Measurement Science and Technology

Medical robotics holds transformative potential for healthcare. Robots excel in tasks requiring precision, including surgery and minimally invasive interventions, and they can enhance diagnostics through improved automated imaging techniques. Despite the application potentials, the adoption of robotics still faces obstacles, such as high costs, technological limitations, regulatory issues, and concerns about patient safety and data security. This roadmap, authored by an international team of experts, critically assesses the state of medical robotics, highlighting existing challenges and emphasizing the need for novel research contributions to improve patient care and clinical outcomes. It explores advancements in machine learning, highlighting the importance of trustworthiness and interpretability in robotics, the development of soft robotics for surgical and rehabilitation applications, and the role of image-guided robotic systems in diagnostics and therapy. Mini, micro, and nano robotics for surgical interventions, as well as rehabilitation and assistive robots, are also discussed. Furthermore, the roadmap addresses service robots in healthcare, covering navigation, logistics, and telemedicine. For each of the topics addressed, current challenges and future directions to improve patient care through medical robotics are suggested.

Time-gated (TG) Raman spectroscopy (RS) has been shown to be an effective technical solution for the major problem whereby sample-induced fluorescence masks the Raman signal during spectral detection. Technical methods of fluorescence rejection have come a long way since the early implementations of large and expensive laboratory equipment, such as the optical Kerr gate. Today, more affordable small sized options are available. These improvements are largely due to advances in the production of spectroscopic and electronic components, leading to the reduction of device complexity and costs. An integral part of TG Raman spectroscopy is the temporally precise synchronization (picosecond range) between the pulsed laser excitation source and the sensitive and fast detector. The detector is able to collect the Raman signal during the short laser pulses, while fluorescence emission, which has a longer delay, is rejected during the detector dead-time. TG Raman is also resistant against ambient light as well as thermal emissions, due to its short measurement duty cycle.

In recent years, the focus in the study of ultra-sensitive and fast detectors has been on gated and intensified charge coupled devices (ICCDs), or on CMOS single-photon avalanche diode (SPAD) arrays, which are also suitable for performing TG RS. SPAD arrays have the advantage of being even more sensitive, with better temporal resolution compared to gated CCDs, and without the requirement for excessive detector cooling. This review aims to provide an overview of TG Raman from early to recent developments, its applications and extensions.

Particle image velocimetry (PIV) has become the chief experimental technique for velocity field measurements in fluid flows. The technique yields quantitative visualizations of the instantaneous flow patterns, which are typically used to support the development of phenomenological models for complex flows or for validation of numerical simulations. However, due to the complex relationship between measurement errors and experimental parameters, the quantification of the PIV uncertainty is far from being a trivial task and has often relied upon subjective considerations. Recognizing the importance of methodologies for the objective and reliable uncertainty quantification (UQ) of experimental data, several PIV-UQ approaches have been proposed in recent years that aim at the determination of objective uncertainty bounds in PIV measurements.

This topical review on PIV uncertainty quantification aims to provide the reader with an overview of error sources in PIV measurements and to inform them of the most up-to-date approaches for PIV uncertainty quantification and propagation. The paper first introduces the general definitions and classifications of measurement errors and uncertainties, following the guidelines of the International Organization for Standards (ISO) and of renowned books on the topic. Details on the main PIV error sources are given, considering the entire measurement chain from timing and synchronization of the data acquisition system, to illumination, mechanical properties of the tracer particles, imaging of those, analysis of the particle motion, data validation and reduction. The focus is on planar PIV experiments for the measurement of two- or three-component velocity fields.

Approaches for the quantification of the uncertainty of PIV data are discussed. Those are divided into a-priori UQ approaches, which provide a general figure for the uncertainty of PIV measurements, and a-posteriori UQ approaches, which are data-based and aim at quantifying the uncertainty of specific sets of data. The findings of a-priori PIV-UQ based on theoretical modelling of the measurement chain as well as on numerical or experimental assessments are discussed. The most up-to-date approaches for a-posteriori PIV-UQ are introduced, highlighting their capabilities and limitations.

As many PIV experiments aim at determining flow properties derived from the velocity fields (e.g. vorticity, time-average velocity, Reynolds stresses, pressure), the topic of PIV uncertainty propagation is tackled considering the recent investigations based on Taylor series and Monte Carlo methods. Finally, the uncertainty quantification of 3D velocity measurements by volumetric approaches (tomographic PIV and Lagrangian particle tracking) is discussed.

Traditional diagnostic methods often have insufficient accuracy and noise reduction, which leads to diagnostic errors. To address these issues, this paper proposes an advanced fault diagnosis model that combines the variational mode decomposition (VMD) improved by a Variable-Objective Search Whale Optimization Algorithm (VSWOA) with a Pelican Optimization (PO)-boosted Kernel Extreme Learning Machine (KELM) algorithm. The application of the method is shown here in the fault diagnosis of rolling bearings. The proposed VSWOA enhances the performance of VMD by incorporating a Sobol sequence, nonlinear time-varying factors, a multi-objective initial search strategy, and an elite Cauchy chaos mutation strategy, significantly improving noise reduction in vibration signals. Fault information is precisely extracted using waveform factors, sample entropy, and advanced composite multiscale fuzzy entropy, which enables effective feature screening and dimensionality reduction. The POA fine-tunes the KELM parameters, increasing the classification accuracy. The effectiveness of the model is verified through experimental evaluations using bearing data with injected Gaussian noise (from Case Western Reserve University) and the SpectraQuest datasets, where significant improvements in noise reduction and fault detection accuracy are achieved.

To address the challenge of measuring volumes of irregular objects, this paper proposes a volume measurement method based on 3D point cloud reconstruction. The point clouds of the object with multiple angles are obtained from an RGB-D camera mounted on a robotic arm, and then are reconstructed to form a whole complete point cloud to calculate the volume of the object. Firstly, the robotic arm is controlled to move to four angles for capturing the original point clouds of the target. Then, by using the rotation and translation matrices obtained from the calibration block pre-registration, the point clouds data from the four angles are fused and reconstructed. Subsequently, the issue of missing bottom point cloud data is addressed using a bottom-filling algorithm. Following this, the efficiency of the point cloud volume calculation algorithm is enhanced through the application of axis-aligned bounding box filtering. Finally, the reconstructed point cloud volume is calculated using a slicing algorithm that integrates 2D point cloud segmentation and point cloud sorting. Experimental results show that this method achieves a volume measurement accuracy of over 95% for irregular objects and exhibits good robustness.

A novel adaptive ensemble empirical feed-forward cascade stochastic resonance (AEEFCSR) method is proposed in this study for the challenges of detecting target signals from intense background noise. At first, we create an unsaturated piecewise self-adaptive variable-stable potential function to overcome the limitations of traditional potential functions. Subsequently, based on the foundation of a feed-forward cascaded stochastic resonance method, a novel weighted function and system architecture is created, which effectively addresses the issue of low-frequency noise enrichment through ensemble empirical mode decomposition. Lastly, inspired by the spider wasp algorithm and nutcracker optimization algorithm, the spider wasp nutcracker optimization algorithm is proposed to optimize the system parameters and overcome the problem of relying on manual experience. In this paper, to evaluate its performance, the output signal-to-noise ratio (SNR), spectral sub-peak difference, and time-domain recovery capability are used as evaluation metrics. The AEEFCSR method is demonstrated through theoretical analysis. To further illustrate the performance of the AEEFCSR method, Validate the adoption of multiple engineering datasets. The results show that compared with the compared algorithms, the output SNR of the AEEFCSR method is at least 6.2801 dB higher, the spectral subpeak difference is more than 0.25 higher, and the time-domain recovery effect is more excellent. In summary, the AEEFCSR method has great potential for weak signal detection in complex environments.

Image based three-dimensional (3D) particle tracking is currently the most widely used technique for volumetric velocity measurements. Inspecting the flow-field around an object is however, hampered by the latter, obstructing the view across it. In this study, the problem of measurement limitations due to the above is addressed. The present work builds upon the recent proposal from Wieneke and Rockstroh (2024 Meas. Sci. Technol. 35 055303), whereby the information of the occluded lines of sight can be incorporated into the particle tracking algorithm. The approach, however, necessitates methods that accurately evaluate the shape and position of the object within the measurement domain. Methods of object marking and the following 3D registration of a digital object model (CAD) are discussed. For the latter, the iterative closest point registration algorithm is adopted. The accuracy of object registration is evaluated by means of experiments, where marking approaches that include physical and optically projected markers are discussed and compared. Three objects with growing level of geometrical complexity are considered: a cube, a truncated wing and a scaled model of a sport cyclist. The registered CAD representations of the physical objects are included in aerodynamic experiments, and the flow field is measured by means of large-scale particle tracking using helium filled soap bubbles. Results indicate that object registration enables a correct reconstruction of flow tracers within regions otherwise affected by domain clipping as a consequence of obstructed camera lines-of-sight. Finally, the combined visualization of the object and the surrounding flow pattern offers means of insightful data inspection and interpretation, along with posing a basis for particle image velocimetry data assimilation at the fluid-solid interface.

Digital twinning is a rapidly growing area of research. Digital twins combine models and data to provide up-to-date information about the state of a system. They support reliable decision-making in fields such as structural monitoring and advanced manufacturing. The use of metrology data to update models in this way offers benefits in many areas, including metrology itself. The recent activities in digitalisation of metrology offer a great opportunity to make metrology data ‘twin-friendly’ and to incorporate digital twins into metrological processes. This paper discusses key features of digital twins that will inform their use in metrology and measurement, highlights the links between digital twins and virtual metrology, outlines what use metrology can make of digital twins and how metrology and measured data can support the use of digital twins, and suggests potential future developments that will maximise the benefits achieved.

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.

High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy. Deep-learning approaches have been shown suitable for such super-resolution tasks. However, a high number of high-resolution examples is needed, which may not be available for many cases. Moreover, the obtained predictions may lack in complying with the physical principles, e.g. mass and momentum conservation. Physics-informed deep learning provides frameworks for integrating data and physical laws for learning. In this study, we apply physics-informed neural networks (PINNs) for super-resolution of flow-field data both in time and space from a limited set of noisy measurements without having any high-resolution reference data. Our objective is to obtain a continuous solution of the problem, providing a physically-consistent prediction at any point in the solution domain. We demonstrate the applicability of PINNs for the super-resolution of flow-field data in time and space through three canonical cases: Burgers’ equation, two-dimensional vortex shedding behind a circular cylinder and the minimal turbulent channel flow. The robustness of the models is also investigated by adding synthetic Gaussian noise. Furthermore, we show the capabilities of PINNs to improve the resolution and reduce the noise in a real experimental dataset consisting of hot-wire-anemometry measurements. Our results show the adequate capabilities of PINNs in the context of data augmentation for experiments in fluid mechanics.

Initial alignment is one of the critical technologies for the smooth and effective operation of the strapdown inertial navigation system (SINS), and the alignment time and accuracy are the two deterministic indicators of SINS initial alignment performance. To address the issues of redundant alignment time and low accuracy in the initial alignment process, a new method utilizing system observations is proposed, which includes velocity errors, equivalent specific force outputs, and equivalent gyro angular velocity errors. System state equations and observation equations are established based on the fundamental principles and error characteristics of inertial sensors. Singular value decomposition was employed to calculate the singular values of each system’s observation matrix, and observability was determined through singular value analysis. Experimental results indicate that the inclusion of equivalent specific force outputs accelerates the convergence of the horizontal misalignment angle, while equivalent gyro angular velocity errors expedite the convergence of the azimuth misalignment angle. Proposed method with enhanced system observability improves alignment accuracy significantly, with horizontal accuracy increased by approximately 62% and vertical accuracy by about 42% compared to a filtering model using only velocity errors as observations.

In the European Union, temporal light artifacts (TLAs) have recently been regulated for mains-connected LED lamps and luminaires available on the market. However, the limitations are only on white LED sources. Colored LED sources are not under current regulation, although they are often used as components to create a source that looks like a white source to an observer. We present a measurement setup based on a hyperspectral camera to measure different types of luminaires simultaneously to quantify the spectral components of the TLA metrics. The camera has one-megapixel spatial resolution and can measure at 1000 different wavelengths with a bandwidth of 10 nm. The sampling frequency of the camera was extended from 100 frames per second (fps) to 1000 fps using triggering with a varied delay, and waveforms within an integration time of 1 s were captured. We demonstrate measurements of white LED lamps at wavelengths of 450 nm, 550 nm and 650 nm, and compare the results of the TLA metrics with other measurement setups. We observed that for one of the lamps, the hyperspectral measurements deviated only 3% from the reference values, while for the other lamp, the demonstrative measurement system did not have sufficient performance, which increased the deviations significantly.

The replacement of insulators for overhead contact systems (OCSs) is currently mainly carried out using a combination of machine detection and manual judgment, lacking quantitative analysis of the extent of the defect. To achieve quantitative analysis of the extent of insulator defects, this paper proposes a YOLOv8-ORSDCV algorithm. The proposed algorithm employs an oriented bounding box to effectively address the tilting of the detection object caused by variations in shooting angles. To solve the problem of missing key information in nighttime insulator detection due to extremely insufficient background light, the algorithm introduces the Content-aware Reassembly of FEature (CARAFE) module; the detection accuracy of the model for targets is effectively improved. To enhance the feature extraction ability of the model for insulators and their defect details, the feature pyramid shared conv module and C2f_diverse branch block module are constructed to improve the model’s understanding of and ability to analyze image content, as well as detection speed. Finally, the OpenCV module is introduced to achieve precise quantification of the defect contour area through filtering, edge detection, and contour extraction. The research results show that, compared to the YOLOv8n algorithm, the four performance indicators (mAP@0.5, mAP@0.5–0.95, insulator detection accuracy, and insulator defect detection accuracy) of the YOLOv8-ORSDCV algorithm have been improved by 2.3%, 11.1%, 1.6%, and 3%, respectively. In quantitative detection, the YOLOv8-ORSDCV algorithm achieves an insulation error of approximately 4.43% and a defect error of about 1.5%. Due to accurately locating the location of the insulator defect, a real-time quantitative display of the defect area is achieved. The research results of this paper provide technical support for the intelligent monitoring and maintenance of high-speed railway OCSs.

On-machine measurement (OMM) has emerged as a critical technology for in situ monitoring and machining error identification in impeller blade manufacturing. Non-vertical OMM offers significant advantages by further reducing probe posture adjustment frequency and enhancing inspection efficiency. However, the complex impeller structure and non-vertical OMM process result in highly time-consuming path planning. To address this issue, an efficient interference-free inspection path planning strategy is proposed in this paper. Firstly, the probe posture is discretized. And its feasibility is represented by a feasible matrix of probe posture (${\mathbf{FMPP}}$). To reduce the number of interference checks, the ranges of approaching direction and probe posture are constrained by physical limitations and machine workspace. Considering the similarity of adjacent ${\mathbf{FMPP}}$, a fast calculation algorithm is developed by iteratively calculating the feasibility of boundary pixels based on the known ${\mathbf{FMPP}}$. To avoid calculating ${\mathbf{FMPP}}s$ of all inspection points, measurable regions of each probe posture are established by ${\mathbf{FMPP}}s$ of a small number of uniformly distributed sampling points. Thus, feasible probe postures for any inspection point can be rapidly determined by identifying which probe posture’s measurable region contains it. Finally, the feasible region’s center pixel of the feasible matrix of approaching direction is selected as the optimal approaching direction. Compared to the exhaustive calculation method, the proposed strategy achieves a 95.0% reduction in path planning time. The feasibility is verified by the impeller blade OMM experiment.

To address limitations of single-source signals and single-view models for high-precision diagnosis of gearbox combined bearings in nuclear power circulating pumps, this study proposes a diagnostic method integrating the complementary representations of image- encoded frequency-domain information from multi-source signals and a dual-view model. First, the frequency-domain information of multi-source signals was encoded into feature matrices, which were then mapped to the RGB channels of true-color images, realizing the conversion from one-dimensional signals to color images. Based on this process, this paper proposes a multi-source signal image encoding method that integrates the frequency-domain fusion Symmetric position matrix and frequency-domain fusion Relative position matrix. Through dimensional concatenation and fusion of feature matrices from multi-source signals, the two generated types of image datasets exhibit information complementarity. Moreover, the proposed method takes image size as the sole variable, thereby avoiding the impact of multi-parameter signal processing on diagnostic results. Subsequently, based on the two types of generated image datasets, this study designs a Dual-view multi-loop cascaded residual neural network. The multi-loop cross-scale cascaded residual structure enhances the model’s capability to extract complex fault features, while the dual-view parallel input mechanism ensures the synchronous extraction and fusion of complementary image information. The proposed method achieved an average diagnostic accuracy of 99.68% on a self-built combined bearing test bench. Ablation experiments and noise robustness analysis proved the effectiveness of complementary image information and the robustness of the proposed method, while comparisons with other methods further highlighted its superiority. Finally, the method’s generalization capability was further validated on a self-collected gear dataset. Open-source code: https://github.com/JY-2024/A-Dual-View-Information-Complementary-Fault-Diagnosis-Method.git.

Metal additive manufacturing (MAM) presents unparalleled opportunities for fabricating complex and high-performance components. While achieving consistent part quality and process repeatability remains challenging. The temperature field is one of the dominant factors influencing the evolution of microstructure, distribution of residual stress, and mechanical properties during MAM. Therefore, it is significant to monitor and control the temperature field. In this review, the influences of the temperature field on the microstructure, residual stress, and mechanical performance are overviewed. The coupling mechanisms between thermal behavior and defect formation are explored. Secondly, a detailed review of the current state-of-the-art in-situ process monitoring techniques for the temperature field is provided. These techniques are evaluated for their capabilities and limitations in detecting defects. Thirdly, the application of machine learning (ML) algorithms in temperature monitoring and defect prediction based on thermal information during the MAM process is summarized. Finally, the advantages and current challenges—such as multiple sensors data fusion, physics-informed modeling, and ML models—are also discussed. This paper aims to provide a comprehensive overview of the precise and efficient monitoring of temperature field in MAM and equip researchers and industry professionals with a holistic understanding of the current capabilities, limitations, and future directions of in-situ process monitoring of temperature field during MAM.

As a critical component in industrial machinery systems, gearboxes demand robust fault diagnosis solutions to ensure operational safety, energy efficiency, and sustainable manufacturing practices. Deep learning (DL) has emerged as a transformative approach for intelligent fault identification, demonstrating superior capabilities in processing complex vibration signatures compared to conventional methods. This review systematically examines DL applications in gearbox diagnostics through dual perspectives of theoretical foundations and industrial implementations. Six principal DL architectures are critically analyzed, including their advanced variants optimized for mechanical signal processing. The study systematically compares these architectures across diagnostic capabilities, computational demands, and implementation constraints. This review identifies promising research directions to address the current challenges in gearbox fault diagnosis. It aims to establish strategic research pathways that bridge the existing gap between theoretical models and industrial requirements, thereby enhancing the predictive maintenance framework for next-generation intelligent manufacturing systems.

Medical robotics holds transformative potential for healthcare. Robots excel in tasks requiring precision, including surgery and minimally invasive interventions, and they can enhance diagnostics through improved automated imaging techniques. Despite the application potentials, the adoption of robotics still faces obstacles, such as high costs, technological limitations, regulatory issues, and concerns about patient safety and data security. This roadmap, authored by an international team of experts, critically assesses the state of medical robotics, highlighting existing challenges and emphasizing the need for novel research contributions to improve patient care and clinical outcomes. It explores advancements in machine learning, highlighting the importance of trustworthiness and interpretability in robotics, the development of soft robotics for surgical and rehabilitation applications, and the role of image-guided robotic systems in diagnostics and therapy. Mini, micro, and nano robotics for surgical interventions, as well as rehabilitation and assistive robots, are also discussed. Furthermore, the roadmap addresses service robots in healthcare, covering navigation, logistics, and telemedicine. For each of the topics addressed, current challenges and future directions to improve patient care through medical robotics are suggested.

The rapid development of novel materials and advanced devices, characterized by ultra-miniaturization and high-density integration, has underscored the significance of digital image correlation (DIC) techniques in multi-scale microscopy applications. This review offers a comprehensive assessment of DIC implementation across three principal microscopy platforms: optical microscopy, scanning microscopy, and transmission microscopy. The investigation focuses on several critical dimensions: (1) imaging modalities and their respective resolution capabilities, (2) advanced methodologies for speckle pattern preparation, and (3) practical applications in materials science and device characterization. Furthermore, this study critically evaluates the current technical limitations of DIC implementation across various length scales, proposing innovative solutions and offering strategic recommendations that substantially advance the development of microscopic deformation measurement techniques for DIC in emerging applications. Through a comprehensive analysis, the review establishes the boundaries of applicability and fundamental constraints for DIC-based deformation measurement techniques across various microscopy platforms. It provides researchers with essential guidelines and standardized frameworks for implementing DIC-based deformation analysis in diverse microscopic environments, thereby enabling more accurate and reliable strain characterization in advanced materials and devices.

The monitoring of wear and lubrication states is essential for ensuring the safe operation of mechanical systems. However, conventional techniques often suffer from delayed response and poor resistance to interference. In recent years, triboelectric phenomena—due to their high sensitivity to interfacial conditions—have emerged as a promising approach for real-time monitoring. This review systematically summarizes the fundamental mechanisms of triboelectrification and its recent advances and representative applications in monitoring wear and lubrication states. Four main charge transfer mechanisms—electron, ion, and material transfer, as well as thermoelectric effects—are introduced. The correlation between tribological behavior and triboelectric behavior is further examined based on the electron cloud overlap model. The paper further reviews signal acquisition methods and typical experimental studies, highlighting the relationship between triboelectric signals and wear or lubrication conditions. On the application front, triboelectrification-based monitoring has shown strong adaptability in representative scenarios such as polymer coatings, sealing structures, and rolling bearings. It holds significant promise for lubrication film failure warning, lubrication state identification, intelligent lubrication system development, and lubricant performance evaluation. Nonetheless, challenges remain in distinguishing among different charge generation mechanisms, interpreting complex triboelectric signals, and achieving practical deployment in industrial settings. Future research should focus on experimental designs that clarify and enhance the dominance of specific charge transfer mechanisms. Signal interpretation methods grounded in triboelectrification principles should be further developed. Moreover, the integration of multi-source data with intelligent decision-making systems to advance the engineering and intelligent application of triboelectrification-based monitoring technologies.

Micropipette technology (MPT), cellular mechanical properties, microscopic vision, mechanical measurement, cell classification.

Fault diagnosis is essential for condition-based maintenance, directly impacting the reliability and safety of satellite power systems. The abundance of telemetry data enhances the performance of data-driven diagnostic methods. Transformer models address the limitation of traditional methods by capturing long-term dependencies and learning high-dimensional feature representations. However, Transformers cannot identify the importance of internal feature elements, and existing attention mechanisms often introduce redundant complexity, potentially degrading performance. To tackle this, we propose a Global Sparse Attention (GSA) mechanism that applies sparse encoding to two feature dimensions and fuses them via an outer product to generate a global attention mask. This design improves model efficiency, precisely identifies key feature regions, and enhances diagnostic accuracy. Experiments on a satellite power platform demonstrate that our method outperforms existing approaches.

Gas path fault diagnosis in high-performance gas turbines is often challenged by insufficient sensor availability, leading to underdetermined problems where fault isolation is easily confused with degradation in other components. In such cases, isolation results become unreliable when the number of sensors is smaller than the number of estimated health parameters, and multiple feasible solutions arise. To alleviate this issue, this paper introduces an isolability indicator for gas path faults under simultaneous unknown degradations, along with a sensor selection algorithm that maximizes isolability. The isolability of a component fault is defined as the probability of distinguishing it from faults in other components, and a sensor configuration performance metric is constructed from the isolabilities of all faults. Since calculating isolability through Monte Carlo simulations imposes a heavy computational burden, an approximation method based on clustering to reduce Monte Carlo simulations is developed. A simplified indicator is designed to infer the change of isolability. And they can be classified by the geometric features of the fault symptoms sets. Building on this, an algorithm is proposed to determine the necessary Monte Carlo simulations, further estimating the remained isolability and reducing computational cost. The approach is validated on a high-bypass ratio turbofan engine model through digital simulations. Results demonstrate that the proposed indicator effectively characterizes component fault isolability, and that appropriate sensor selection enhances the fault isolability in the presence of unknown degradation. Moreover, the algorithm successfully identifies optimal or near-optimal sensor configurations across different scenarios.

As an image-based optical technique for full-field deformation measurement, the metrological performance of three-dimensional digital image correlation (3D-DIC) highly depends on the binocular stereovision system used for acquiring stereo images of a test object surface. Such a system requires on-site adjustment to meet the specific requirements of the measurement task. However, currently existing adjustment methods rely on empirical trial-and-error during on-site setup, which not only reduces experimental efficiency but also fails to guarantee the measurement accuracy. To cope with this problem, we present a framework for optimal design of structural parameters of a 3D-DIC system composed of two cameras. Specifically, the framework utilizes the 3D reconstruction uncertainty at the field-of-view center as an objective function and optimizes the structural parameters through genetic algorithm, while considering multiple experimental constraints, including the available cameras and lenses, specimen dimensions, and experimental types. Upon implementation, the framework enables determination of structural parameters such as device specifications, stereo angle, and baseline to meet specific measurement requirements. The proposed design method was applied to three representative experimental cases, including one hypothetical and two real-world applications. Experimental verification of the two practical systems confirmed the accuracy and reliability of the design strategy. This framework enables users to efficiently develop demand-oriented 3D-DIC systems with enhanced metrological performance, offering valuable guidance for configuring 3D-DIC systems in measuring specific objects.

To improve the evaluation accuracy and calculation efficiency of sphericity error, a high-accuracy and high-efficiency sphericity error evaluation method based on the conical search algorithm is proposed. Firstly, an auxiliary sphere is constructed with the least squares sphere center as a reference, and seven auxiliary search points are selected for coarse search to identify the optimal and suboptimal auxiliary search points. Secondly, fine search is performed: taking the optimal solution of the coarse search as the vertex and the line connecting the optimal and suboptimal solutions as the axis, a conical search space is constructed. Conical search points are generated by layered equal-angle sampling on the conical surface. By utilizing the feature that the radius of the conical cross-section decreases as height increases, high-density sampling is achieved in the region close to the optimal solution, and a global optimal sphere center is obtained through iterative convergence. Finally, based on the "3+2" and "2+3" geometric configurations of the minimum zone theory, the accurate solution of sphericity error is completed. Experimental results show that in the four groups of simulated datasets, the evaluation results of the proposed algorithm are consistent with the ideal values. Compared with the Genetic Algorithm (GA), the average running time is shortened by approximately 98.6%; compared with the Particle Swarm Optimization (PSO) algorithm, it is reduced by about 93.9%, showing a significant improvement in computational efficiency. The standard deviation of 20 repeated experiments is 0, indicating high stability. In the comparison of three groups of reference datasets, the sphericity error evaluation results are more accurate than those of existing mainstream algorithms, verifying the effectiveness of this algorithm in sphericity error evaluation.

Initial alignment is one of the critical technologies for the smooth and effective operation of the strapdown inertial navigation system (SINS), and the alignment time and accuracy are the two deterministic indicators of SINS initial alignment performance. To address the issues of redundant alignment time and low accuracy in the initial alignment process, a new method utilizing system observations is proposed, which includes velocity errors, equivalent specific force outputs, and equivalent gyro angular velocity errors. System state equations and observation equations are established based on the fundamental principles and error characteristics of inertial sensors. Singular value decomposition was employed to calculate the singular values of each system’s observation matrix, and observability was determined through singular value analysis. Experimental results indicate that the inclusion of equivalent specific force outputs accelerates the convergence of the horizontal misalignment angle, while equivalent gyro angular velocity errors expedite the convergence of the azimuth misalignment angle. Proposed method with enhanced system observability improves alignment accuracy significantly, with horizontal accuracy increased by approximately 62% and vertical accuracy by about 42% compared to a filtering model using only velocity errors as observations.

In the European Union, temporal light artifacts (TLAs) have recently been regulated for mains-connected LED lamps and luminaires available on the market. However, the limitations are only on white LED sources. Colored LED sources are not under current regulation, although they are often used as components to create a source that looks like a white source to an observer. We present a measurement setup based on a hyperspectral camera to measure different types of luminaires simultaneously to quantify the spectral components of the TLA metrics. The camera has one-megapixel spatial resolution and can measure at 1000 different wavelengths with a bandwidth of 10 nm. The sampling frequency of the camera was extended from 100 frames per second (fps) to 1000 fps using triggering with a varied delay, and waveforms within an integration time of 1 s were captured. We demonstrate measurements of white LED lamps at wavelengths of 450 nm, 550 nm and 650 nm, and compare the results of the TLA metrics with other measurement setups. We observed that for one of the lamps, the hyperspectral measurements deviated only 3% from the reference values, while for the other lamp, the demonstrative measurement system did not have sufficient performance, which increased the deviations significantly.

The saw-cut test (SCT) is a non-destructive testing technique based on performing superficial cuts in a prestressed concrete (PC) member aimed at inducing the relief of compressive stresses in the resulting isolated concrete block (ICB). The SCT is suitable in the context of assessing the residual prestressing force in existing unmonitored PC members. However, issues related to the use of electrical resistance-based strain gauges limit their application. To overcome these issues, this paper presents a new methodological strategy focusing on an efficient alternative measurement approach to determine the involved concrete strain changes by the SCT. To this end, three prototypes were tested with the aim of familiarizing with and learning about the SCT, and then a subsequent experimental program including four PC members was carried out to assess the feasibility and reliability of the conceived measurement approach by validating its implementation under conditions of redundancy, reproducibility and repeatability. By the sole use of mechanical extensometry as a measurement technique, the study confirms the viability of consistently characterizing the behavior of an ICB through conforming internal measurement bases of different nominal base lengths within the ICB from measurements taken from external measurement bases to the ICB.

Fast tomographic acquisitions are essential to study time-dependent and rapid phenomena in materials science, e.g., viscoelasticity or crack propagation. Acquisition time can be halved by employing two X-ray sources and detectors, provided they can be switched interchangeably. This work discusses the reconstruction of 3D volumes using data acquired simultaneously from two crossed-line laboratory tomographs. For this, geometrical parameters are proposed and their sensitivity is tested, allowing to discard less significant parameters. The selected parameters are then calibrated solely from the acquired projections of the object, by minimizing the difference between the reprojected volume and the experimental data. A gypsum sample is used as a model material and three acquisition speed configurationsranging from 20 s to 500 s -are tested to evaluate the effect of noise. The proposed procedure was shown to be robust even under noisier acquisition conditions. This methodology is believed to be an important step towards in-situ mechanical tests to be analyzed with projection-based techniques, with potentially two-to three-orders-of-magnitude increase in acquisition rates.

An inverse problem(IP) approach is proposed to simultaneously determine the three-dimensional position and size of bubbles or droplets in a two phase flow from a single camera image. The method is based on interferometric particle imaging(IPI) and defocusing particle tracking velocimetry(DPTV). A forward model(FM) is introduced that integrates a scattering model based on geometrical optics and the Lorentz-Mie theory, along with a wave propagation model based on the Huygens-Fresnel-principle to simulate particle images. Using bounding boxes from object detection methods as initialization, the InvP-approach approximates the position and diameter of each particle in the image. The performance of the presented approach is evaluated on the grounds of the data achieved by Sax et al.(Phys.Rev.Appl.24): As key aspects it achieves sub-pixel-accuracy in position determination, exceeds the diameter accuracy of current FFT-based benchmarks on real data and furthermore achieves sub-micrometer precision in diameter resolution, even for three-dimensionally distributed particles. The InvP approach achieves a decoupling of the diameter estimation from the out-of-plane position estimation, thus avoiding error propagation from one to the other, which significantly increases the sizing accuracy. The incorporated FM accounts for aliasing effects in the interference pattern, effectively increasing the measurable volume both closer to and further from the focal plane. This improvement qualifies the approach to measure closer to the focal plane, which in turn allows to obtain images with higher signal-to-noise ratio(SNR). The InvP-approach is capable of handling ignificantly lower signal-to-noise ratios compared to commonly applied algorithms and noise levels at which detection algorithms typically fail, presenting significant potential for single optical access IPI in side-and backscatter regions where low SNR usually necessitates sophisticated data processing methods. Notably, the InvP-approach is largely unaffected by particle image overlaps, addressing another major challenge in single-camera particle tracking and sizing at high source densities in a given field of view.

This work summarizes reference measurements, Monte Carlo (MC) particle transport modelling, and validation of the Finapp Model 3 Cosmic-Ray Neutron Sensor (CRNS). The MC calculations and reference measurements were performed independently in the broad-range radionuclide reference neutron fields of Czech Metrology Institute (CMI) and Slovak Institute of Metrology (SMU). The validated models outputs were subsequently verified using monoenergetic neutron reference fields with energies ranging from 24 keV to 1.2 MeV at the Physikalisch-Technische Bundesanstalt (PTB), Germany confirming the predicted response agreement within 13% of experimental results. To demonstrate the applicability of the validated and verified models, they were used to predict the probe behavior in a virtual reference neutron field simulating realistic conditions with relative volumetric soil moisture levels from 1% to 50%. The obtained results matched the Universal Transport Solution (UTS) model across whole soil moisture range. Single parameter power equation which, together with on-site calibration, can be used to convert relative count rate of the Finapp 3 probe to volumetric soil moisture is proposed.

Distributed fiber sensing (DFS) is a powerful tool for structural health monitoring (SHM), allowing continuous and seamless measurements of temperature and strain along the fiber. The spatial accuracy of a DFS interrogator, as a key parameter of the system, is vital for precisely locating structural perturbations or defects. Its evaluation and calibration methods however attract little attention. A fiber optic artefact based on a fiber loop is developed to evaluate distance accuracy and signal quality for both self-developed and commercial sensing systems based on Rayleigh, Raman, and Brillouin scattering effects, respectively. The measured distance is corrected to remove the influence of the pulse width. Additionally, the obtained SNRs are compared for different loop trips and pulse widths, assisting to assess signal quality for SHM applications.

Silicon Carbide (SiC)-based detectors offer exceptional radiation hardness and thermal stability, making them suitable for neutron spectroscopy in fusion reactor environments, which are characterized by high temperatures and intense neutron fluxes. In this study we demonstrate a 250 μm-thick 4H-SiC p-n junction detector that maintains stable DT neutron detection performance across the full temperature range from 25°C to 500°C, thereby overcoming the limitations commonly encountered with diamond-based detectors. These results highlight the potential of thick SiC detectors for monitoring neutron flux and performing neutron spectroscopy in harsh environments, such as the breeding blanket of fusion reactors.

Advanced manufacturing, precision metrology, space exploration, and other fields increasingly rely on multi-degree-of-freedom (multi-DOF) and high-precision measurements. The demands of such measurement systems—structural complexity, systematic error control, and information decoupling—have challenged traditional interferometric techniques. Wavefront interference imaging, which integrates laser interferometry with image analysis, has emerged as an advanced technique capable of subnanometer displacement and submicroradian angular resolution using a single laser beam. This method has gained importance in multi-DOF measurement technologies because it simultaneously obtains ultra-precise multi-DOF measurements with a compact setup, strong decoupling capability, and high integrability. This review systematically examines the development of wavefront interference imaging. Beginning with physical modeling of interference fringes, it traces the evolution of representative measurement models from two-dimensional to six-DOF configurations and analyzes the potential integration of this technique with emerging deep learning–based fringe processing methods. The paper further discusses frequency and phase decoupling algorithms in both the spatial and spectral domains and summarizes recent applications of this technology to nanometric coordinate measurements, atomic force microscopy, laser leveling, and spacecraft systems. The transition of wavefront interference imaging, from single-DOF extraction to coupled modeling and real-time resolution of multi-DOFs, demonstrates the excellent system scalability and application potential of this technology. This review aims to establish a theoretical framework and developmental roadmap for wavefront interference imaging, facilitating the advancement of high-precision, high-dimensional measurement systems in related domains.

Knowledge of the acoustic properties of materials is critical for many applications of acoustics. Common methods for measuring acoustic material properties include the through transmission substitution-insertion method (SIM) and the multiple-reflection method (MRM). However, the accuracy and uncertainty of MRM measurements have not been evaluated as widely as for SIM. This study investigates the impact of system compressional force, buffer rod diameter, and buffer rod and specimen surface conditions on the accuracy and repeatability of MRM measurements of the attenuation coefficient and longitudinal sound velocity of 3D printed specimens validated against SIM measurements. The measurement accuracy and repeatability improved as the system compression was increased, with attenuation coefficients measured by SIM and MRM at 0.5 MHz converging to within 0.1 dB cm⁻¹ (1.6 ± 0.1 dB cm⁻¹ versus 1.7 ± 0.2 dB cm⁻¹) for the RGDA8625 material. A buffer rod of at least twice the diameter of the transducer was required for agreement between the two methods and differences of $ \gt $40% were measured with narrower buffer rods. The buffer rod and specimen should be sufficiently flat and well-polished to minimize errors. These findings may guide future system design and optimization, and suggest that experimental convergence should be verified in the setup. This may aid the standardization of measurement protocols, helping to improve the accuracy and repeatability of acoustic material property measurements.

As a practical and effective tool for quantitative in-plane deformation measurement of a planar object surface, two-dimensional digital image correlation (2D DIC) is now widely accepted and commonly used in the field of experimental mechanics. It directly provides full-field displacements to sub-pixel accuracy and full-field strains by comparing the digital images of a test object surface acquired before and after deformation. In this review, methodologies of the 2D DIC technique for displacement field measurement and strain field estimation are systematically reviewed and discussed. Detailed analyses of the measurement accuracy considering the influences of both experimental conditions and algorithm details are provided. Measures for achieving high accuracy deformation measurement using the 2D DIC technique are also recommended. Since microscale and nanoscale deformation measurement can easily be realized by combining the 2D DIC technique with high-spatial-resolution microscopes, the 2D DIC technique should find more applications in broad areas.

This article is a personal review of the historical developments of digital image correlation (DIC) techniques, together with recent important advances and future goals. The historical developments of DIC techniques over the past 35 years are divided into a foundation-laying phase (1982–1999) and a boom phase (2000 to the present), and are traced by describing some of the milestones that have enabled new and/or better DIC measurements to be made. Important advances made to DIC since 2010 are reviewed, with an emphasis on new insights into the 2D-DIC system, new improvements to the correlation algorithm, and new developments in stereo-DIC systems. A summary of the current state-of-the-art DIC techniques is provided. Some further improvements that are needed and the future goals in the field are also envisioned.

The detection and measurement of gas concentrations using the characteristic optical absorption of the gas species is important for both understanding and monitoring a variety of phenomena from industrial processes to environmental change. This study reviews the field, covering several individual gas detection techniques including non-dispersive infrared, spectrophotometry, tunable diode laser spectroscopy and photoacoustic spectroscopy. We present the basis for each technique, recent developments in methods and performance limitations. The technology available to support this field, in terms of key components such as light sources and gas cells, has advanced rapidly in recent years and we discuss these new developments. Finally, we present a performance comparison of different techniques, taking data reported over the preceding decade, and draw conclusions from this benchmarking.

Planetary gearboxes have various merits in mechanical transmission, but their complex structure and intricate operation modes bring large challenges in terms of fault diagnosis. Deep learning has attracted increasing attention in intelligent fault diagnosis and has been successfully adopted for planetary gearbox fault diagnosis, avoiding the difficulty in manually analyzing complex fault features with signal processing methods. This paper presents a comprehensive review of deep learning-based planetary gearbox health state recognition. First, the challenges caused by the complex vibration characteristics of planetary gearboxes in fault diagnosis are analyzed. Second, according to the popularity of deep learning in planetary gearbox fault diagnosis, we briefly introduce six mainstream algorithms, i.e. autoencoder, deep Boltzmann machine, convolutional neural network, transformer, generative adversarial network, and graph neural network, and some variants of them. Then, the applications of these methods to planetary gearbox fault diagnosis are reviewed. Finally, the research prospects and challenges in this research are discussed. According to the challenges, a dataset is introduced in this paper to facilitate future investigations. We expect that this paper can provide new graduate students, institutions and companies with a preliminary understanding of methods used in this field. The dataset can be downloaded from https://github.com/Liudd-BJUT/WT-planetary-gearbox-dataset.

The uncertainty of a PIV displacement field is estimated using a generic post-processing method based on statistical analysis of the correlation process using differences in the intensity pattern in the two images. First the second image is dewarped back onto the first one using the computed displacement field which provides two almost perfectly matching images. Differences are analyzed regarding the effect of shifting the peak of the correlation function. A relationship is derived between the standard deviation of intensity differences in each interrogation window and the expected asymmetry of the correlation peak, which is then converted to the uncertainty of a displacement vector. This procedure is tested with synthetic data for various types of noise and experimental conditions (pixel noise, out-of-plane motion, seeding density, particle image size, etc) and is shown to provide an accurate estimate of the true error.

Despite continuous technological enhancements of metal Additive Manufacturing (AM) systems, the lack of process repeatability and stability still represents a barrier for the industrial breakthrough. The most relevant metal AM applications currently involve industrial sectors (e.g. aerospace and bio-medical) where defects avoidance is fundamental. Because of this, there is the need to develop novel in situ monitoring tools able to keep under control the stability of the process on a layer-by-layer basis, and to detect the onset of defects as soon as possible. On the one hand, AM systems must be equipped with in situ sensing devices able to measure relevant quantities during the process, a.k.a. process signatures. On the other hand, in-process data analytics and statistical monitoring techniques are required to detect and localize the defects in an automated way. This paper reviews the literature and the commercial tools for in situ monitoring of powder bed fusion (PBF) processes. It explores the different categories of defects and their main causes, the most relevant process signatures and the in situ sensing approaches proposed so far. Particular attention is devoted to the development of automated defect detection rules and the study of process control strategies, which represent two critical fields for the development of future smart PBF systems.

To guarantee the stability and safety of industrial production, it is necessary to regulate the behavior of employees. However, the high background complexity, low pixel count, occlusion and fuzzy appearance can result in a high leakage rate and poor detection accuracy of small objects. Considering the above problems, this paper proposes the Enhanced feature extraction-You Only Look Once (EFE-YOLO) algorithm to improve the detection of industrial small objects. To enhance the detection of fuzzy and occluded objects, the PixelShuffle and Receptive-Field Attention (PSRFA) upsampling module is designed to preserve and reconstruct more detailed information and extract the receptive-field attention weights. Furthermore, the multi-scale and efficient (MSE) downsampling module is designed to merge global and local semantic features to alleviate the problem of false and missed detection. Subsequently, the Adaptive Feature Adjustment and Fusion (AFAF) module is designed to highlight the important features and suppress background information that is not beneficial for detection. Finally, the EIoU loss function is used to improve the convergence speed and localization accuracy. All experiments are conducted on homemade dataset. The improved YOLOv5 algorithm proposed in this paper improves mAP@0.50 (mean average precision at a threshold of 0.50) by 2.8% compared to the YOLOv5 algorithm. The average precision and recall of small objects show an improvement of 8.1% and 7.5%, respectively. The detection performance is still leading in comparison with other advanced algorithms.

This paper discusses the propagation of the instantaneous uncertainty of PIV measurements to statistical and instantaneous quantities of interest derived from the velocity field. The expression of the uncertainty of vorticity, velocity divergence, mean value and Reynolds stresses is derived. It is shown that the uncertainty of vorticity and velocity divergence requires the knowledge of the spatial correlation between the error of the x and y particle image displacement, which depends upon the measurement spatial resolution. The uncertainty of statistical quantities is often dominated by the random uncertainty due to the finite sample size and decreases with the square root of the effective number of independent samples. Monte Carlo simulations are conducted to assess the accuracy of the uncertainty propagation formulae. Furthermore, three experimental assessments are carried out. In the first experiment, a turntable is used to simulate a rigid rotation flow field. The estimated uncertainty of the vorticity is compared with the actual vorticity error root-mean-square, with differences between the two quantities within 5–10% for different interrogation window sizes and overlap factors. A turbulent jet flow is investigated in the second experimental assessment. The reference velocity, which is used to compute the reference value of the instantaneous flow properties of interest, is obtained with an auxiliary PIV system, which features a higher dynamic range than the measurement system. Finally, the uncertainty quantification of statistical quantities is assessed via PIV measurements in a cavity flow. The comparison between estimated uncertainty and actual error demonstrates the accuracy of the proposed uncertainty propagation methodology.

Although data-driven methods have been widely used in planetary gearbox fault diagnosis, the difficulty and high cost of manual labeling leads to little labeled training data, which limits the classification performance of traditional data-driven methods. Therefore, the semi-supervised fault diagnosis method with few labeled samples becomes one of the main research directions. Graph attention network (GAT) is distinguished from traditional classification network by using graph structure for fault node information aggregation and feature extraction, which is an effective semi-supervised learning algorithm. This paper uses fast Fourier transform to process the original vibration signal of gearbox and use it as graph nodes, and propose a KNN graph construction method using pooling for fuzzy distance calculation. In addition, this paper improves the distribution of attention weights by introducing dynamic graph attention networks to correct the problem that classical static GATs cannot clearly distinguish the weights of different categories of nodes. Experiments show that the method proposed in this paper can better extract fault features in complex gearbox vibration signals with an accuracy of more than 99% with very few labeled samples, and has better diagnostic performance compared with other graph neural network architectures and traditional classification networks.

Schlieren and shadowgraph techniques are used around the world for imaging and measuring phenomena in transparent media. These optical methods originated long ago in parallel with telescopes and microscopes, and although it might seem that little new could be expected of them on the timescale of 15 years, in fact several important things have happened that are reviewed here. The digital revolution has had a transformative effect, replacing clumsy photographic film methods with excellent—though expensive—high-speed video cameras, making digital correlation and processing of shadow and schlieren images routine, and providing an entirely-new synthetic schlieren technique that has attracted a lot of attention: background-oriented schlieren or BOS. Several aspects of modern schlieren and shadowgraphy depend upon laptop-scale computer processing of images using an image-capable language such as MATLAB^TM. BOS, shock-wave tracking, schlieren velocimetry, synthetic streak-schlieren, and straightforward quantitative density measurements in 2D flows are all recent developments empowered by this digital and computational capability.