Search Results (2,684)

Prevailing rolling bearing fault diagnosis frameworks based on long short-term memory (LSTM) are susceptible to noise interference under industrial strong-noise working conditions, suffering from insufficient feature extraction capability and low diagnostic precision. To address these limitations, this paper proposes a fault diagnosis framework integrating deep learning with signal processing, which consists of probabilistic principal component analysis (PPCA) for noise suppression, the autoregressive (AR) model for discrete interference elimination, spectral kurtosis (SK) for fault feature enhancement, and LSTM-based intelligent classification. To improve the signal-to-noise ratio (SNR) of vibration signals, the proposed method first estimates and suppresses noise via PPCA, and then eliminates periodic discrete frequency interferences represented by gear meshing components using the AR model. Following interference suppression, the SK method is adopted to implement multi-scale resonant frequency band screening and envelope demodulation. Finally, the demodulated features are learned by the LSTM to realize intelligent fault diagnosis of rolling bearings. This novel approach not only improves fault diagnosis accuracy but also enhances the model interpretability with the aid of signal processing techniques. Experimental results on the Case Western Reserve University (CWRU) and industrial field datasets demonstrate that the proposed method achieves superior accuracy compared with state-of-the-art approaches under various SNR conditions. It effectively mitigates the accuracy degradation of deep learning diagnostic models in strong-noise environments, providing a reliable technical solution for the intelligent diagnosis of rolling bearings. Full article

Hong–Ou–Mandel (HOM) interference is a standard method for characterizing photon indistinguishability. However, in practical systems, the measured interference visibility is not solely determined by intrinsic spectral–temporal mismatch but can also be affected by photon-number statistics, multiphoton background, and technical noise. Here, we establish an observable-level framework for HOM interference in practical sources. Within this framework, under stationary-field and fixed-detection conditions, the measured coincidence rate is decomposed into a delay-dependent interference term and a stationary background term that is independent of the HOM interference. The former is described by an effective spectral overlap function $\mathcal{G}\left(\tau \right)$, while the latter mainly originates from source statistics and additive imperfections. This separation shows that the dip profile and intrinsic depth are governed by overlap-limited interference, whereas the observed visibility can be further degraded by statistical and external backgrounds. For single-photon and weak coherent-state inputs, $\mathcal{G}\left(\tau \right)$ represents the effective temporal–spectral mode overlap. For the stationary thermal field, its delay-dependent envelope can be related to the first-order temporal coherence function $g^{\left(1\right)}\left(\tau \right)$, whereas photon bunching contributes separately through $g^2\left(0\right)$. We further clarify that post-selection suppresses background and reveals intrinsic interference more directly but cannot improve the intrinsic spectral–temporal overlap. This framework provides a unified basis for distinguishing genuine indistinguishability loss from measurement-induced visibility degradation. Full article

In high-voltage cable sheaths, incipient weak faults, such as jacket-damage grounding and high-resistance core–sheath breakdown, release little energy, rarely trigger protection, and can evolve into permanent faults. This study examines whether the topology-induced joint structure of multichannel sheath currents can support fault detection without fault samples. A Mahalanobis-distance-based method is formulated for the three-phase sheath circulating currents measured at a single cross-bonding box. An induction–leakage analysis relates the healthy joint structure to the bonding topology and shows how weak faults disturb it. A normalized pointwise Mahalanobis distance is combined with a threshold calibrated on separate healthy data and a K-consecutive-sample rule; the method requires no signal decomposition, and its per-sample cost is constant. On a PSCAD model of a 110 kV cross-bonded system, all 52 development fault cases are detected with confirmation delays below 6 ms; an independent sixteen-record healthy test is false-alarm-free after an envelope recalibration; boundary-grade faults under joint non-ideal conditions retain nine-fold margins; and per-line calibration extends the criterion to asymmetric and longer geometries. A twenty-seed Monte Carlo campaign shows zero noisy false alarms at all tested signal-to-noise ratios, the observable fault range being set by the disturbance-to-noise energy ratio of the acquisition chain. Full article

Pile-anchor retaining systems represent a fundamental support technology for urban deep excavations, where accurate quantification of anchor axial force is essential to achieving both structural safety and economical design. Here, we systematically assessed the predictive performance of several established design approaches—namely the static equilibrium method, the equivalent beam method, the earth pressure envelope method, an empirical chart-based method, and the CPD method—against in-situ monitoring data acquired from a deep excavation project. Under the site-specific conditions, the chart-based method delivered the closest agreement with the measured anchor forces. We further implemented a finite element model to perform a parametric sensitivity analysis, elucidating the influence of anchor embedment depth, excavation depth, and groundwater table position on anchor axial force. The results revealed that increasing anchor embedment depth produced only a marginal variation in anchor force, indicating weak sensitivity. In contrast, deepening the excavation produced a pronounced escalation in anchor force and significantly modulated pile bending moments. Elevating the depth to the groundwater table (i.e., lowering the water level) caused the anchor force to decline monotonically, with the rate of decrease progressively attenuating at greater depths, thereby demonstrating a moderate dependency on groundwater conditions. Full article

To address the limitations of poor adaptability and insufficient versatility of current surface capture technologies for autonomous underwater vehicles (AUVs) under high sea state conditions, this study proposes an ROV-based capture system equipped with guidance and clamping mechanisms for efficient and stable recovery of AUVs and similar floating targets in rough seas. This work is intended to provide technical support for solving AUV capture challenges in high sea states. Through a systematic investigation of capture methods and associated operational systems, the overall design of the dynamic surface AUV capture device is established. A three-dimensional model is developed to verify the feasibility of the overall operational sequence. Computational fluid dynamics (CFD) simulations are performed to analyze the hydrodynamic performance of the ROV carrier, with particular focus on the drag force and pressure distribution under flow velocities corresponding to Sea State 4 and below. The simulation results indicate that when the ROV inflow velocity reaches 3 m/s, the maximum drag force is approximately 5862 N and the maximum pressure on the frontal area is about 4570 Pa. Based on these data, the overall structural stability is verified, and the results confirm that the structure maintains adequate stability under the target operating conditions. Kinematic simulations are further conducted to investigate the collision force between the target AUV and the guidance/capture device under various initial attitudes and velocities in Sea State 4, thereby determining the overall capture tolerance envelope. Through design manual buffer parameter tuning and comparative improvement analysis, the collision force between the guidance device and the target is reduced by approximately 60%. Full article

Cuffless blood pressure estimation using the finger-pressing method remains sensitive to improper finger centering and inconsistent contact force, which degrade the accuracy of the oscillometric envelope and PPG signal morphology. This paper details the development of a research prototype that integrates three force sensors and a photoplethysmograph (PPG) sensor to quantify the finger–device interaction. This system is intended as a pre-clinical research tool rather than a clinically validated medical device. We implement a weighted centroid algorithm for center of pressure (CoP) feedback to guide geometric centering, alongside a Hybrid Ridge Regression model to estimate the total contact force. The system was evaluated on a pre-clinical pilot cohort of 48 healthy participants (1274 recordings), comparing inflationary (ramp-up) and deflationary (ramp-down) interaction modalities. Force calibration achieved a mean absolute error (MAE) of 1.2 g, with hardware analysis confirming a limited zero-load baseline drift of −0.29% over 50 days. The best single-recording calibrated model achieved a mean absolute error (MAE) of 5.55 mmHg (systolic) and 5.20 mmHg (diastolic), with a mean error (ME) ± standard deviation (SD) of $+0.50\pm 7.25$ and $+1.19\pm 6.47$ mmHg, respectively, in this pilot cohort, demonstrating the feasibility of the three-point force-sensing design with CoP tracking. Full article

The building industry is the largest consumer of energy and the largest source of carbon emissions, and the sustainable development of building envelopes is thus inevitable. Among the facade systems, metal cladding materials offer a number of benefits such as high durability, architectural freedom, and recyclability; however, the choice of these materials needs to be based on several performance aspects. This research presents a novel comprehensive approach for analyzing metal cladding options in a health care building through the integration of building energy simulation and multi-attribute decision analysis (MADA) techniques. A primary health care facility in Istanbul, Türkiye, was used as a case example. Eight metal cladding materials—steel, aluminum, copper, zinc, titanium, stainless steel, Corten steel, and magnesium alloy—were evaluated against various wall and insulation combinations. The assessment combined energy with physical–mechanical, thermal, acoustic, and sustainability indicators such as density, thermal conductivity, Young’s modulus, damping capacity, traffic noise insulation, service life, and recyclability. A series of building energy simulations was performed to estimate the effect of facade design options on yearly energy consumption, and the resulting data set was analyzed based on the CRITIC, LOPCOW and ALPAS methods. This method allows the comprehensive evaluation of metal cladding material on energy efficiency, structural strength, acoustic performance, durability, and circularity simultaneously. The results provide a practical decision support framework for sustainable facade material selection in health care and other energy-intensive buildings. Titanium emerged as the optimal metal cladding material, distinguished by its superior combination of low thermal conductivity, damping capacity, and recyclability under both CRITIC and LOPCOW weighting schemes. Comparative analysis across nine MADA methods (ρ = 0.932) and sensitivity analysis over 70 scenarios confirmed the robustness of this finding, with titanium retaining first place in 64 out of 70 perturbation scenarios. These outcomes provide materials engineering insight into how the mechanical, thermal, and durability characteristics of structural metals and alloys translate into differentiated in-service performance, offering evidence-based guidance for metal selection in facade applications. The outcomes reported relate to one health care facility located in Istanbul and demonstrate the potential of the novel framework for the specific investigated case but are not intended to be generalized across all building types and climatic zones or to provide universally applicable material rankings. Full article

Tailless underactuated hypersonic glide vehicles rely on elevons alone to control both roll and yaw. The dual-loop cascade architecture, in which differential elevon deflection regulates sideslip and the resulting sideslip response drives roll, exploits the high roll-to-yaw ratio to address lateral–directional underactuation. However, because the inner loop relies on sideslip angle feedback, its performance is sensitive to composite measurement bias. This study first analyzes lateral–directional open-loop divergence, the high roll-to-yaw ratio, and adverse yaw induced by differential elevons using a six-degree-of-freedom model and linearizations at multiple trim points, and then develops a baseline cascade controller employing stability-axis yaw-rate feedback. A first-order Gauss–Markov process with a constant offset is subsequently used to represent sideslip angle measurement bias. A linear extended state observer is introduced only in the velocity–bank angle outer loop to jointly estimate and compensate for the equivalent effect propagated by the bias, aerodynamic perturbations, center-of-mass offsets, and residual inner-loop dynamics. Observer parameters are selected through input direction identification, hierarchical two-dimensional parameter sweeps, and local grid verification. Local stability and boundedness near the design operating point are analyzed under bounded-input assumptions at the levels of the observer error, velocity–bank angle tracking error, and complete attitude control closed loop. Strictly paired Monte Carlo simulations and ablation experiments show that the proposed method effectively attenuates the propagation of sideslip angle measurement bias into the roll channel, improves velocity–bank angle tracking accuracy and response consistency under random uncertainties, and does not appreciably increase the required elevon position command envelope. Full article

This paper presents a smartphone-based sonar-sensing system for contactless monitoring of respiration and gesture-based user activity, using a single commercial phone with no hardware modification. A 20 kHz audio signal is transmitted from the phone’s speaker and reflections are captured by its microphone; motion-induced Doppler shifts are recovered using an envelope detection method that removes the need for a reference copy of the transmitted signal and improves robustness to transmitter frequency drift. Actuator experiments at a target distance around 50 cm demonstrated motion sensitivity at two conditions, 6 mm at 0.15 Hz and 2 mm at 0.6 Hz, with a measured signal-to-noise ratio of approximately 21.7 dB. Respiration rate detected by the system was compared against a pulse oximeter across two subjects, with an error within 0.1 Hz of the reference. Gesture-based interactions (tapping, scrolling, typing, talking) were analyzed using short-time Fourier transform features as a first step toward device authentication. Distance and direction estimation are outside the scope of this work and are identified as directions for future development. Full article

The American Association of State Highway and Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) live-load distribution-factor (DF) equations were calibrated on straight bridges, while their use for horizontally curved I-girder bridges is bounded by the L_as/R < 0.06 rad criterion in Article 4.6.1.2.4b of the AASHTO LRFD Bridge Design Specifications, 10th ed. (2024). This study quantifies their accuracy beyond that limit using the finite element method (FEM) in 35 three-dimensional CSiBridge models subjected to numerical consistency checks: three composite plate-girder arrangements (4–6 girders, 9.0 m deck) at central angles of 0–15°, with near-limit, span-transfer, sensitivity, and out-of-range extensions to 25°, under the AASHTO LRFD vehicular design live-load model (HL-93) and the Iraqi Class 100 wheeled military vehicle (HB115; 1150 kN). At the limit, curvature amplification is only 1.8–2.6%. Beyond it, the exterior-moment equations become unconservative almost immediately; FEM demand exceeds AASHTO by 21–29% at 15°, whereas the interior-shear equations remain conservative. A two-part correction factor (CF) of the form CF = R₀[1 + (a + a₁S/L)(L/R)] is proposed (R² ≈ 0.97) and predicts the withheld out-of-range cases within 3.3%. Within the tested envelope, exterior-girder amplification depends primarily on L/R; for HB115, its rate is about half that of HL-93. Direct CSiBridge reconstruction of two published 1/10-scale laboratory specimens shows good agreement in global deflection and moderate agreement in strain-based transverse distribution. Because full-scale measurements for the exact 38 m reference configuration were unavailable, this evidence is treated as external experimental benchmarking of the modeling methodology rather than complete validation of the full parametric matrix. Full article

Fault feature extraction for rotate vector (RV) reducers is hindered by weak impulsive components masked by noise, empirical parameter selection in conventional variational mode decomposition (VMD), and mode redundancy. To address these issues, an improved wild horse optimizer-based variational mode decomposition (IWHO-VMD) framework with effective-mode reconstruction is proposed. A two-stage search strategy and a composite fitness function integrating squared envelope spectrum (SES) negentropy, reconstruction error, and an effective-mode penalty are used to optimize the VMD mode number and penalty factor. Fault-related intrinsic mode functions are then selected using SES negentropy and normalized energy ratio, followed by effective mode reconstruction and Hilbert envelope spectrum analysis. The method is validated on two self-built RV reducer datasets involving rolling-element wear and planetary gear tooth-surface wear, together with a public crankshaft wear dataset, and compared with several optimization methods. It achieves an average characteristic-frequency identification accuracy of 98.95% across the three datasets. On the comparative dataset, IWHO-VMD achieves the lowest fitness value and reduces the number of convergence iterations by 25.0–50.0%; with effective-mode reconstruction, its average identification accuracy reaches 99.03%. The proposed framework improves VMD parameter adaptivity and enhances fault-feature representation, providing a reliable basis for RV reducer condition monitoring. Full article

To overcome the nonlinear and non-stationary characteristics of rolling bearing vibration signals and the challenge of extracting incipient weak fault features, this paper proposes a joint fault diagnosis method based on Variational Mode Decomposition (VMD), Maximum Correlated Kurtosis Deconvolution (MCKD) and Support Vector Machine (SVM). Different from most existing studies that separately optimize individual stages of the fault diagnosis workflow, the proposed method adopts a multi-strategy enhanced grey wolf algorithm (S-LE-EGWO) to collaboratively tune parameters for multiple key modules within a unified framework. Firstly, taking the minimum envelope entropy as the fitness function, the S-LE-EGWO algorithm is utilized to optimize the mode number $K$ and penalty factor $\alpha$ of VMD to realize adaptive decomposition of vibration signals. Secondly, kurtosis combined with the correlation coefficient is adopted to select effective. Intrinsic Mode Function (IMF), and the signal is reconstructed based on the screened components. Then, the S-LE-EGWO algorithm is employed to optimize the parameters of MCKD to realize effective extraction of periodic fault impulses. Finally, multi-dimensional fault features are extracted, dimension-reduced by Kernel Principal Component Analysis (KPCA), and fed into the optimized SVM classifier to complete fault identification. Feature-oriented mechanism analysis is carried out using simulation signals, and the proposed method is validated on the CWRU rolling-bearing dataset, with comparative investigations against four mainstream optimization-based diagnostic algorithms. The test results show that the proposed method can effectively mine weak fault features of bearings. Compared with other algorithms, the presented method achieves superior identification performance and possesses favorable recognition capability for incipient weak faults, which can realize the classification of bearing faults. Full article

Background/Objectives: Packing, flow, and aerosolization properties of a series of binary adhesive mixtures containing micronized or spray-dried drugs were investigated, and the relationships between these blend properties and the blend structure were studied. Methods: Micronized or spray-dried terbutaline sulfate and salbutamol sulfate were used as model drugs, and an α-lactose powder was used as the carrier. Binary mixtures with drug loads ranging from 2 to 20% were prepared. The bulk density, compressibility, permeability, and shearing properties of the carrier powder and mixtures were determined, along with the in vitro aerosolization propensity of the mixtures using two types of inhalers. Imaging of the mixtures was used to assess the blend structure. Conclusions: The particle engineering method gave differences in particle crystallinity and morphology. The development of the adhesive layer with drug load was broadly consistent with the blend state concept. Spray-dried particles, however, exhibited a higher propensity to localize within surface cavities on the carrier and produced a more voluminous enveloped adhesive layer. The spray-dried particles gave a higher bulk density, a lower Hausner ratio, comparable shear strength, and a lower angle of internal friction. Aerosolization performance, including metrics such as fine particle fraction (FPF), depended on inhaler design; nevertheless, for both inhalers, aerosolization behavior was influenced by blend state and physicochemical properties of the drug. At low drug loads, spray-dried particles dispersed to a lower degree, while at high drug loads, the dispersion performance of the two particle types converged. For example, at an intermediate drug load of 7.4%, the FPF was about 20% for the spray-dried drugs and about 30% for the micronized drugs using the Screenhaler device, while the corresponding FPF:s were about 25% and 50% for spray-dried drugs and 40% and 55% for crystalline drugs using the Monodose inhaler. Overall, the physical characteristics of the drug particles were found to influence the structural evolution of the blends, as well as their mechanical and aerosolization properties. Full article

Water-lubricated bearings (WLBs) may exhibit marked friction-induced vibration during low-speed and heavy-load operation as hydrodynamic lubrication becomes insufficient. However, the load dependence of the low-speed operating limit and associated vibration characteristics remains insufficiently understood. In this study, a WLB was tested under specific pressures of 0.28, 0.42, 0.56, and 0.84 MPa during stepwise deceleration from 20 to 6 r/min. A joint three-standard-deviation criterion based on root mean square and peak-to-peak acceleration was used to identify the first measured speed point with marked vibration amplification. The coefficient of friction, time-domain features, spectral energy distribution, envelope characteristics, and FSI-based limiting hydrodynamic capacity were analyzed. The first vibration amplification points occurred at 6, 8, 10, and 10 r/min, respectively, accompanied by audible abnormal sound used only as qualitative corroboration. The onset responses varied from isolated or repeated bursts to pronounced medium-high-frequency impulsive excitation and quasi-periodic low-frequency amplitude modulation. The limiting hydrodynamic capacity calculated at a prescribed eccentricity ratio decreased with increasing load and was lower at the onset condition than at the adjacent pre-onset condition. This vibration-based framework provides an operational method for identifying low-speed operating boundaries related to loads and may support operating-condition selection and early warning of abnormal vibration in WLB systems. Full article

To address the challenging problem of extracting fault information from rolling bearings, this study proposes a novel hybrid method that integrates singular value decomposition (SVD), Dung Beetle Optimization (DBO), and Variational Mode Decomposition (VMD) for enhanced fault feature extraction. The method consists of three key steps: (1) adaptive SVD denoising via singular-value difference spectrum, where the collected signal is first reconstructed into a phase-space Hankel matrix, and the maximum extremum point of its singular value difference spectrum is identified as the optimal rank order for SVD denoising; (2) DBO of VMD parameters using minimum envelope entropy—to prevent over-decomposition, where DBO is employed to automatically determine the optimal number of modes K by minimizing the envelope entropy, and the signal is decomposed into a set of Intrinsic Mode Functions (IMFs); (3) kurtosis-peak-to-peak joint screening of sensitive IMFs, in which a joint criterion based on kurtosis and peak-to-peak values is applied to select the IMF that contains the richest fault information. This sensitive IMF is then subjected to Hilbert envelope spectrum analysis to accurately extract the fault characteristic frequency of rolling bearings. Experimental results from two different test rigs demonstrate that the proposed method can more effectively highlight periodic fault impulses and identify fault types, offering a reliable approach for rolling bearing fault feature extraction. Full article