Search Results (1,588)

Rolling bearings are critical components in rotating machinery, and their failures may cause unexpected downtime and safety risks. However, conventional deep diagnostic models are often difficult to deploy on resource-constrained edge devices because of their high computational cost and memory consumption. This paper proposes a lightweight time–frequency attention network (LTFANet) for multi-fault diagnosis of rolling bearings on an edge platform. The proposed model directly processes one-dimensional vibration signals and employs multi-scale depthwise separable convolutions to capture impact and periodic fault features with low computational complexity. A lightweight frequency branch is introduced to enhance fault-frequency representation, while an efficient channel attention module adaptively emphasizes fault-sensitive features. Moreover, a severity-aware multi-task extension is introduced to jointly identify the fault location and degradation level. To further improve edge inference efficiency, knowledge distillation, structured pruning, and TensorRT-based acceleration are integrated into the deployment pipeline. Experiments on CWRU-10 and Paderborn achieve 97.20% and 90.25% accuracy, respectively, while LTFANet contains only 0.020 M parameters and requires 0.610 M FLOPs. Knowledge distillation increases the CWRU-10 accuracy to 98.50%, and the severity-aware extension achieves 95.18% severity accuracy. On the NVIDIA Jetson Nano, the pruned TensorRT FP16 implementation achieves an average inference latency of 0.520 ms and a throughput of 1923.08 samples/s. The framework provides an effective solution for real-time and low-cost bearing condition monitoring at the edge. Full article

Targeting the rock burst hazard induced by the hard roof key stratum during deep mining at the Mengcun Coal Mine in the Binchang mining area, this study takes the No. 403109 working face as the engineering background and systematically investigates the rockburst prevention mechanism and effectiveness of ground hydraulic fracturing through theoretical analysis, UDEC numerical simulation, and surface microseismic monitoring. The results indicate that fracturing pre-weakens the overlying key stratum, transforming its load-bearing mode from a long-beam rigid support to a segmented flexible support. This significantly reduces the cantilever length, lowers the accumulation of elastic strain energy, and enables flexible load transfer and stress redistribution in the overburden. Numerical simulations reveal that after fracturing, the breakage timing of the key stratum advances, the fragmentation size decreases, and the over-burden movement shifts from stepwise fracturing to sequential caving, with the stress concentration zone substantially narrowed. In the field, a total of 44 fracturing stages were implemented in wells MC-05L and MC-06L, creating a fracture network with an average fracture length of 317 m and an average fracture height of 55 m, achieving an effective stimulated volume ratio of 86.7%. During the mining period, microseismic events exhibited a median energy of only 868.14 J, characterized by high frequency and low energy. The average weighting interval was 13.69 m, the peak coal stress was controlled within 5.0–6.7 MPa, and the loads on roadway bolts and cables remained within safe limits. This study validates the source-control effect of ground hydraulic fracturing on working faces with strong rock burst risks in deep mining, providing a theoretical basis and engineering reference for mines with analogous conditions. Full article

Battery packs for new energy vehicles have evolved from simple load-bearing and protective assemblies into multifunctional safety structures integrating structural support, crash protection, thermal-runaway mitigation, flame retardancy, electrical insulation, electromagnetic interference shielding, waterproof sealing, and long-term reliability. Fiber-reinforced polymer composites are promising for upper covers, underbody shields, trays, cross beams, side frames, and local protective structures because of their low density, corrosion resistance, design flexibility, and functional-integration potential. However, composite-part performance is strongly governed by forming. Resin flow, impregnation, curing or cooling shrinkage, fiber orientation, filler dispersion, and interfacial bonding may induce voids, dry spots, resin-rich regions, delamination, warpage, and fiber waviness, thereby affecting load bearing, sealing, thermal protection, and durability. This review focuses on composite-forming technologies for new energy-vehicle battery packs. It summarizes component-level service requirements and material systems and compares representative forming routes, including sheet molding compound (SMC), prepreg compression molding/wet compression molding (PCM/WCM), resin transfer molding/high-pressure resin transfer molding (RTM/HP-RTM), vacuum-assisted resin transfer molding (VARTM), long-fiber thermoplastic direct processing (LFT-D), glass-mat thermoplastic (GMT), thermoplastic sheet forming, pultrusion, and multi-material joining. These routes are evaluated from six dimensions: material form, forming cycle, typical defects, representative mechanical performance, applicable components, and engineering maturity. The review further discusses defect mechanisms, performance effects, detection and control methods, and the roles of in-line monitoring, non-destructive testing, process simulation, machine learning, and digital twins in closed-loop quality manufacturing. Finally, engineering challenges are examined in multi-material joining, thermal-safety integration, low-carbon recycling, and standard certification. Composite-material battery-pack structures should therefore be developed as coordinated design and closed-loop manufacturing systems linking materials, processes, defects, performance, and validation. Full article

Petrochemical plants are complex facilities composed of interconnected equipment to produce essential products for daily human activities. In view of the adoption of the Industrial Internet of Things (IIoT), these facilities use various sensors, including those for level, flow, temperature, and pressure, to monitor and control operations. Several process faults can be detected and classified using data from these sensors. Detecting and classifying these faults helps mitigate production losses, improve product quality, decrease environmental concerns, and improve human safety. Conventional static learning, which learns from historical data, and continual learning, which learns incremental tasks with known preset boundaries, fall short on asynchronous online unknown streaming boundary tasks such as the OpenWorld Problem, which is the true representation of real-world dynamic plant scenarios. To tackle these challenges, we propose a novel online asynchronous framework termed OD-ViTFScL. Our framework utilizes a dual-attention backbone, which concatenates orthogonal information from attention on the vertical time-series data and horizontal inter-sensor relationships. The fused MLP from the temporal and sensor streams learns independent weights for each orthogonal axis. Our framework also uses ODIN, an out-of-distribution technique, to trigger the arrival of a new fault class in the streaming data. Extensive experiments on the Tennessee Eastman Process (TEP) and Case Western Reserve University (CWRU) bearing datasets validate that our proposed OD-ViTFScL outperforms other state-of-the-art fault detection and diagnosis (FDD) approaches. Full article

Background/Objective: Persistent infection with human papillomavirus type 16 (HPV-16) is the principal cause of cervical high-grade squamous intraepithelial lesions (cHSIL) and cervical cancer, yet the established treatments remain limited to ablative or excisional procedures that carry reproductive risk and do not eliminate the underlying infection. We report the preclinical development of ARV-2001, a messenger RNA (mRNA)–lipid nanoparticle (LNP) immunotherapeutic encoding mutated, non-oncogenic HPV-16 E6 and E7 fused to a SARS-CoV-2 spike S2 subdomain enriched in human CD4 helper epitopes, formulated in a novel cholesterol-derived ionizable lipid (ARV-T1). Methods: Interactions of ARV-2001-expressed antigens with p53 and retinoblastoma (Rb) were evaluated in human cervical carcinoma cell line C33A, in lentiviral constructs in primary human keratinocytes, and in soft-agar colony-formation assays. ARV-2001 was administrated by intramuscular (IM) or intradermal (ID) injection in naive mice or in the TC-1 tumor models. Tumor size and survival were monitored over time and tumor-infiltrated lymphocytes were characterized by flow cytometry. Intracellular cytokine staining and Elispot were used to evaluate immunogenicity. Results: In vitro, the mutated E6/E7–S2 antigen lost the ability to degrade p53, to deregulate the retinoblastoma (Rb) pathway, and to support anchorage-independent growth, suggesting abrogation of oncogenic activity. The S2 domain and imiquimod administration each augmented antitumor activity and intratumoral CD8⁺ T-cell infiltration while reducing myeloid-derived suppressor cells in the syngeneic HPV-16 E6/E7 TC-1 tumor models. In addition, ID administration of ARV-2001 into TC-1 tumor-bearing mice was superior to IM administration in terms of both tumor growth inhibition and survival. ID vaccination with ARV-2001 in mice consistently elicited a more potent E6/E7-specific T-cell response than the same dose given IM. Dose-escalation studies showed a dose-dependent T cell response against E6/E7 in ID-injected mice. Conclusions: This study supports future human evaluation of intradermally administrated ARV-2001 for treatment of HPV-16+ cHSIL in clinical trials. Full article

Reliable motor-current-based bearing diagnosis requires evaluation on unseen physical bearings and operating conditions. This study uses the Paderborn University benchmark, acquired from a 425 W permanent-magnet synchronous motor (PMSM) test rig, to evaluate time–frequency deep transfer learning under strict bearing-level grouping. Four representations—continuous wavelet transform (CWT), short-time Fourier transform (STFT), wavelet synchrosqueezed transform (WSST), and Fourier synchrosqueezed transform (FSST)—are combined with pretrained CNN backbones across four binary targets: aged-only A/B and artificial-plus-aged C/D, with mixed-fault bearings excluded/included within each pair. The workflow includes pooled-condition candidate discovery, exploratory Main-split leave-one-operating-condition-out (LOCO) screening, and a retrospective multi-split LOCO audit. The audit contains 288 crossed condition–split–seed evaluations. Because pooled test summaries and Main-split LOCO results informed later stages, these evaluations provide descriptive robustness evidence rather than an independent post-selection test. Target A achieved the highest all-split mean balanced accuracy (0.736 for CWT–EfficientNetB0). The pairs for Targets B and C were near-ties, and the Target D ordering reversed when Main was excluded. Across the eight audited candidates, mean sensitivity ranged from 0.618 to 0.948, whereas specificity ranged from 0.092 to 0.564. Target D combined approximately 0.89 sensitivity with an approximately 0.90 false-alarm rate. Thus, operating condition, fault-class composition, bearing split, and error-cost priorities all affect model interpretation. A matched current-domain baseline audit added 336 evaluations using handcrafted-feature RBF–SVM and Random Forest models and a compact raw-current 1D-CNN. The results show that instability is broader than the TF–CNN pipeline but is not uniform across model families: TF candidates were clearly stronger for Targets A and C, feature-based models were stronger for Target B, and Target D remained mixed and protocol-sensitive. A complementary bearing-level source-group analysis quantified six healthy/fault-source categories across the 37 current features; among the eight features with the largest mean between-group variance fraction, only spectral entropy and dominant power fraction preserved the same mixed-versus-non-mixed contrast direction across all four operating conditions. An additional matched Target D reference held the binary target, physical-bearing split, held-out condition, seed, fourth-order FSST representation, ResNet-50 backbone, and training settings fixed while changing the sensing channel. Across 24 matched runs, vibration showed descriptively higher mean balanced accuracy (0.642 vs. 0.503) and specificity (0.476 vs. 0.146), while sensitivity was slightly lower (0.807 vs. 0.859); the comparison does not establish universal modality superiority. Pooled-condition performance is useful for candidate discovery, but credible condition-generalization claims require explicit separation of exploratory selection and confirmatory testing. The numerical findings are specific to the evaluated PMSM benchmark and do not establish universal performance across electric-machine types. Full article

A variety of measurement systems are available for monitoring existing concrete bridges with deficiencies in shear capacity. In addition to established systems, fiber optic sensors (FOS) offer significant potential for structural health monitoring. However, FOSs are often installed in the tension zone, where crack formation may occur even under service loads, increasing the risk of sensor failure and potentially resulting in a loss of measurement capability. A promising approach to significantly reduce this risk is the installation of FOSs in the compression zone. However, it remains unclear whether measurements obtained from FOSs in the compression zone can be used to assess the load-bearing and deformation behavior of reinforced concrete beams and how they relate to measurements obtained in the tension zone. To address this question, shear tests were carried out on reinforced concrete beams with shear reinforcement ratios commonly used in practice. The experimental results demonstrate close agreement between measurements obtained from FOSs installed in the compression and tension zones under service loads. Furthermore, the findings and the characteristic strain profiles associated with shear and flexural failure provide a basis for assessing existing structures monitored using FOSs. Full article

This paper presents an adaptive noise-aware bearing fault diagnosis framework that integrates Fast Fourier Transform (FFT)-windowed feature extraction, wavelet-based Signal-to-Noise Ratio (SNR) estimation, and Long Short-Term Memory (LSTM) classification to maintain high diagnostic accuracy under both clean and severely noisy operating conditions. The core novelty of the proposed framework lies in its adaptive model selection mechanism, which automatically selects the most appropriate LSTM classifier according to the estimated SNR, thereby improving diagnostic robustness across different noise environments. Experiments were conducted on two benchmark datasets, the Case Western Reserve University (CWRU) dataset and the Huazhong University of Science and Technology (HUST) bearing dataset, to evaluate the generalization capability of the proposed approach. Two preprocessing pipelines were examined: time-domain normalization before FFT and frequency-domain normalization after FFT. Vibration signals were segmented without overlap to ensure unbiased evaluation. The results demonstrate that both the choice of window function and the normalization strategy significantly influence classification accuracy and robustness. Under noise-free conditions, several window types achieved accuracies above 99%, with triangular and Hamming windows providing the best performance. The combination of triangular windowing and time-domain normalization achieved the highest accuracy of 99.69%. Furthermore, time-domain normalization combined with triangular windowing exhibited superior stability and noise resistance compared with frequency-domain normalization. Under noisy conditions, noise-augmented training was found to be essential for achieving robust generalization. Models trained with moderate noise levels (8–12 dB) provided the best trade-off between accuracy and robustness, whereas excessive noise during training degraded performance. To accommodate varying noise environments, a lightweight wavelet-based SNR estimator was used to categorize operating conditions into low-, medium-, and high-SNR regions and select the corresponding LSTM classifier. The proposed framework was successfully implemented on a ZedBoard FPGA (Field-Programmable Gate Array) development board using a System-on-Chip (SoC) architecture. Experimental results show that, with a sampling frequency of 48 kHz and a processing window of 2048 samples, the proposed system updates the diagnostic result every 42.7 ms, demonstrating its suitability for real-time industrial condition monitoring and intelligent predictive maintenance applications. Full article

Rolling-element bearings are a leading cause of unplanned downtime in continuous-process manufacturing, and the migration toward Industry 4.0 condition-based maintenance (CBM) has intensified the need for diagnostic methods evaluated on real in-service assets. This paper reports an exploratory, longitudinal single-asset industrial field demonstration of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based Hilbert spectral analysis for rolling-element bearing condition monitoring. An in-service bearing on a critical production machine was monitored over eight measurements spanning approximately four months and analyzed with the Hilbert–Huang Transform, using CEEMDAN in place of the classical Empirical Mode Decomposition to suppress mode mixing. The Hilbert spectra tracked the evolution of the bearing’s vibration signature as a growing concentration of vibration amplitude in a stable band of the 0–400 Hz analysis window (approximately 280–380 Hz); because the analysis characterizes the distribution of amplitude within the band rather than resolving discrete defect lines, this is reported as band-amplitude trending, and the band is treated as compatible with bearing-related excitation rather than attributed to a specific kinematic fault frequency. At the final pre-replacement measurement (M8), a bounded consistency cross-check against a conventional single-sided Fast Fourier Transform (FFT) amplitude spectrum computed from the same exported waveform recovered co-located dominant content. This establishes cross-method consistency for the measurement examined, but does not independently validate diagnostic correctness or the full campaign trend. Interpretability of the representation is argued qualitatively and remains to be tested. The contribution is therefore the documented field application and its explicit consistency cross-check procedure; applicability beyond this asset and its operating conditions requires multi-asset evaluation. Full article

To address the need for low-power, real-time underwater acoustic signal processing and autonomous target detection on deep-sea unmanned mobile platforms, such as profiling acoustic buoys and underwater gliders, this study developed an embedded Linux-based signal processing system for buoy platforms. Conventional digital signal processing hardware platforms are often constrained by large size, high power consumption, and limited data communication capability. The proposed system adopts a compact, low-power architecture and a multithreaded processing framework based on the AM6254 heterogeneous multicore processor. It acquires four-channel vector-hydrophone signals together with attitude data from an inertial navigation module and performs band-pass filtering, fast Fourier transform (FFT), direction-of-arrival (DOA) estimation, and constant false alarm rate (CFAR) detection for autonomous target detection. The measured typical power consumption was approximately 2.3 W. Anechoic-tank and sea-trial results showed the lowest tested spectral level at which autonomous detection was achieved was 54 dB at 1 kHz, corresponding to an average in-band level of 46 dB. Under sea state 3, the system maintained continuous bearing tracking after target acquisition for a surface target traveling at 7 kn, up to a range of approximately 7 km, and provided unambiguous bearing estimation. These results demonstrate the target-detection capability and practical applicability of the system under representative operating conditions and indicate its potential for marine environmental monitoring and unmanned-platform observation and detection. Full article

Concrete structures are prone to cracking during service, and epoxy grouting is a widely adopted technique for structural intervention. However, the inherent brittleness and poor durability of neat epoxy under complex loading and environmental exposure remain critical challenges. This study systematically evaluates the mechanical performance of epoxy-grouted concrete interlayer systems modified by carbon fiber (CF), glass fiber (GF), and polyethylene microspheres (PE). A comprehensive experimental program was conducted, including compression, three-point bending, and Brazilian splitting tests at three loading angles, combined with digital image correlation for surface strain monitoring. The effects of grout thickness and wet–dry cycles were systematically investigated. Results demonstrate that reinforcement modification helps to improve the performance of grouted concrete, with optimal behavior highly dependent on loading mode. CF-reinforced specimens with strong interfacial bonding exhibit the highest compressive strength, which is 150% higher than the bearing capacity of intact concrete, but are prone to brittle fracture under loading involving tension-shear interaction. GF reinforced specimens with moderate interfacial bonding exhibit better load-bearing capacity under tensile-shear stress interaction, reaching a normalized splitting peak load of 0.95 at a grouting thickness of 5 mm. PE-reinforced specimens with weak interfacial bonding provide relatively extensive energy dissipation. A 3 mm grouting layer shows favorable performance among the tested thicknesses, balancing load transfer enhancement and defect control. A single wet–dry cycle temporarily improves performance, possibly due to epoxy post-curing and pore filling, whereas repeated cycling generates cumulative micro-damage. GF- and CF-reinforced systems demonstrate the most stable resistance to short-term wet–dry conditioning. These findings provide guidance for loading-mode-dependent reinforcement selection in epoxy grouting applications. Full article

This paper presents a comprehensive wind tunnel investigation on the wind-induced vibration characteristics of a novel four-cable-supported photovoltaic (PV) structure. The proposed structure system integrates two adjacent dual-cable rows through rigid connecting rods to form a collaborative load-bearing framework, aiming to enhance overall stiffness and mitigate wind-induced vibrations. A 1:15-scale aeroelastic model was tested in a boundary-layer wind tunnel for both single-row and five-row configurations. Wind-induced displacements were measured using a non-contact high-definition camera system capable of real-time, multi-point monitoring across multiple rows, while cable tension forces were simultaneously recorded with load cells—a combined measurement approach rarely reported in existing studies. The effects of wind speed and wind direction angle on the vibration responses were systematically examined. Results reveal that vertical vibrations dominate, with mid-span displacements reaching maximum values. The shielding effect among multiple rows is pronounced: the windward first row consistently exhibits the largest displacements and cable forces under both wind pressure and suction. Wind directions of 0° and 180° are identified as the most unfavorable for pressure and suction, respectively. Cable forces under pressure exceed those under suction, and within each row, windward cables sustain greater forces than leeward cables. These findings provide essential experimental reference data for the wind-resistant design of multi-row cable-supported PV support structures. Full article

The accuracy of axle-box bearing temperature prediction is important for monitoring the operating condition of high-speed electric multiple units. However, temperature data collected in service often contain missing values, abnormal fluctuations, and noise, which can affect the reliability of the prediction results. To address this problem, this paper proposes an Attention-BP-LSTM framework that combines data preprocessing with temperature prediction. First, missing data are completed by linear interpolation. The dynamically augmented temperature sequence is then processed using time-delay low-rank sparse decomposition to separate the normal temperature variation trend, sparse anomalies, and measurement noise, based on which the abnormal data are reconstructed. After Z-score normalization, the processed time series is converted into supervised learning samples using a sliding window. The prediction model consists of LSTM, an attention mechanism, Dropout, and a BP network. LSTM is used to extract the temporal dependencies in the temperature sequence, the attention mechanism assigns corresponding weights to different historical features, Dropout alleviates model overfitting, and the BP network completes the nonlinear mapping from temporal features to the predicted temperature. Experimental results based on axle-box bearing temperature data show that, compared with BP, LSTM, and BP-LSTM, the proposed model achieves lower MAE and RMSE and a higher R². This indicates that improving data quality before prediction, together with attention-based temporal feature extraction, helps improve the reliability of short-term axle-box bearing temperature prediction. Full article

Rolling bearings, as a key component of rotating machinery, require precise fault diagnosis to ensure the safe and reliable operation of industrial systems. Nevertheless, the performance of traditional narrowband filtering demodulation (NFD) methods is constrained by inherent limitations of spectral segmentation frameworks and insufficient discriminative capability of feature indicators (FIs). To address these limitations, this paper proposes a new NFD method based on an adaptive multi-level spectra segmentation strategy. Firstly, using power spectral density (PSD) as the analysis basis, an iterative framework is constructed to obtain multi-level spectral trend lines (STLs), which achieves multi-perspective characterization of spectral features. Secondly, the local minimum points of the STLs are used as the segmentation boundaries to extract the demodulation frequency band. Subsequently, a robust blind feature indicator, synergistic characterization criterion (SCC), is proposed, which can simultaneously fully evaluate periodicity and impulsiveness, guiding the selection of the optimal demodulation frequency band (ODFB). Finally, based on the enhanced demodulation spectrum, power exponent transformation is introduced to construct a generalized spectral family, and the adaptive determination of the optimal transformation parameter is guided by frequency-domain signal-to-noise ratio (FDSNR), thereby obtaining the generalized enhanced demodulation spectrum (GEDS). Validation experiments on laboratory and public datasets demonstrate that the proposed method outperforms Fast Kurtogram, Autogram, and CFFsgram, with average improvements of 63.86% and 89.06% in mean-peak ratio (MPR) and fault feature coefficient (FFC), respectively, and provides a new perspective for NFD and expands its application potential in bearing fault diagnosis and condition monitoring. Full article

Whereas existing approaches to financial regime detection calibrate thresholds to historical price-and-return statistics, we propose a framework built on immunisation: a particle filter whose observation model is calibrated to the distributional shape of market stress rather than specific historical episodes. The particle filter—with theoretical foundations in probabilistic robotics and autonomous driving, and adapted to financial markets—forms the inferential backbone. Its observation model stacks distributional shape descriptors—skewness, tail asymmetry, kurtosis, and the share of leading sector-eigenvalue energy in cross-asset return covariance—computed across a hierarchy of temporal windows and injected as structured distributional archetypes, replacing the random initialisation of Thrun and Burgard, and of Reisinger. Applied to S&P 500 across four distinct crises (Dotcom 2002, Lehman 2009, COVID-19 2020, and the 2022 inflation-driven bear market), the descriptors show pre-crisis discrimination, with effect sizes (Cohen’s d) of 2.9 or more for realised volatility, Bowley downside skewness, and tail-quantile features, and 1.3 for sector concentration (${\lambda }_1$ ratio). Out of sample (2015–2026), the pipeline confirms endogenous regime transitions with lead-time before the market trough. With the flexibility of particle filters, the richness of the observation model is the primary enabler of real-time regime detection. We frame the system as a distributional-shape monitor that detects regime transitions in real time, not a pre-peak forecaster: highly sensitive, it registers deformation as stress becomes measurable, with the attendant sensitivity–specificity trade-off. On the abrupt COVID-19 shock, it confirms the transition sixteen trading days before the trough, without claiming pre-peak detection; confirmation timing scales with each crisis’s own duration, from roughly two to three weeks for the fastest episodes to several months for the slowest. Full article