction method, is 1.45, and the potential sliding surface is identified as the F15 structural zone. While the slope is currently stable, the limited safety redundancy and observed local landslides highlight the need for targeted monitoring and reinforcement of the fault zone. This study provides data-supported insights for the prevention and control of geological disasters in similar fault-controlled mining slopes.

Rapid extraction of forest burned areas using Sentinel-2 satellite imagery on the PIE-engine platform

Mao, Lijun — 2026-04-16T00:00:00Z

Journal of Measurements in Engineering, (in Press).
Lijun Mao, Tianxiang Zhou, Chenghui Nan
The increasing of forest fires highlights the importance of rapidly and accurately quantifying the burned areas, which is crucial evidence for determining the cause of fire and assigning responsibility. This study develops an operational tool based on the cloud-computing abilities of the PIE-Engine platform and Sentinel-2 satellite imagery. The burned areas are rapidly extracted by applying differential spectral indices such as dNDVI, dNBR and dNBR2 using the adaptive thresholds determined by the Otsu algorithm. Compared with field investigations, the accuracy of using a wildfire case in Liuhe Village, Hubei Province is high. The findings indicate that dNBR can achieve an area accuracy of 98.22 % and a pixel-level F1-score of 0.94, significantly higher than dNDVI. Although the accuracy of the random forest model is slightly high, the dNBR based method achieves an excellent balance between computational efficiency (about 2 s of processing time) and accuracy. In addition, we have developed a user-friendly web application that allows for custom parameter settings and visualizes results as a quantitative burned area map. This tool only requires a web browser, greatly reducing the technical barriers of remote sensing applications and providing a transparent, efficient, and accessible solution for supporting forest fire investigation and emergency response.