Search Results (3,221)

The mathematical characterization of non-stationary signals remains a significant challenge, particularly when impulsive components are obscured by high-dimensional noise and structural coupling. This paper proposes an application-driven mathematical methodology for a learnable discrete wavelet transform (LDWT) that combines classical multi-resolution analysis with task-optimized data-driven adaptivity. Rather than introducing entirely new foundational theory, our approach strategically relaxes constraints from orthogonal wavelet theory within the non-perfect reconstruction filter bank framework, enabling controlled spectral decomposition optimized for supervised fault diagnosis. We introduce a specialized regularization term based on the half-band property to ensure spectral complementarity and minimize cross-band correlation, while a Jacobian-based stabilization approach is formulated to ensure the convergence of filter coefficients during optimization. The proposed algorithmic architecture, LDBRFnet, features a dual-branch encoder system designed to capture the mathematical synergy between sub-band-level global statistics and time-domain local morphology. This dual-view representation effectively mitigates feature leakage and overconfidence in classification. Theoretical analysis and numerical experiments demonstrate that the learned filters satisfy the frequency-shift property and maintain robust spectral partitioning even under low signal-to-noise ratios. Validation on complex vibration datasets confirms that the framework achieves superior diagnostic accuracy (over 95.5%) and computational efficiency, reducing model parameters by 96.7% compared to state-of-the-art baselines. This work provides a generalizable mathematical approach for adaptive signal decomposition and robust pattern recognition in interdisciplinary applications. Full article

Steel moment-resisting frames exhibiting vertical geometric irregularities, particularly those with setback configurations, experience increased seismic demands due to stiffness discontinuities and complex dynamic interactions. These conditions present significant challenges for conventional vibration control strategies. This study introduces a performance-based optimization framework that utilizes the Circle-Inspired Optimization Algorithm (CIOA) to enhance the design of tuned liquid dampers (TLDs) in irregular steel structures. Structural responses are simulated in OpenSees, with a rheological model based on the Housner method employed to accurately capture fluid–structure interaction. Seismic performance is evaluated using a suite of real subduction-type ground motions, selected to represent the seismic hazard level of Armenia, Colombia, in accordance with the Conditional Scenario Spectra (CSS) methodology and the National Seismic Risk Model for Colombia. The optimization process considers the mean response across multiple ground-motion records to ensure robustness against seismic variability. Multiple time-domain objective functions are examined, including peak interstory drift, maximum displacement, and peak acceleration. The results indicate that objective functions related to interstory drift and displacement provide the most effective, stable, and consistent reductions in seismic demand across all scenarios, while acceleration-based objectives display greater sensitivity to record-to-record variability. These outcomes underscore the importance of objective function selection in determining both optimization stability and control effectiveness. The CIOA demonstrates rapid convergence, numerical robustness, and reliable performance, confirming its suitability as a computationally efficient and resilient optimization tool for the design of passive control systems in irregular steel structures exposed to high seismic hazard. Full article

Piezo-enhanced photocatalysis is progressively considered an eco-friendly technology for contaminant removal, harvesting not only solar energy but also mechanical vibrations found in nature. Multiferroic materials present a coupled effect of various properties and can potentially increase the applicability of this process. In this study, Cr- doped bismuth ferrite thin film was deposited on SrTiO₃ substrate by HiPIMS, and its photo-, piezo-, and piezo-photocatalytic efficiencies in Rhodamine B (RhB) degradation were analyzed. The highest removal percentage was found under the simultaneous exposure of visible light and mechanical vibrations, reaching 86.2% after 180 min. The calculated efficiencies for photo- and piezocatalysis were 12.2% and 83.7%, respectively. The rate constant (k) for piezo-photocatalysis was 16.1 times higher than that found during photocatalytic experiments. To assess the contribution of each reactive species to the decomposition process, different reagents were added to the Rhodamine B contaminated solution. The results revealed that when p-benzoquinone was used, the degradation efficiency declined significantly from 86.2% to 37.6%, suggesting that superoxide radicals (O₂^•−) play a key role in decomposing RhB molecules. The structural, chemical, optical, and ferroelectric changes caused by the catalytic processes were analyzed and linked to the proposed degradation mechanisms. The poor photocatalytic efficiency was linked to an improper band structure and an improper polarization orientation of the ferroelectric domains in the as-deposited film. The degradation mechanisms in piezo-photocatalysis were driven partly by the band bending caused by mechanical vibrations and partly by the reorientation of the induced polarization of the domains in the unstrained film. Full article

Providing reliable sensory feedback is one of the most challenging aspects of transfemoral prosthetics, motivating the development of intuitive vibrotactile interfaces capable of conveying information about limb position in real-time. The aim of this study was to develop a vibrotactile feedback prototype and examine which interstimulus intervals (ISIs) and vibration waveforms might best enhance recognition of sequential tactile patterns. The results will be used to inform the development of a prototype to be tested on participants with transfemoral amputation where prosthetic feedback is provided. A forearm-mounted six-actuator feedback system, encoding eight lower-limb configurations, was used in two experiments with healthy adults. Experiment 1 assessed recognition accuracy across ISIs from 10 to 110 ms, while Experiment 2 compared sinusoidal and square waveforms under matched conditions. Recognition accuracy was high across all tested conditions, with no significant effects of ISI (p = 0.79) or waveform type (p = 0.17). These results indicate that participants were able to interpret spatially distributed vibrotactile patterns even under rapid temporal sequencing and with differing signal shapes. The system therefore offers design flexibility for real-time prosthetic feedback, suggesting that fast update rates may be achievable without a statistically detectable reduction in perceptual clarity within the tested conditions. These findings provide practical guidance for developing robust, user-friendly sensory substitution systems intended to increase proprioceptive awareness in transfemoral prosthesis users. Full article

Bearing fault diagnosis is an important task for condition monitoring and predictive maintenance of rotating machinery. Nevertheless, many existing deep learning-based methods have difficulty in jointly modeling multi-scale fault characteristics, adaptively highlighting informative features, and maintaining robustness under noisy measurement conditions. To address these issues, this study presents MDCAD-Net, a multi-dilated convolution attention denoising network that integrates multi-scale temporal feature extraction, attention-based feature refinement, and explicit noise suppression within an end-to-end learning framework. Parallel dilated convolutions with different dilation rates are employed to capture short-duration transient impulses as well as long-range periodic patterns in vibration signals. Channel-wise feature recalibration using squeeze-and-excitation networks and spatial-temporal attention via a convolutional block attention module are combined to enhance informative representations. In addition, a denoising block with gated attention and residual connections is introduced to reduce noise interference while retaining fault-related signal components. Experiments conducted on the Case Western Reserve University bearing dataset show that the proposed method achieves a classification accuracy of 98.93% and yields competitive performance compared with several commonly used deep learning models. Ablation studies and feature visualization results further illustrate the contributions of the individual components and the separability of the learned feature representations under noisy conditions. The results indicate the potential of the proposed framework for practical bearing fault diagnosis under noisy operating conditions. Full article

In the aerospace industry, thin-web gears are preferred for achieving high power-density transmission. However, thin-webbed structures always lead to out-of-plane resonance during the transmission process, which commonly happens in helical gears, manifesting as severe vibration at a specific rotational speed. To address this, a shaft–web–ring dynamic model is proposed. The shaft, gear web, and gear ring are modelled based on the Timoshenko straight beam, Mindlin plate, and Timoshenko bent beam theory. Simultaneously, the potential energy caused by the time-varying meshing stiffness is coupled to the gear ring. The kinetic and potential energies of each discretized finite element of the components are derived based on elastic deformation theory, and the governing equations of each element are obtained using Hamilton’s principle. The model is verified through a modal experiment. The comparison with traditional rotor-gear models has demonstrated the significance of gear body flexibility in helical gears with thin webs. The effects of the web thickness and helix angle on dynamic response are studied, revealing that gear web elasticity and an appropriately high helix angle can effectively reduce vibrations at the support bearing, prevent excessive vibrations, and contribute to vibration and noise reduction in the transmission system. Full article

Research on methods for detecting microwave-based noncontact vibration has garnered significant attention in recent years. To simplify system complexity and reduce costs, which would enable broader application of radar technology in daily life, we propose a low-complexity, high-precision continuous-wave (CW) radar system for noncontact vibration detection. This system employs a hardware-based approach for phase comparison to extract vibration information, enabling simultaneous detection of both vibration amplitude and frequency under a CW radar architecture. In this study, we establish a phase discrimination error model to characterize the inconsistent detection sensitivity of the hardware phase comparator in different phase intervals, and we further propose a phase compensation scheme to mitigate the nonlinearity of phase discrimination and the “null-point” problem in continuous phase comparison, consequently improving the sensitivity and precision of the proposed radar system. Through loudspeaker vibration and experiments on human vital signs, the system maintains a vibration amplitude detection accuracy above 90.3% within 1.8 m while achieving respiratory rate and heartbeat rate detection accuracies of 96.34% and 98.02%, respectively. Full article

To explore the influence of reaction wheel perturbations on the image quality of a space optical telescope, a comprehensive dynamic model of a precision optical system was established, and an optical-mechanical integrated analysis approach was adopted to calculate the line-of-sight (LOS) error of the optical telescope under reaction wheel disturbances and determine the key mode that contributes the most significantly to the LOS error based on the entire satellite hierarchy. The rigid body displacements and mirror deformations generated by the optical reflector under reaction wheel perturbations were analyzed in synergy with the optical system to illuminate the impact of reaction wheel perturbations on the imaging quality of the optical imaging system. Finally, a satellite micro-vibration experiment was conducted, and the relative errors between the simulation and the experiment of the optical telescope’s object space axis of LOS error under key modes were 9.34% and 6.52% respectively, thereby validating the accuracy of the simulation analysis. The analysis outcomes offer direct engineering guidance for the structural layout and vibration isolation design of on-orbit optical satellites. The core innovations of this study are primarily manifested in three aspects: First, a full-link optomechanical integrated analysis framework is established, which synergistically accounts for the coupled effects of mirror rigid-body displacement and surface deformation on imaging performance, thereby addressing the limitations of single-factor analysis in existing research. Second, the framework is validated through satellite micro-vibration experiments, with the relative errors between simulation and experimental results both below 10%, ensuring the engineering reliability of the proposed method. Third, the scope of micro-vibration analysis is extended across scales from macroscopic space optical systems to micro/nano-scale precision optical devices. Beyond its application to space telescopes, this framework can be directly generalized to micro-optical systems sensitive to micro-vibrations, including augmented reality (AR) near-eye displays, microlithography objectives, and MOEMS-based micro-devices. The proposed framework is universal and can be directly extended to micro-optical systems such as MOEMS-based devices, near-eye display modules, and photonic crystal optomechanical systems, providing a standardized analytical approach for anti-vibration design in micro-system engineering. Full article

Unmanned aerial vehicles (UAVs) operating in safety-critical missions require effective anomaly detection methods to identify propulsion-system faults before they cause catastrophic failures. However, current vibration-based diagnostic models typically rely on datasets representing only discrete, isolated fault states, and do not capture the continuous structural degradation that occurs during real flight operations. To address this gap, this study proposes a severity-ordered vibration data augmentation framework for anomaly detection in rotary-wing UAV propulsion systems. Controlled experiments were conducted under healthy, tape-induced imbalance, scratch, and cut propeller conditions using stepped throttle excitation from 10% to 100% in 10% increments, with 40 s per level. A severity-ordered arrangement strategy based on throttle level and a robust peak-to-peak severity metric generated approximately 7.5 h of augmented vibration data per axis, representing a continuous degradation trajectory. Three-axis continuous wavelet transform (CWT) scalograms of size $48\times 96\times 3$ were used to train an unsupervised anomaly detection framework. Comparative experiments with Isolation Forest, One-Class SVM, and LSTM–AE demonstrated that the proposed Convolutional Neural Network (CNN)–Bidirectional Gated Recurrent Unit (BiGRU)–State-Space Model (SSM)–Autoencoder (AE) architecture achieved the best performance, reaching 0.9959 precision, 0.4428 recall, 0.6131 F1-score, and 0.9284 Area Under the Receiver Operating Characteristic Curve (AUROC). The ablation study further showed that incorporating temporal modeling and state-space dynamics improves detection robustness compared with CNN–AE and CNN–BiGRU–AE baselines. These results show that combining severity-ordered augmentation with deep temporal learning improves progressive propulsion anomaly detection in UAV vibration monitoring. This work introduces a methodology that connects rotor dynamics principles with deep learning, providing a continuous degradation manifold that improves early-stage detection and condition monitoring of UAV propulsion systems. Full article

To improve the feature representation and cross-condition generalization of bearing fault diagnosis, this paper proposes an adaptive multi-sensor fusion network with a skip fusion module and a parameter-efficient dual-convolution diagnosis block. The vibration and current signals are first augmented by overlapping segmentation and transformed into the frequency domain using FFT. Multi-scale depthwise convolutions are then employed in parallel branches to capture fault patterns at different receptive fields, and an attention-based skip fusion mechanism selectively aggregates cross-sensor features for complementary enhancement. After fusion, self-calibrated convolution and dilated convolution are alternately applied to strengthen discriminative representation without increasing model complexity. Experiments on multiple bearing datasets under both constant and variable operating conditions demonstrate that the proposed method achieves consistently higher accuracy and robustness than representative CNN-based baselines, verifying its effectiveness for practical bearing fault diagnosis. Full article

In this study, nanolubricants based on SAE 5W-30 mineral oil were formulated using oleic acid-functionalized graphene nanoplatelets (GNPs), and their colloidal stability, rheological behaviour, thermal stability, and tribological performance under cold rolling conditions were systematically investigated. The nanolubricants were prepared at GNP concentrations of 0.05, 0.1, 0.2, 0.4, and 0.6 wt%. FT-IR analysis confirmed successful functionalization, evidenced by the characteristic C=O band at approximately 1710 cm⁻¹ and changes in CH₂ stretching vibrations in the 2850–3000 cm⁻¹ range. UV–VIS results indicated initially homogeneous dispersions; however, after three days, relative concentrations decreased to 95%, 90%, and 75% for 0.05, 0.2, and 0.6 wt% GNPs, respectively. Viscosity measurements showed minimal variation at low concentrations, with only a 0.64% increase at 0.2 wt% compared to the base oil. TGA revealed enhanced oxidative stability at low GNP contents, with the oxidation onset temperature increasing from 205.3 °C to 207.2 °C at 0.05 wt%, while a marked decline was observed at higher concentrations (176.8 °C at 0.6 wt%). In cold rolling experiments at a 3% reduction ratio, the rolling force was measured at 1341 N/mm with the neat lubricant, decreasing to 1210 N/mm with a lubricant containing 0.1 wt% GNPs, corresponding to an approximate 10% reduction. Compared with dry conditions, this reduction was approximately 21%. Surface roughness and 3D topography analyses further showed that GNPs-containing lubricants reduced asperities and promoted the formation of a more uniform tribofilm. At low concentrations, the improved lubrication performance of oleic acid-functionalized graphene nanoplatelets is attributed to their homogeneous dispersion in mineral oil, where physically adsorbed oleic acid improves colloidal stability by reducing agglomeration and promotes the formation of a stable tribofilm, facilitating interlayer sliding under boundary lubrication conditions. Overall, the findings demonstrate that oleic acid-functionalized GNPs, when used at optimal concentrations, significantly enhance both lubricant stability and cold rolling performance. Full article

The load of the elastic tooth drum-type pepper harvester is a key parameter affecting harvesting efficiency and quality. Real-time analysis and prediction of drum load are crucial for stabilizing harvester operation and optimizing performance. Existing research focuses on either machine vision-based image analysis, which is difficult to collect in the field, or parameter-mapping methods, which suffer from time lag. This study proposes a GARCH-KPCA-ATLSTM method for load prediction, combining the generalized autoregressive conditional heteroskedasticity (GARCH) model, kernel principal component analysis (KPCA), and attention-enhanced long short-term memory (ATLSTM). EMD is first applied to denoise and reconstruct the load signal, removing mechanical vibration and other interferences. Conditional heteroskedasticity is confirmed, and the GARCH series (one symmetric and three asymmetric models) is introduced to extract fluctuation features. KPCA reduces dimensionality, removing redundant information and saving 2.91 s in computation while slightly improving accuracy. Additive attention in LSTM emphasizes critical information, enhancing learning of nonlinear relationships and further improving prediction. Comparative experiments demonstrate the model’s reliability. The method achieves RMSE = 0.911, MAE = 0.682, MBE = −0.025, MAPE = 1.147%, R² = 0.968, with a runtime of 2.023 s, confirming high accuracy and stability. This study provides a theoretical and technical foundation for real-time load prediction of pepper harvesters. Full article

Accurate classification of spindle rotational errors is critical for ensuring machining precision and operational reliability of CNC machine tools. However, existing methods face challenges in extracting discriminative feature information from vibration signals due to small inter-class differences and complex electromechanical interference. This paper proposes a novel deep learning model, MFAFENet, based on multi-sensor collaboration and multi-scale feature information adaptive fusion. Vibration signals from three mounting positions are transformed into time-frequency information representations via Short-time Fourier Transform. The proposed network adaptively fuses multi-scale feature information from parallel branches with different kernel sizes through a branch attention mechanism. An efficient channel attention module is then incorporated to recalibrate channel-wise feature responses. The cross-entropy loss function is employed to optimize the network parameters during training. Experiments on a spindle reliability test bench demonstrate that MFAFENet achieves 93.37% average test accuracy, outperforming other comparative methods. Ablation and comparative studies confirm the effectiveness of each module and the clear advantage of adaptive fusion over fixed-weight multi-scale methods. Multi-sensor fusion further improves accuracy by 7.23% over the best single-sensor setup. The proposed method establishes an effective end-to-end mapping between vibration signals and rotational errors, providing a promising solution for high-precision spindle condition monitoring. Full article

This study investigates magnetic harvesting of Chlorella vulgaris cultivated under saline and wastewater conditions using Fe₃O₄ and polyethylene-glycol-coated Fe₃O₄ (Fe₃O₄@PEG) nanoparticles synthesized by ultrasound-assisted coprecipitation. TEM showed agglomerated, quasi-spherical particles with mean diameters of 13 ± 1 nm (Fe₃O₄) and 15 ± 1 nm (Fe₃O₄@PEG). FTIR confirmed the Fe–O vibrational bands of magnetite and the characteristic PEG vibrations in the coated sample. VSM measurements indicated superparamagnetic behavior, with saturation magnetizations of 72.74 emu/g for Fe₃O₄ and 32.25 emu/g for Fe₃O₄@PEG. SEM–EDX of native and functionalized cells verified nanoparticle attachment on the algal surface. Magnetic separation experiments (OD684) showed a decrease in supernatant absorbance with increasing nanoparticle dose, consistent with biomass removal; the PEG-coated system showed a lower apparent biomass concentration after functionalization. Full article

Recent advances in machine design, automation, and industrial digitalization have transformed Coordinate Measuring Machines (CMMs) from standalone inspection devices into fully integrated elements of automated manufacturing systems. In the automotive sector, CMMs increasingly operate in workshop, near-line, and in-line environments, interacting with production equipment and contributing directly to process control and zero-defect manufacturing strategies. This paper presents a structured methodology for the industrial deployment of automated CMMs in automotive mechanical manufacturing. The proposed approach is illustrated through an industrial use case involving the dimensional inspection of mechanically machined components under real production conditions. The methodology addresses machine design selection, sensor configuration, environmental constraints, and multi-axis architectures, as well as validation and acceptance procedures based on the ISO 10360 series. Particular attention is given to the integration of CMMs within automated manufacturing systems, including robustness against thermal variations, vibrations, and contamination, and the use of metrological data for feedback to machining processes. Rather than introducing new metrological principles, the proposed approach focuses on the structured integration of established engineering practices into a coherent lifecycle-based deployment framework. Based on industrial experience, the proposed methodology is illustrated through an industrial case study to support the reliable of automated dimensional inspection, reduce measurement-related risks, and support the integration of CMMs as active components of modern automated manufacturing systems. Full article