Search Results (87)

Uniaxial compression testing provides essential mechanical property characterization for intact rock specimens. The accuracy of specimen preparation critically affects compression test results through end-surface geometry deviations: parallelism, perpendicularity, and diameter tolerance. Specimen end-surface parallelism is affected by surface irregularities (e.g., protrusions, warping), whereas perpendicularity deviations indicate angular misalignment of the specimen with the loading axis. This study develops a 3D uniaxial compression model using RFPA3D, with rigid loading plates to simulate realistic boundary conditions. Three typical end-surface defects are modeled: protrusions (central/eccentric), grooves, and unilateral warping. Specimens with varying tilt angles are generated to evaluate perpendicularity deviations. Simulation results reveal that central end-surface protrusions induce: (1) localized stress concentration, which forms a dense core, and (2) pronounced wedging failure when protrusion height exceeds critical thresholds. Eccentric protrusions trigger characteristic shear failure modes, while unilateral warping causes localized failure through stress concentration at the deformed region. Importantly, end-surface grooves substantially alter stress distributions, generating bilateral stress concentration zones when groove width exceeds critical dimensions. Full article

This study systematically analyzes the dynamic behavior of bearing tilt-misalignment coupling faults in rotary joints and establishes a high-fidelity nonlinear dynamic model for a dual-support bearing–rotor system. By integrating Hertzian contact theory, the nonlinear contact forces induced by the tilt of the inner/outer rings and axial misalignment are considered, and expressions for bearing forces incorporating time-varying stiffness and radial clearance are derived. The system’s vibration response is solved using the Newmark-β numerical integration method. This study reveals the influence of tilt angle and misalignment magnitude on contact forces, vibration patterns, and fault characteristic frequencies, demonstrating that the system exhibits multi-frequency harmonic characteristics under misalignment conditions, with vibration amplitudes increasing nonlinearly with the degree of misalignment. Furthermore, dynamic models for single-point faults (inner/outer ring) and composite faults are constructed, and Gaussian filtering technology is employed to simulate defect surface roughness, analyzing the modulation effects of faults on spectral characteristics. Experimental validation confirms that the theoretical model effectively captures actual vibration features, providing a theoretical foundation for health monitoring and intelligent diagnosis of rotary joints. Full article

Roll misalignment in roll-to-roll (R2R) processes is a critical cause of lateral displacement and tension imbalance, leading to dimensional instability and surface defects in polymer films. Conventional analyses based on beam or camber models often require complex calibration and additional sensing, which can limit their applicability in real-time production environments. This study introduces a diagnostic approach that estimates web misalignment directly from side-to-side tension differences measured in roll-to-roll (R2R) systems. The method eliminates the need for additional sensors and complex geometric calibration, simplifying system setup. The correlation between tension imbalance, lateral displacement, and the equivalent misalignment angle was experimentally established. Our approach produced accurate predictions across various process conditions, including different roll misalignments and applied tensions, and we found that reducing tension “hunting” further enhances prediction stability. This study demonstrates that the proposed tension-based approach can complement existing systems and reduce the reliance on complex external sensing for diagnostic checks of misalignment. By simplifying alignment diagnostics, the method provides a practical route to enhance process setup, reduce downtime, and improve the uniformity of polymer films in continuous manufacturing. Full article

This study investigates torsional vibration characteristics in an aged coastal car ferry propulsion system using theoretical calculations based on the Matrix method alongside experimental measurements. While the measured torsional vibration at the propeller shaft remained within the limits, it was significantly higher than the calculated values, particularly at the 5th harmonic order excited by engine combustion. Negative torque peaks observed during transient clutch engagement caused gear hammering. Structural vibration analysis identified potential gearbox defects, such as wear or misalignment. Multiple torsional vibration calculation models were developed considering various degrees of degradation of the aged rubber blocks and viscous torsional damper. A model assuming that the damping capacity of damper drops to about 1%, corresponding to the specified values at 125 °C, produced results that closely reproduced the measured vibration characteristics. The finding, confirmed by an actual inspection, identifies viscous oil leakage and deterioration of the damper as the primary cause of excessive vibration. Prompt replacement of the viscous oil is recommended to improve torsional vibration behavior. Full article

In industrial settings, defect detection using deep learning typically requires large numbers of defective samples. However, defective products are rare on production lines, creating a scarcity of defect samples and an overabundance of samples that contain only background. We introduce ImbDef-GAN, a sample imbalance generative framework, to address three persistent limitations in defect image generation: unnatural transitions at defect background boundaries, misalignment between defects and their masks, and out-of-bounds defect placement. The framework operates in two stages: (i) background image generation and (ii) defect image generation conditioned on the generated background. In the background image-generation stage, a lightweight StyleGAN3 variant jointly generates the background image and its segmentation mask. A Progress-coupled Gated Detail Injection module uses global scheduling driven by training progress and per-pixel gating to inject high-frequency information in a controlled manner, thereby enhancing background detail while preserving training stability. In the defect image-generation stage, the design augments the background generator with a residual branch that extracts defect features. By blending defect features with a smoothing coefficient, the resulting defect boundaries transition more naturally and gradually. A mask-aware matching discriminator enforces consistency between each defect image and its mask. In addition, an Edge Structure Loss and a Region Consistency Loss strengthen morphological fidelity and spatial constraints within the valid mask region. Extensive experiments on the MVTec AD dataset demonstrate that ImbDef-GAN surpasses existing methods in both the realism and diversity of generated defects. When the generated data are used to train a downstream detector, YOLOv11 achieves a 5.4% improvement in mAP@0.5, indicating that the proposed approach effectively improves detection accuracy under sample imbalance. Full article

Background/Objectives: Morphological defects such as limb malformations, cranial asymmetries, loin deviations, jaw misalignments, and navel irregularities are associated with early culling and reduced productivity in beef cattle. In Bos taurus indicus such as Nellore, the genetic basis of these traits remains poorly characterized. This study aimed to investigate the genetic architecture of six morphological defects in Nellore cattle, namely feet and legs malformation, chamfer asymmetry, fallen hump, loin deviation, jaw misalignment, and navel irregularities, via a genome-wide association study (GWAS) approach tailored for binary traits. Methods: Depending on the trait, the number of genotyped animals analyzed ranged from 3369 to 23,206, using 385,079 SNPs (after quality control). Analyses were conducted using a linear mixed model framework adapted for binary outcomes. Results: Significant associations were identified for four traits: feet and legs, chamfer, hump, and loin. No significant markers were detected for jaw or navel defects, likely due to lower sample sizes and trait incidence. Gene annotation revealed 49 candidate genes related to feet and legs, 4 for chamfer, 4 for hump, and 6 for loin. Conclusions: Candidate genes were enriched for biological functions, including bone remodeling, muscle development, lipid metabolism, and epithelial organization. Overlaps with QTL related to conformation, feed intake, reproductive traits, and carcass quality were also observed. These findings provide novel insights into the genetic control of morphological defects in Nellore cattle and may inform breeding strategies aimed at improving structural soundness. Full article

Although numerous studies have used ML to predict gear transmission error, few have provided a normalized, interpretable risk metric for early tolerance assessment. This work fills that gap by proposing the Tonal Risk Index (TRI). Kinematic Transmission Error (KTE) is a well-established primary excitation source of tonal gear noise in electric vehicle drivetrains. This study introduces the TRI, a novel, dimensionless indicator that quantifies relative tonal noise risk directly from predicted KTE values. We employ a large-scale dataset of 39,984 Monte Carlo simulations comprising 15 manufacturing tolerance and process-shift variables, with KTE values as the target. Baseline linear regression failed to capture the strongly non-linear relationships between tolerances and KTE (R² ≈ 0), whereas non-linear models—Random Forest and XGBoost—achieved high predictive accuracy (R² ≈ 0.82). Feature importance analysis revealed that pitch error, radial run-out, and misalignment are consistently the most influential parameters, with notable interaction effects such as pitch error × run-out and misalignment × form-defect shift. The TRI normalises predicted KTE values to a 0–1 scale, enabling rapid comparison of tolerance configurations in terms of tonal excitation risk. This approach supports early-stage design decision-making, reduces reliance on high-fidelity simulations and physical prototypes, and aligns with sustainability objectives by lowering material usage and energy consumption. The results demonstrate that data-driven surrogate models, combined with the TRI metric, can effectively bridge the gap between manufacturing tolerances and NVH performance assessment. Full article

This article addresses the monitoring and diagnosis of high-speed spindles (HSM) used in CNC grinding machines, emphasizing the importance of the real-time evaluation of their dynamic behavior during commissioning. Due to the complexity of these dynamic phenomena, especially at high speeds (up to 150,000 RPM), common faults such as bearing wear, imbalance, or misalignment can lead to catastrophic failures and high repair costs. An original method is proposed, based on synchronous envelope vibration analysis (SEVA) using the Hilbert transform, to detect mechanical defects in both low-frequency domains (imbalance, mechanical looseness) and high-frequency domains (bearing faults). The system includes vibration, temperature, and speed sensors, and the experimental protocol involves step-by-step monitoring from 10,000 to 90,000 RPM. Through synchronous FFT analysis and IFFT, critical frequencies and their impacts on machining quality are identified. The method enables the accurate fault diagnosis of new or refurbished spindles under real industrial conditions, reducing downtime and production losses. The method involves both local and remote real-time monitoring and diagnosis using a remote data center protocol. Full article

The construction of pumped storage power stations (PSPSs) is undergoing rapid expansion globally. Detecting operational faults and defects in pumped storage units is critical, as effective diagnostic methods can not only identify fault types quickly and accurately but also significantly reduce maintenance costs. This study leverages the symmetry characteristics in the vibration signals of pumped storage units to enhance fault diagnosis accuracy. To address the challenges of selecting the key parameters (e.g., decomposition level and penalty factor) of the variational mode decomposition (VMD) algorithm during vibration signal analysis, this paper proposes an algorithm for an improved subtraction-average-based optimizer (ISABO). By incorporating piecewise linear mapping, the ISABO enhances parameter initialization and, combined with a balanced pool method, mitigates the algorithm’s tendency to converge to local optima. This improvement enables more effective vibration signal denoising and feature extraction. Furthermore, to optimize hyperparameter selection in the bidirectional long short-term memory (BILSTM) network—such as the number of hidden layer units, maximum training epochs, and learning rate—we introduce an ISABO-BILSTM classification model. This approach ensures robust fault diagnosis by fine-tuning the neural network’s critical parameters. The proposed method is validated using vibration data from an operational PSPS. Experimental results demonstrate that the ISABO-BILSTM model achieves an overall fault recognition accuracy of 97.96%, with the following breakdown: normal operation: 96.29%, thrust block loosening: 98.60%, rotor-stator rubbing: 97.34%, and rotor misalignment: 99.59%. These results confirm that the proposed framework significantly improves fault identification accuracy, offering a novel and reliable approach for PSPS unit diagnostics. Full article

The efficacy of in vitro fertilization (IVF) is significantly hindered by early embryonic developmental failure and oocyte maturation arrest. Recent findings in reproductive genetics have identified several oocyte-specific genes—TUBB8, KIF11, and CKAP5—as essential regulators of meiotic spindle formation and cytoskeletal dynamics. Mutations in these genes can lead to significant meiotic defects, fertilization failure, and embryo arrest. The links between genotype and phenotype, along with the underlying biological mechanisms, remain inadequately characterized despite the increasing number of identified variations. This systematic review was conducted in accordance with PRISMA 2020 guidelines. Relevant papers were retrieved from the PubMed and Embase databases using combinations of the keywords “TUBB8,” “KIF11,” “CKAP5,” “oocyte maturation arrest,” “embryonic arrest,” and “IVF failure.” Studies were included if they contained clinical, genomic, and functional data on TUBB8, KIF11, or CKAP5 mutations in women undergoing IVF. Molecular data, including gene variant classifications, inheritance models, in vitro tests (such as microtubule network analysis in HeLa cells), and assisted reproductive technology (ART) outcomes, were obtained. Eighteen trials including 35 women with primary infertility were included. Over fifty different variants were identified, the majority of which can be attributed to TUBB8 mutations. TUBB8 disrupted α/β-tubulin heterodimer assembly due to homozygous missense mutations, hence hindering meiotic spindle formation and leading to early embryo fragmentation or the creation of many pronuclei and cleavage failure. KIF11 mutations resulted in spindle disorganization and chromosomal misalignment via disrupting tubulin acetylation and microtubule transport. Mutations in CKAP5 impaired bipolar spindle assembly and microtubule stabilization. In vitro validation studies showed cytoskeletal disturbances, protein instability, and dominant negative effects in transfected animals. Donor egg IVF was the sole effective treatment; however, no viable pregnancies were documented in patients with pathogenic mutations of TUBB8 or KIF11. TUBB8, KIF11, and CKAP5 are essential for safeguarding oocyte meiotic competence and early embryonic development at the molecular level. Genetic differences in these genes disrupt microtubule dynamics and spindle assembly, resulting in various aspects of oocyte maturation and fertilization. Functional validation underscores the necessity of routine genetic screening for women experiencing unresolved IVF failure, as it substantiates their causal role in infertility. Future therapeutic avenues in ART may be enhanced by tailored counseling and innovative rescue methodologies like as gene therapy. Full article

In modern manufacturing, ensuring quality control for geometric features is critical, yet detecting anomalies in circular components remains underexplored. This study proposes a real-time defect detection framework for metal parts with holes, optimized for deployment on a Raspberry Pi 5 edge device. We fine-tuned and evaluated three deep learning models ResNet50, EfficientNet-B3, and MobileNetV3-Large on a grayscale image dataset (43,482 samples) containing various hole defects and imbalances. Through extensive data augmentation and class-weighting, the models achieved near-perfect binary classification of defective vs. non-defective parts. Notably, ResNet50 attained 99.98% accuracy (precision 0.9994, recall 1.0000), correctly identifying all defects with only one false alarm. MobileNetV3-Large and EfficientNet-B3 likewise exceeded 99.9% accuracy, with slightly more false positives, but offered advantages in model size or interpretability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that each network focuses on meaningful geometric features (misaligned or irregular holes) when predicting defects, enhancing explainability. These results demonstrate that lightweight CNNs can reliably detect geometric deviations (e.g., mispositioned or missing holes) in real time. The proposed system significantly improves inline quality assurance by enabling timely, accurate, and interpretable defect detection on low-cost hardware, paving the way for smarter manufacturing inspection. Full article

Spondylocostal dysostosis (SCDO) is a group of rare genetic disorders characterized by segmental vertebral defects and rib deformities due to congenital misalignment, fusion, or reduction in the number of ribs. The causes of the disease have been found in seven genes, including DLL3 (SCDO1, OMIM 602768), MESP2 (SCDO2, OMIM 608681), LFNG (SCDO3, OMIM 609813), HES7 (SCDO4, OMIM 608059), TBX6 (SCDO5, OMIM 602427), RIPPLY2 (SCDO6, OMIM 616566), and DLL1 (SCDO7). Among these, SCDO4, characterized by a short trunk, short neck, and mild nonprogressive scoliosis, is a rare form of reported cases. SCDO4 is identified as caused by homozygous or compound heterozygous variants in the HES7 gene (NM_001165967.2; NP_001159439.1). This study reports a novel homozygous HES7 splice variant (c.43-9T>A) detected in an SCDO4 patient by whole-exome sequencing and confirmed by Sanger sequencing. This variant was evaluated as an acceptor loss variant in intron 1 in the HES7 transcript by in silico analysis and was inherited from the patient’s parent. This study also reviews previous reports to provide a comprehensive overview of SCDO and help us to understand the pathogenesis to develop future treatment strategies. Full article

Laser marking on wafers can introduce various defects such as inconsistent mark quality; under- or over-etching, and misalignment. Excessive laser power and inadequate cooling can cause burning or warping. These defects were inspected using machine vision, confocal microscopy, optical and scanning electron microscopy, acoustic/ultrasonic methods, and inline monitoring and coaxial vision. Machine learning has been successfully applied to improve the classification accuracy, and we propose a random forest algorithm with a training database to not only detect the defect but also trace its cause. Four causes have been identified as follows: unstable laser power, a dirty laser head, platform shaking, and voltage fluctuation of the electrical power. The object-matching technique ensures that a visible image can be utilized without a precise location. All inspected images were compared to the standard (qualified) product image pixel-by-pixel, and then the 2D matrix pattern for each type of defect was gathered. There were 10 photos for each type of defect included in the training to build the model with various labels, and the synthetic testing images altered by the defect cause model for laser marking defect inspection had accuracies of 97.0% and 91.6% in sorting the error cause, respectively Full article

Mechanical defects and sensor failures can substantially undermine the reliability of low-cost sensors, especially in applications where measurement inaccuracies or malfunctions may lead to critical outcomes, including system control disruptions, emergency scenarios, or safety hazards. To overcome these challenges, this paper presents a novel Self-X architecture with sensor redundancy, which incorporates dynamic calibration based on multidimensional mapping. By extracting reliable sensor readings from imperfect or defective sensors, the system utilizes Self-X principles to dynamically adapt and optimize performance. The approach is initially validated on synthetic data from tunnel magnetoresistance (TMR) sensors to facilitate method analysis and comparison. Additionally, a physical measurement setup capable of controlled fault injection is described, highlighting practical validation scenarios and ensuring the realism of synthesized fault conditions. The study highlights a wide range of potential TMR sensor failures that compromise long-term system reliability and demonstrates how multidimensional mapping effectively mitigates both static and dynamic errors, including offset, amplitude imbalance, phase shift, mechanical misalignments, and other issues. Initially, four individual TMR sensors exhibited mean absolute error (MAE) of 4.709°, 5.632°, 2.956°, and 1.749°, respectively. To rigorously evaluate various dimensionality reduction (DR) methods, benchmark criteria were introduced, offering insights into the relative improvements in sensor array accuracy. On average, MAE was reduced by more than 80% across sensor combinations. A clear quantitative trend was observed: for instance, the MAE decreases from 4.7°–5.6° for single sensors to 0.111° when the factor analysis method was applied to four sensors. This demonstrates the concrete benefit of sensor redundancy and DR algorithms for creating robust, fault-tolerant measurement systems. Full article

The electric drive is strategically placed in the power industry. It is exposed to wear and tear, defects, and constructional damage, as is any technical device. An information–analytical system is presented in this work. It performs the tasks of monitoring, diagnostics, general assessment of technical condition, and continuous assessment of energy and mechanical efficiency of the electric drive based on the analysis of immediate values of currents and voltages. The system modules are finished products with practical application, which are supported by experimental validation. This article contains a detailed description of the methods implemented in the system development, as well as a description of the laboratory bench and equipment used in our experiments. The information–analytical system is shown and proved on the basis of a fault reconstruction example with electric drive misalignment. According to the obtained results, recommendations for preventive control and proposals for development in this direction are formulated. Full article