Measurement Science and Technology

In practical industrial applications, the scarcity of compound fault samples poses significant challenges in obtaining sufficient training data, leading to class imbalance and negatively impacting the diagnostic performance of models. To tackle this issue, we propose a generative zero-shot learning method for rolling bearing compound fault intelligent diagnosis, attempting to diagnose unknown target faults using only known fault samples. Specifically, a novel fault attribute description method is designed, which combines fault semantic information with manually defined fault description information, thereby constructing fault category auxiliary information (FCAI) to learn the correlation between single faults and compound faults. Furthermore, the continuous wavelet transform is used to data preprocess the raw vibration signals, and a wide kernel convolutional neural network is constructed to extract deep fault feature information from the samples. Finally, an adversarial training strategy is adopted to learn the mapping relationship between the fault feature information and the FCAI, and compound faults are diagnosed using a distance metric method based on the similarity relationship between fault features. Through experimental validation on the laboratory bearing dataset and the Huazhong University of Science and Technology bearing dataset, the effectiveness and superiority of the proposed method in scenarios lacking compound fault samples.

Cracks in high-speed rail track slabs are important indicators of aging, and the issue of ineffective crack repairs remains a significant challenge in crack maintenance. However, research on this issue is limited. This paper proposes a novel multi-class crack detection method based on the YOLOv8 framework, addressing the problem of ineffective crack repairs. To enhance the model's feature extraction capability, we introduce the dynamic snake convolution (DSC) module into the YOLOv8 backbone network and reconstruct the Bottleneck structure in C2f, establishing the C2f-DSC module. This modification replaces some of the C2f modules, enabling better multi-scale feature extraction, particularly for difficult-to-detect ineffective cracks. Additionally, we incorporate the CA attention mechanism in the neck network to improve the model's ability to focus on critical features and effectively transmit subtle crack details throughout the network. We also replace the CIoU loss function with SIoU, reducing excessive penalization of geometric factors, thereby enhancing the model's generalization ability. Finally, we validate the proposed method through a comprehensive evaluation of network structure, crack data, classification methods, and environmental conditions. Experimental results show that the proposed Crack-YOLOv8 model significantly improves detection accuracy, reducing both false positives and false negatives. Specifically, the average precision and recall are enhanced by 4.9% and 4.3%, respectively, demonstrating the effectiveness of our approach in accurately detecting ineffective crack repairs in rail track slabs.

Tooth root crack faults of gears have long been affecting the performance and service life of rotating machiners. The quantitative diagnosis of tooth root crack is crucial for effective equipment condition monitoring and management. However, affected by the high costs and limited availability of sample data, the problem of insufficient sample size and challenges to the quantitative diagnosis of tooth root crack fault always exist. A method is proposed to facilitate transfer diagnosis from the simulation domain (source domain) to the experimental domain (target domain), which is driven by the dynamic simulation model combining the Gaussian mixture model (GMM) and an improved domain adversarial neural network (DANN). A gear dynamics simulation model is first established based on the fundamental parameters of tooth root crack fault of gears to generate sufficient simulated data to compensate for the limited real data. Then the feature distribution of the simulated data is optimized with a small amount of real data based on GMM. The key indicators, such as amplitude and impact point characteristics, are aligned with the real data distribution. It reduces dependence on experimental data to a certain extent. Furthermore, the DANN improved by a Beta distribution DANN is employed in the feature spaces of two domains introducing interpolation processing. The distribution differences between the two domains are further reduced to enhance the cross-domain generalization ability of the model. The effectiveness of the proposed method is validated under 15 transfer tasks. The results demonstrate that the method achieves accurate cross-device and cross-condition quantitative diagnosis of tooth root crack from simulated data to experimental data, using fewer experimental data. The advantage of simulated data in addressing the limited sample sizes problem is confirmed, with accuracy improving by 8% compared to other methods.

Partial-domain adaptive techniques are widely applied in cross-operational bearing fault diagnosis to address inconsistencies between source and target domain fault classes effectively. However, existing studies face challenges in feature alignment, including insufficient alignment of shared class fault features between the source and target domains, interference from outlier class samples in the source domain, and low-confidence pseudo-labels in the target domain. These issues hinder efficient alignment of shared class features, ultimately reducing fault diagnosis performance. Therefore, this paper proposes a joint weighting metric domain adaptation model. To improve the alignment of shared class fault features, a joint metric combining correlation alignment and local maximum mean discrepancy is developed. This metric complements adversarial training, reduces distributional discrepancy between domains, and optimizes feature alignment. To address interference from outlier class samples in the source domain, class-level weights are employed to effectively mitigate the negative transfer effects of these samples. Additionally, sample-level weights are introduced to reduce the negative transfer effects of low-confidence pseudo-labels and boundary samples, enhancing the accuracy and robustness of shared class feature alignment. The proposed method is validated through experiments on both public and self-constructed bearing datasets. Experimental results demonstrate that the proposed method achieves higher diagnostic accuracy than existing partially domain-adaptive methods in cross-operational diagnostic tasks involving similar and different equipment.

The cracks in hydro-turbine runner blades impact turbine unit reliability and stability. The vibration monitoring based on natural frequency variation of hydro-turbine is an accurate and potential online monitoring method. In this paper, the modal characteristics of runner blades with different cracks under flow-induced vibration are studied numerically in different modes. The results indicate that the presence of cracks in the blade leads to a decrease in the natural frequency compared to healthy blades. Furthermore, the natural frequency of the wet mode experiences a significant decrease compared to the dry mode and the prestressed mode. The analysis reveals that cracked blade lead to large frequency difference (f_d) of the natural frequencies of the runner mainly at the 4th–12th orders in the prestressed mode. For the wet mode, cracks cause large f_d of the natural frequencies of the runner mainly at the 3rd–20th orders, but the most significant ones are at the 3rd–10th orders. The frequency-doubling (f_fd) values of runner blade with crack under the 4th–12th orders in the prestressed mode or under the 3rd–10th orders in the wet mode are concentrate on 0.3–1.0. The results of modal analysis can provide a valuable reference for the crack detection of turbine runner.

Traditional physical models face significant challenges in parameter determination and adapting to complex systems, whereas data-driven models are constrained by data quality and quantity, making them susceptible to overfitting or underfitting problems. These limitations lead to deficiencies in robustness, physical interpretability, and generalization capabilities of existing models. In recent years, the fusion of physical mechanism-based and data-driven approaches has effectively addressed the shortcomings of both types of models, attracting widespread attention. However, there is no systematic review specifically on bearing prognosis and health management under hybrid physical mechanism and data-driven methods. To fill this gap, this paper comprehensively analyzes the research advancements in bearing prognosis and health management based on hybrid physical mechanism and data-driven methods. From the perspective of fusion strategy, the paper categorizes bearing prognosis and health management methods based on the fusion of physical mechanism and data-driven model into three levels: data level, network level, and model level, and further subdivides the research methods at each fusion level. In each subdivision field, this paper discusses the application of each research method in three main aspects: condition monitoring, fault diagnosis, and remaining useful life prediction, summarizing the research methods employed by current scholars. Finally, this paper evaluates the advantages and disadvantages of each analytical method in practical applications, identifies current research challenges, and proposes future research directions. The aim is to provide guidance and in-depth insights for researchers and engineers in the field of bearing prognosis and health management.

Rotating machinery holds a pivotal role in industrial and daily life. Incorporating robust health monitoring or fault diagnosis methodologies is imperative to safeguard such equipment's reliability and operational availability. Digital twin (DT) technology, which mirrors the real-time condition of a physical entity through a digital counterpart, has garnered considerable attention, particularly in the realm of fault diagnosis for rotating machinery. Despite its growing significance, there is a notable lack of comprehensive literature reviews pertaining to this domain. Consequently, this paper thoroughly reviews current and emerging DT technology-driven rotating machinery fault diagnosis trends. Initially, the origin and evolution of DT, alongside its application in fault diagnosis across diverse areas, are elucidated. Subsequently, the pivotal technologies and extant solutions within the DT-driven fault diagnosis purview are inspected. Furthermore, the literature on DT-driven rotating machinery for real-time condition monitoring, fault diagnosis, and life prediction is examined through varying tasks and application imperatives. Ultimately, the extant challenges of besetting DT-driven rotating machinery fault diagnosis techniques are defined, and their prospective evolutionary trajectories are appraised.

To improve the accuracy of bearing state classification in multi-source monitoring environments, a novel decision fusion method based on probabilistic Jensen–Shannon (PJS) divergence and D-S evidence theory is proposed. Firstly, the initial state probability of each bearing is determined by the bearing state classification score value, and the basic probability assignment (BPA) value of each bearing state is set by obtaining the global credibility of each sensor based on the pre-network test results. Secondly, the PJS divergence is proposed on the basis of the Kullback Leibler (KL) divergence to measure the conflict between the data collected by each sensor, and the weight value of the BPA value of each sensor is reset according to the PJS divergence value, which improves the accuracy of the BPA value. Finally, the D-S evidence theory is used to fuse the BPA value of each sensor and obtain the final bearing state classification result. The experimental part shows the effectiveness using the bearing signals collected in the laboratory, and the generality of this paper's method is verified by using it in conjunction with other networks.

Advanced three-dimensional (3D) tracking methods are essential for studying particle dynamics across a wide range of complex systems, including multiphase flows, environmental and atmospheric sciences, colloidal science, biological and medical research, and industrial manufacturing processes. This review provides a comprehensive summary of 3D particle tracking and flow diagnostics using digital holography (DH). We begin by introducing the principles of DH, accompanied by a detailed discussion on numerical reconstruction. The review then explores various hardware setups used in DH, including inline, off-axis, and dual or multiple-view configurations, outlining their advantages and limitations. We also delve into different hologram processing methods, categorized into traditional multi-step, inverse, and machine learning (ML)-based approaches, providing in-depth insights into their applications for 3D particle tracking and flow diagnostics across multiple studies. The review concludes with a discussion on future prospects, emphasizing the significant role of ML in enabling accurate DH-based particle tracking and flow diagnostic techniques across diverse fields, such as manufacturing, environmental monitoring, and biological sciences.

Large volumes of light sources and their characteristics control devices are challenges to out-of-laboratory applications of quantum precision measurement devices, such as spin-exchange relaxation-free (SERF) inertial and extremely weak magnetic field measurement devices. High-Q whispering gallery mode (WGM) resonators have excellent optical field confinement capability, extremely narrow linewidth, and small mode volume, which can easily realize compact low noise and high-frequency stability light sources, having a great potential to realize more compact quantum precision measurement devices. This will broaden the use of quantum precision measurement devices in areas such as transportable SERF gyroscopes, wearable magnetometers, etc. In this review, the fundamentals and characteristics of WGM optical cavities are firstly introduced, as well as its fabrication techniques and built-up materials. Then, the laser stabilizing applications of WGM optical cavities including wavelength tuning, self-injection locking, and thermal stabilization are discussed. Finally, the application prospects of quantum precision measurement are discussed.

The implementation of strain-sensing structural materials for structural health monitoring in civil engineering has the potential to enable self-diagnosing buildings, including masonry and concrete structures. However, a major bottleneck of such technologies is the high cost of the measurement hardware. Dealing with smart sensing systems that require expensive laboratory equipment is impractical for the construction industry. Researchers typically use costly laboratory power sources, signal generators, and acquisition systems for measuring structural responses using smart self-sensing materials, due to performance and reliability reasons. In particular, promising sensing composites evaluated in this paper are piezoresistive nanocomposite or microcomposite concretes and bricks that allow measuring mechanical strain using their piezoresistive response. To address the issues above for such kind of materials, the newly proposed Smart construction Materials Electrometer (SME) device offers a practical alternative to off-the-shelf hardware. The SME can generate sinusoidal, triangular, and biphasic signals with $\pm$10 V and program signal frequencies ranging from 1 Hz to 10 Hz. What is more, it can also accurately measure the electrical outputs of smart materials under an applied mechanical load or change in environmental conditions. The SME provides significant advantages over traditional instrumentation: it integrates multiple functionalities for state-of-the-art measurements in self-sensing materials such as smart concretes and smart bricks, allows easy mobility for field testing, is about 50 times cheaper than commercial devices, and offers versatile and accurate measurements. These benefits are confirmed through experimental tests on commercial resistors, comparisons with off-the-shelf high-cost equipment, and field measurements on real structures. The proposed SME is a crucial step toward the practical implementation of strain-sensing structural materials and their widespread use in real-world structural health monitoring applications.

Extracting weak fault features under strong background noise interference is key to early fault diagnosis of rolling bearings. A method combining the improved honey badger algorithm (IHBA), successive variational mode decomposition (SVMD), and maximum correlated kurtosis deconvolution (MCKD) is proposed. The SVMD method effectively suppresses noise while retaining key fault features. To improve feature extraction, IHBA is used as the optimization tool, and a composite index PE composed of the Pearson correlation coefficient ratio-envelope spectrum peak factor is used as the fitness function, enhancing the efficiency and stability of parameter optimization. To address the issue of incorrect IMF component selection leading to excessive noise and poor robustness, the Dynamic Threshold via Pearson and Kurtosis is constructed, which adaptively selects IMF components rich in fault features for signal reconstruction. MCKD can highlight continuous pulse signals obscured by noise. IHBA is used again, with a composite index EE composed of envelope entropy-envelope spectrum peak factor as the fitness function, and the SVMD-processed signal is input to MCKD. Finally, envelope demodulation is used to extract rolling bearing fault feature frequencies, significantly improving the accuracy of weak fault feature extraction. Through verification with simulated signals of early rolling bearing faults and the full-lifecycle dataset XJTU-SY, the proposed method improves the accuracy and robustness of early fault diagnosis under strong background noise and other interference factors, providing a possible solution for extracting weak fault features.

Dual-axis rotational modulation has been recognized as a promising technique for suppressing inertial sensor errors, though its application in compact and mobile platforms remains limited due to challenges in system weight and reliability. This paper presents a novel 44-position dual-axis rotational modulation scheme that has the potential to enhance the reliability and reduces the weight of rotational inertial navigation systems(RINS) by avoiding a full circle rotation in the vertical direction to reduce the use of slip ring structure, paving the way for their miniaturization and application in small and compact carriers. A theoretical analysis of IMU errors during the modulation process under the navigation frame is conducted based on the sensor coordinate system, and the modulation criterion under restricted rotation angle conditions is derived. Simulation and experimental results from a 24-hour navigation test using a 0.2°/h INS validate the scheme's effectiveness to modulate IMU errors under one-axis rotation constraints. The results revealed a maximum horizontal position error reduction of 14.77% compared to traditional methods, with an RMS error of 1.93 n miles.

Accurate thrust measurement is essential for spacecraft propulsion and space-based gravitational wave detection. However, thrust measurements are highly sensitive to environmental noise, making effective noise suppression critical for reliable signal acquisition. Thrust signals typically exhibit nonlinear abrupt transitions alongside smooth trends, posing challenges for traditional denoising methods, which often struggle to suppress noise while preserving key signal features. To address this issue, an improved Successive Variational Mode Decomposition (SVMD) algorithm optimized by the Black-winged Kite Algorithm (BKA) is proposed. The BKA optimizes the penalty factor α based on a minimum envelope entropy fitness function. The enhanced SVMD then adaptively decomposes the signal, with the Hausdorff Distance (HD) employed to distinguish between effective and noise modes, enabling accurate signal reconstruction. Experimental results on both simulated and actual thrust signals demonstrate that the proposed method significantly improves noise suppression compared to conventional approaches, while effectively preserving critical signal characteristics and enhancing data interpretation accuracy. These advantages make it a promising solution for thrust signal processing in high-precision measurement systems.

For the focused sensing of targets at different axial depths, the synchronized display of their three-dimensional (3D) morphology is challenging. Terahertz waves exhibit selective penetration of some dielectrics and non-polar substances, which produces con-structive guidelines for the targets. Here, a high-quality, superior-resolution, and depth-free sensing approach is demonstrated by a terahertz holographic mode. This work proposes and develops a novel stereoscopic-field method of axial multi-depth targets (AMDT) based on terahertz digital holography. The feasibility of the pre-propagating diffraction decomposition strategy is in-vestigated, and the mutual disturbance from the stacking interference patterns is effectively curbed. The optimized holograms are propagated to a pre-screening plane where exclusive mask filters of samples are shaped by using Otsu's method. The signals are reconstructed by classical auto-focusing criteria and the automated merging process can promise the performance of the targets in one stereoscopic-field. Meanwhile, the implementation of extending the field of view method, global and discrete optimization, and the phase retrieval and unwrapping algorithm also contribute to the imaging performance. Furthermore, two representative setups are applied and the positive experimental results indicate the universality and robustness of stereoscopic-field terahertz depth-free holography technology.

The implementation of strain-sensing structural materials for structural health monitoring in civil engineering has the potential to enable self-diagnosing buildings, including masonry and concrete structures. However, a major bottleneck of such technologies is the high cost of the measurement hardware. Dealing with smart sensing systems that require expensive laboratory equipment is impractical for the construction industry. Researchers typically use costly laboratory power sources, signal generators, and acquisition systems for measuring structural responses using smart self-sensing materials, due to performance and reliability reasons. In particular, promising sensing composites evaluated in this paper are piezoresistive nanocomposite or microcomposite concretes and bricks that allow measuring mechanical strain using their piezoresistive response. To address the issues above for such kind of materials, the newly proposed Smart construction Materials Electrometer (SME) device offers a practical alternative to off-the-shelf hardware. The SME can generate sinusoidal, triangular, and biphasic signals with $\pm$10 V and program signal frequencies ranging from 1 Hz to 10 Hz. What is more, it can also accurately measure the electrical outputs of smart materials under an applied mechanical load or change in environmental conditions. The SME provides significant advantages over traditional instrumentation: it integrates multiple functionalities for state-of-the-art measurements in self-sensing materials such as smart concretes and smart bricks, allows easy mobility for field testing, is about 50 times cheaper than commercial devices, and offers versatile and accurate measurements. These benefits are confirmed through experimental tests on commercial resistors, comparisons with off-the-shelf high-cost equipment, and field measurements on real structures. The proposed SME is a crucial step toward the practical implementation of strain-sensing structural materials and their widespread use in real-world structural health monitoring applications.

Deep learning (DL) has shown great promise in electrocardiogram (ECG) analysis, revolutionizing cardiovascular medicine by enabling precise and efficient diagnosis. This study explores the integration of expert knowledge into DL models, creating a flexible structure that adapts during training to refine this knowledge and guides feature separation in higher-dimensional space, thereby improving multi-label ECG classification. By leveraging domain expertise on the relationships between specific ECG abnormalities and their corresponding changes across lead dimensions, a lead-wise prior knowledge framework (LPKF) was introduced to enhance the learning efficiency of DL models. The effectiveness of this framework was validated by the higher classification performance of LPKF-enhanced models compared to their original versions. In addition, the LPKF-enhanced Inception model outperforms recent state-of-the-art DL methods, underscoring the benefits of integrating learnable expert knowledge. The study also demonstrated the interpretability of the LPKF-enhanced Inception model using gradient-weighted class activation mapping, revealing its capability to identify crucial diagnostic symptoms from ECG signals that align with clinical criteria.

The calibration of NH₃-sensing instruments has been an ongoing challenge to atmospheric chemists. Here, the potential utility of ammonium nitrate (NH₄NO₃), sulfate ((NH₄)₂SO₄), bisulfate (NH₄HSO₄), -carbonate ((NH₄)₂CO₃), and bicarbonate (NH₄HCO₃) to generate gas streams containing NH₃ or aerosol NH₄⁺ calibrated by measuring the co-emitted acids was investigated. Head space vapours and aerosols were analysed using a commercial total nitrogen (N_t) instrument modified to quantify N_t mixing ratios in both the gas- and particle phases, scanning mobility particle sizing (SMPS), thermal-dissociation cavity ring-down spectroscopy (TD-CRDS), and Fourier Transform Infrared Spectroscopy (FTIR). The best results were obtained with NH₄HCO₃. Using a simple gas delivery setup consisting of two dilution stages and a line heater, the NH₃ output from NH₄HCO₃ was calibrated by quantifying the stoichiometric co-emission of CO₂ with a relatively inexpensive non-dispersive infrared (NDIR) spectrometer. The new approach constitutes a viable alternative to conventional NH3 cylinder calibration setups.

Bearing is a critical component of equipment, and it is important to predict its Remaining Useful Life (RUL), so that early intervention can be applied before its failure. A challenge to predict the RUL is the target bearing's working condition and/or failure mode is usually unknown. Though various methods have been proposed to address this challenge, few of them take into consideration two practical issues. (1) When various datasets are available and used to train a single deep learning model, the training datasets from bearings of quite different status would affect prediction accuracy. (2) Transfer learning could be used to alleviate issue (1), but it requires data collection from the target bearing and fine-tuning, and additional time for this process may delay the RUL prediction and intervention. To address these issues towards industrial practice, we propose StRUL: stacking-based RUL prediction for bearings of unknown working condition and failure mode. StRUL is a stacking of Transformers with novel designs: modified amplitude spectrum comparison approach, similarity-based attention mechanism, distribution-based attention mechanism. First, StRUL has each Transformer pre-trained with a specific dataset, so that each Transformer can generate one encoding for input data from target bearing. Second, it applies a modified amplitude spectrum comparison approach to calculate a similarity value between the input data and each Transformer's training dataset, and then uses a similarity-based attention mechanism to prioritize the Transformer encoding with relatively large similarity values in prediction. Third, it also includes an additional Transformer trained with all the training datasets, and uses a distribution-based attention mechanism to decide how much it contributes to the predicted RUL, when distribution of the similarity values is nearly uniform. Case studies on the XJTU-SY and PRONOSTIA data each containing more than 10 datasets demonstrate that, StRUL can efficiently use all the training datasets without additional data collection or fine-tuning to achieve high prediction accuracy and speed useful for practice.

Correlative imaging, integrating diverse observational methods, has become increasingly vital for comprehensive sample analysis by linking distinct datasets that are accessible only through individual techniques. This study combines cryogenic scanning electron microscopy (cryo-SEM) and cryogenic Raman micro-spectroscopy (cryo-Raman), which together offer extensive insight into sample analysis. Cryo-SEM effectively investigates the surface, structure, and morphology, while cryo-Raman excels in detecting and identifying chemical composition. To integrate these techniques, we developed a novel assembly compatible with commercial cryo-SEM sample holders. The assembly is designed for precise sample positioning and observation at cryogenic temperatures of liquid nitrogen. This setup, controlled by LabVIEW software, includes a Dewar vessel containing liquid nitrogen, a 3D-printed stage for mounting sample holders, and a piezoelectric stage for XY motion, with z axis adjustments connected to the Raman spectrometer table. A plexiglass cover minimizes contamination and preserves sample integrity during analysis. The assembly was validated by first analyzing polystyrene at cryogenic temperatures to ensure its functionality. Subsequent tests on Cupriavidus necator H16—bacteria capable of producing polyhydroxyalkanoates (PHAs), a type of biodegradable polyester—demonstrated the system's efficiency. PHAs, which are of interest as a sustainable alternative to petrochemical plastics, can be produced from industrial waste streams. Our results show that the combination of cryo-SEM and cryo-Raman is suitable for studying these microorganisms and permits a deeper understanding of the properties of polymer granules in microbial cells. This integrated approach represents a significant advance in semi-correlative imaging, providing more efficient and detailed analyses for future research in microbial physiology production and related fields.

Recent advances in volumetric camera calibration techniques have established non-invasive methods for multi-camera setups. Laser-beam methods, particularly those introduced by Hardege et al (2022 Proc. Int. Symp. on the Application of Laser and Imaging Techniques to Fluid Mechanics pp 1–15, 2023 Exp. Fluids64 193) and enhanced by Gunady et al (2024 Meas. Sci. Technol.35 105901), show significant promise. A critical component of these calibration procedures is the precise detection of laser beam intersections in 2D images. Current methodologies, including Hough-Line-Transformation, Template Matching, RANSAC, and 2D Gaussian beam fitting, exhibit limitations in both stability and accuracy, especially when encountering reflections, refractions, and other image artifacts. This paper introduces a deterministic approach for identifying straight light beams and accurately determining their intersections in grayscale 2D images. The proposed method exploits the Gaussian intensity distribution of laser beams, implements adaptive beam width detection, and eliminates the need for binarization, probabilistic sampling, or manual intervention such as template creation or parameter initialization. Extensive evaluation using both synthetic and real-world images demonstrates superior accuracy and robustness compared to existing methods. The presented approach reaches subpixel accuracy in noisy conditions at angles as low as 10^∘ which further increases for higher intersection angles.

The objectivity of three-dimensional imaging has made X-ray Computed Tomography (CT) an indispensable diagnostic method in clinical and industrial applications. Despite all the advances in the X-ray CT imaging, high-resolution three-dimensional reconstruction of defects or details in objects with large in-plane dimensions has been a fundamental challenge for the commercially available X-ray CT capability. An alternative scanning geometry, where X-ray source and a detector move synchronously irradiating a specimen at an angle from the rotation axis, has been proposed to address this challenge. To improve the reconstruction of radiography data obtained under the limited angular conditions, new designs of prior (or penalty) functions are developed. The new Gaussian prior targets reduction of shape distortion artifacts, common for laminographic reconstructions, as well as improving edge contrast and defect detection. Prior function performance is demonstrated on the computational phantom that contains worst-case scenario defects and simulated using limited angular data CT geometry. Gaussian prior' superior quantitative detection of defects is shown by the line profile plots as compared to Hyperbola prior. Performance of the reconstruction algorithm with the novel prior function is further verified under the real CT scan conditions using a novel microfocus X-ray CT scanning device that rotates X-ray source and moves the detector synchronously while the X-ray source and detector stay on the same opposite side of the test specimen (Inclined CT). This device allows scanning of critical areas of up to 3-meter-wide and potentially unlimited-length specimens with the high resolution required for identification of manufacturing and structural defects typical for modern composite structural elements.

A customised dual-wire sensor, incorporating both a cold-wire and a hot-wire, has been developed to enable simultaneous measurement of temperature and velocity at the same wall-normal location within a thermal boundary layer. This study details the sensor's design, its in-situ calibration technique, and addresses potential sensor drift, offering guidance for its effective utilisation. While the results obtained from this sensor confirm the similarity between the wall-normal profiles for mean temperature and velocity, profiles of the temperature variance present a less obvious plateau in the logarithmic region when compared to the streamwise velocity variance. The simultaneously acquired instantaneous signals also reveal the markedly different characteristics of the streamwise velocity and temperature fluctuations at the edge of the boundary layer, highlighting the dissimilarity between velocity and temperature (with the latter exhibiting a unique one-sided distribution).

Railway track structural health monitoring (SHM) plays a critical role in modern railway systems. Axle box acceleration (ABA) signals serve as a valuable information source for track SHM. However, the relationship between ABA frequency components and train–track dynamics, especially the muti-tone harmonics interaction, remains underexplored. This paper investigates the frequency components of ABA signals with time–frequency analysis methods, including short-time Fourier transform, continuous wavelet transform, and wavelet synchro squeezed transform. Through experimental field studies on a ballasted railway track and an embankment–bridge transition zone, the interactions between the sleeper passing frequency (${f_{{\text{sp}}}}$) and wheelset rotation frequency (${f_{{\text{wr}}}}$) are identified as the result of multi-tone harmonic coupling. The revealed empirical coupling relationship is shown by ${f_{\text{c}}} = \left( {1/2} \right){C_1}{f_{{\text{sp}}}} \pm \left( {1/2} \right)$${C_2}{f_{{\text{wr}}}}$, which remains consistent across different train speeds on the embankment and could also be observed on bridge sections. Additionally, the influence of differential settlement in the transition zone on the collected ABA signals from experimental campaigns has also been considered. The novelty of this paper lies in the identification of multi-tone harmonic interactions in ABA signals, and the empirical validation of frequency coupling relationships across varying track conditions using the time–frequency analysis methods. These contributions give insights into practical applications for ABA signal analysis and potentially offer a guideline for future practical applications.

Camera calibration is a key component of three-dimensional particle tracking velocimetry (PTV) experiments, and its proper implementation is key to the success of the method. In this paper, we review and compare four different camera calibration models used in PTV experiments without volumetric refinement. One of the calibration models is new and provides an analytical inversion of the Soloff polynomial. The other three calibration models are taken from three established open source PTV frameworks: OpenPTV, MyPTV and proPTV. In particular, we present a general formulation of calibration models that allows their rigorous comparison and evaluation with respect to their 3D-to-2D projection errors and 2D-to-3D reconstruction errors. We compare the models and the calibration errors in three different tasks, including extrapolation and interpolation of marker points, using a realistic calibration of an experimental camera setup. In the end, we conclude with the pros and cons of each method in order to be able to choose the most suitable one for individual needs.