Search Results (127)

Vibration–based identification methods provide a feasible way to evaluate bolt loosening, but measured responses of bolted joints are often affected by environmental and measurement noise, making robust nonlinear feature extraction challenging. Although Volterra–series–based methods have been used to characterize nonlinear structural behavior, existing studies generally employ different–order kernels or diagonal kernel vectors directly as preload–sensitive indicators. Such treatments provide limited explanation of the physical mechanisms underlying Volterra–based nonlinear separation, especially from the perspective of frequency–domain coupling. To address this limitation, this study proposes a frequency–domain Volterra analysis framework for nonlinear feature extraction and bolt preload identification. The bolted joint is first modeled as a nonlinear dynamic system, and a Kautz–expanded Volterra model is established to identify the input–output relationship under dynamic excitation. The identified Volterra responses are then transformed into the frequency domain to reveal the harmonic composition of different nonlinear orders. Based on the generalized frequency response function (GFRF), a nonlinear frequency component associated with high–order harmonic generation is extracted to construct an interpretable Volterra nonlinear frequency–domain feature (VNFF). Furthermore, an unsupervised preload–sensitive indicator is developed based on the VNFF using the initial–state feature center, and supervised preload identification is performed using machine learning models. Experimental results show that the identified Volterra model can accurately reconstruct the measured responses. The proposed preload–sensitive indicator exhibits a consistent increasing trend with preload degradation across different datasets, demonstrating its monotonicity and sensitivity to bolt loosening. In addition, the proposed VNFF achieves the best preload identification performance, with optimal accuracies of 97.73%, 95.45%, and 93.18% on three datasets, respectively. These results demonstrate that the VNFF effectively highlights nonlinear contact information and supports reliable tightening–state assessment. Full article

This study provides a review of recent advances in percussion-based bolt looseness monitoring. Beginning with the engineering mechanism of the tap-induced vibro-acoustic response in bolted joints, this study traces the technical evolution of the field across three progressive levels: basic signal preprocessing, discriminative feature extraction, and intelligent diagnosis modeling. Subsequently, a multi-dimensional comparative analysis of typical monitoring methods is conducted, and targeted combined detection schemes tailored to different engineering scenarios are synthesized. Furthermore, the main constraints impeding engineering application are elucidated, and prospective research directions oriented to complex engineering conditions are outlined. This review is intended to provide a systematic technical roadmap and theoretical framework for the further development and engineering deployment of intelligent percussion-based bolt looseness monitoring technology. Full article

Bolted flange joints are critical load-bearing connection components of complex mechanical equipment, whose service performance is dominated by the coupled evolution of bolt preload relaxation and interfacial stiffness degradation under long-term cyclic combined loads. Under cyclic transverse excitation, the contact interfaces experience successive full-stick, partial microslip and macroslip states, accompanied by embedding, creep and friction-induced wear, resulting in time-varying preload attenuation and the continuous degradation of joint mechanical properties. Distinguishing preload relaxation without nut rotation from rotational self-loosening is essential for revealing multi-bolt flange degradation mechanisms. This paper presents a systematic review of physics-driven constitutive modeling and degradation characterization of bolted flange connections. A unified classification framework for joint interface models is established, covering static friction models; velocity-dependent dynamic friction models; hysteresis stick–slip models, represented by the four-parameter Iwan model and Valanis endochronic model; and reduced-order equivalent joint models. The inherent differences between the Dahl model and LuGre model regarding the Stribeck velocity–friction characteristics are clarified. The representative models are comprehensively evaluated from the perspectives of physical interpretability, hysteresis reproduction accuracy, preload–degradation correlation and engineering applicability. A further comparative analysis is conducted of parameter identification bottlenecks of the Iwan, LuGre and Valanis models, focusing on multi-solution risk, anti-noise robustness and cross-working-condition transferability. Compared with single-bolt lap specimens, multi-bolt annular flanges exhibit prominent inter-bolt elastic interaction, circumferential contact pressure non-uniformity and local-to-global chain-reaction degradation behaviors. The common experimental test-beds and sensing techniques for joint degradation monitoring are summarized, and the measurement limitations, including sensor drift, synchronization error and installation constraints, and their influences on model parameter identification, are discussed. The existing machine-learning, digital-twin and physics-informed modeling applications for bolted joints are briefly outlined. Finally, this review summarizes the existing research consensus, unresolved contradictions and open research challenges. Special attention is paid to the limitations of existing time-variant Iwan-type models for multi-bolt flange degradation. Potential future research directions include multi-channel synchronous sensing matching time-varying constitutive models, quantification of inter-bolt load redistribution, and robust identification strategies under noisy experimental data. Full article

Dynamic inspection of hydropower-generator rotors is hindered by long imaging distances, metallic reflections, and motion-induced degradation of anti-loosening marking lines. We present a task-decoupled vision framework that first localizes bolts and then segments their marking lines within scale-normalized regions of interest. The YOLO-SE detector combines spatial-to-depth convolution with efficient multi-scale attention to preserve small-object features and suppress illumination interference. The segmentation stage integrates a MobileViT2 backbone, attention-enhanced atrous spatial pyramid pooling, and Dice cross-entropy loss to recover global context and sparse foreground boundaries. In laboratory experiments, YOLO-SE achieved 99.4% mean average precision at 0.5 intersection-over-union (mAP50) and 99.8% recall, exceeding YOLO26n by 3.2 and 3.4 percentage points, respectively. The complete segmenter achieved 98.67% mean intersection-over-union (mIoU) and 95.38% target-line IoU; across five seed-matched runs, YOLO-SE obtained 99.32 ± 0.10% mAP50 and the segmenter obtained 98.61 ± 0.09% mIoU. Site-specific validation on M12 rotor-clamp bolts supported field applicability under changes in scale, viewpoint, and illumination. The complete cascade required 53.1 million parameters, 207.7 GFLOPs, 2.38 GB peak GPU memory, and 31.8 ms per frame, meeting a 30 FPS GPU budget under the evaluated conditions. Full article

Bolted connections in combine-harvester vibrating screens are affected by reciprocating screen motion, frame vibration, and intermittent impact, making weak local vibration components difficult to distinguish from background fluctuations. This study used particle swarm optimization to adaptively select the wavelet basis, decomposition level, and thresholding rule for vibration signal denoising. Triaxial acceleration signals were collected under tightened- and loosened-bolt conditions. Gaussian white noise with input signal-to-noise ratios from 0 to 14 dB was added to evaluate parameter selection under controlled noise levels. The selected wavelet basis and decomposition level varied with the input signal-to-noise ratio. Three or four decomposition levels were selected under stronger noise, whereas two levels were generally sufficient at higher signal-to-noise ratios. Soft thresholding was selected in all tested cases. The optimized method reduced random fluctuations while retaining the main waveform trend in simulated noisy signals. For measured loosened-bolt signals, it reduced background fluctuation and retained local waveform changes. Full article

Tourism scholarship has long treated technological innovation as an accelerant bolted onto a stable practice: people move through physical space to consume experiences, then return home. This conceptual paper asks what happens to the field’s theory once that assumption is relaxed. Rather than forecasting a single future, the paper develops a structured conceptual foresight framework to examine how emerging technological trajectories may challenge the constitutive assumptions of tourism theory. Drawing on tourism scholarship alongside developments in computation, the biosciences, and cognitive science, the framework integrates established concepts from futures studies including Voros’s futures cone, Inayatullah’s causal layered analysis, and Dator’s four generic futures to explore alternative trajectories for tourism. This analysis identifies four constitutive assumptions on which tourism theory quietly rests, namely geographic displacement, embodied co-presence, the destination as passive stage, and the tourist–host dyad, together with a typology of de-anchoring that traces what becomes of the field as each assumption loosens. Four conceptual scenarios follow: the dematerialization of place, biologically mediated travel, autonomous destination ecosystems, and the erosion of the tourist–host distinction. Some sit in the cone’s plausible band; others are placed at its preposterous edge on purpose to stress-test the field’s categories rather than to predict events. Throughout, the analysis stays tethered to tourism and hospitality concerns: destination economics, the work of destination management organizations, labor displacement, experiential inequality, the measurement infrastructure behind tourism statistics, and the shift from sustainability toward regeneration. We close with a research agenda for an anticipatory, theory-building stream within the field. Full article

Hydrogen energy systems—proton exchange membrane fuel cells (PEMFCs), water electrolyzers, and high-pressure hydrogen storage vessels—are increasingly deployed in transportation, maritime, aerospace, and stationary applications where mechanical vibration is unavoidable. Yet vibration research remains fragmented into single-technology studies whose findings often appear inconclusive or contradictory. This review provides a cross-technology assessment of vibration phenomena in hydrogen energy systems, covering PEMFC performance and degradation, structural dynamics of stacks and storage vessels, water management and two-phase flow, diagnostics and modeling, and application-specific challenges for road, marine, aircraft, and space systems. By organizing the evidence around a small set of shared mechanisms—loss of mechanical preload (bolt loosening), two-phase flow disruption, and fatigue-driven crack growth—we establish a unified framework that reconciles the seemingly case-dependent results of earlier, single-technology reviews. Whether vibration acts as friend or foe is governed by a consistent parameter set: amplitude, frequency, direction, and cumulative exposure time. Short, low-frequency excitation can aid water removal in fuel cells, improve cold-start behavior, and raise electrolyzer hydrogen yield by up to 128%, whereas sustained exposure roughly doubles PEMFC voltage degradation rates, loosens clamping bolts, and drives fatigue in storage-vessel supports. The evidence base is currently dominated by PEMFC studies, and this review accordingly treats fuel cells in the greatest depth. Priority research needs are identified: standardized vibration test protocols, long-duration durability data, vibration characterization of electrolyzers prior to offshore deployment, and coupled multiphysics models supporting vibration-aware design. Full article

Hydropower-generator rotors contain numerous closely spaced bolted joints, making full-coverage contact instrumentation impractical, while single-view vision methods are vulnerable to missing or ambiguous evidence during rotation. This study proposes a detector-guided multi-view visual monitoring framework that separates region-of-interest (ROI) localization from explicit geometric interpretation. On a simulated hydropower-generator rotor platform operating at 15 rpm, YOLO-family detectors localize candidate ${bolt}_{top}$, ${bolt}_{side}$, and ${star}_{marker}$ regions. Quality-retained top-view ROIs yield the image-space angular indicator ${\theta }_{img}$ from the relative orientation of nut-side and disk-side anti-loosening lines; side-view ROIs yield the pixel-space thread-exposure indicator $L_{px}$ from exposed-thread endpoints and, when marker geometry is sufficiently visible, an auxiliary angular cue. A star-shaped reference marker organizes accepted frame-level observations into approximate rotation intervals, while hierarchical checks of ROI completeness, endpoint availability, image quality, geometric plausibility, and temporal membership retain both usable evidence and explicit rejection reasons. YOLO11n achieved precision 0.9987, recall 1.0000, mAP50 0.9950, and mAP50–95 0.7798 for laboratory ROI localization. After geometric screening, evidence availability was 16.7% for the top-view branch and 76.3% for the side-view branch. In a supplementary 169-image operational field subset, the principal ROI model achieved precision 0.9782, recall 0.9942, mAP50 0.9946, and mAP50–95 0.7948. The field results support appearance-level localization under complex rotor-bolt conditions, and the framework provides a traceable, reliability-aware basis for organizing, screening, and interpreting multi-view evidence in hydropower-generator rotors and similar rotating structures. Full article

Extreme wind events, particularly tropical cyclones, pose the most severe safety threat to aging lattice steel space-truss structures in coastal regions, including transmission towers, communication and observation towers, and lattice supports of building-integrated wind-energy facilities. Such structures suffer progressive capacity degradation through multiple concurrent mechanisms, yet their actual residual safety margin under extreme wind loading remains poorly quantified. Current assessment practices rely on code-prescribed simplified wind speeds that ignore terrain-induced local amplification, and assume an intact structural condition that neglects in-service deterioration. This paper proposes a Risk-Based Safety Assessment Framework (RBSAF) that addresses both deficiencies through a five-step pipeline: (i) multi-scale wind field downscaling that resolves terrain-amplified wind profiles at individual structure sites; (ii) independent degradation models for atmospheric corrosion, bolt loosening, fatigue accumulation, and pitting corrosion; (iii) a multi-degradation coupling aggregation method that yields a unified Structural Health Index (SHI) capturing nonlinear interaction effects; (iv) code-based multi-scenario safety margin scanning with automatic identification of weak components; and (v) a risk-informed reinforcement priority mapping strategy. A representative 220 kV angle-steel lattice tower in a coastal mountainous corridor of Southeastern China is employed as the case study. Results show that after 30 years of service in an ISO 9223 C4 corrosive environment, the structure-level SHI decreases from 1.47 (intact, code wind) to 1.00 under the proposed coupled assessment with code-prescribed wind, and further to 0.76 when terrain amplification (15% speed-up) is considered, with the failure probability rising from 3.6% to 24.1%. Multi-degradation coupling causes an additional 28% capacity loss relative to single-factor assessment and substantially alters the weak-component ranking. Reinforcing the five most critical members restores the SHI to 1.25 with only a 2.8% steel-weight increase. The framework provides a systematic, quantitative tool for safety evaluation and maintenance prioritization of aging lattice steel structures in wind-prone built environments. Full article

Bolted joints are critical load-transfer components in bridges, wind turbines, aerospace systems, mechanical equipment, and offshore platforms, where preload loss can degrade stiffness, accelerate fatigue, and compromise safety. For structural health monitoring, early monitoring of preload reduction before marked loosening is essential, yet existing ultrasonic guided wave indicators remain affected by frequency dependence, non-monotonic responses, amplitude drift, and environmental disturbances. This study proposes an early-warning-oriented preload identification method that combines broadband excitation, multi-frequency narrowband reconstruction, spectral centroid extraction, optimized weighted fusion, and fixed SC-domain linear calibration from one reference loading group. Using a 20–250 kHz Chirp response, 14 narrowband signals from 50 to 180 kHz were reconstructed for an M20 single-bolt specimen tested over 50–90 N·m. The fused spectral centroid index exhibited a stable, monotonic, and approximately linear relationship with preload. When fixed weights and calibration coefficients were transferred to held-out repeated-loading groups, all Pearson correlation coefficients exceeded 0.99. Feature-level robustness tests showed that the arithmetic mean of the spectral centroid reduced temperature-induced Range% by 98.42–99.08% and RSD by 98.89–99.31% relative to energy-based features. This work provides an interpretable multi-frequency spectral descriptor and a calibration transfer framework for repeatable early warning of preload loss in a controlled single-bolt configuration. Full article

Flanged connections are a critical joining method in modern industrial production, making the detection of bolt loosening in flanges a vital step to ensure industrial safety. Current research on bolt loosening detection in flanges mainly focuses on flat-face flanges without gaskets, while studies on bolted pipe flanges containing gaskets are relatively limited. To achieve bolt loosening detection in such gasketed pipe flanges, this paper analyzes the influence of bolt loosening on wave propagation in the gasket based on the stress wave principle and finite element simulation, and employs the hammer impact method to realize the detection of bolt loosening degree in pipeline flanges. The optimal knock force and hammer head material for the bolt loosening detection experiments were determined experimentally. Through comparative experiments, the Support Vector Machine—Recursive Feature Elimination (SVM-RFE) model was identified as being more accurate and efficient in assessing the degree of bolt loosening. Furthermore, the model was optimized by incorporating feature enhancement and cost-sensitive learning, thereby providing a reliable methodological solution for the rapid identification of bolt loosening severity in pipeline flanges. Full article

Bolt loosening is a critical precursor to structural failure in major industrial and transportation equipment. Although acoustic non-destructive testing (NDT) offers a cost-effective diagnostic solution, its practical deployment is often hindered by low signal-to-noise ratios (SNRs) and the limited ability of conventional models to isolate fine-grained transient acoustic signatures from complex background interference. To address these challenges, this paper proposes PCT-Net, a multi-scenario noise-adaptive fusion network for bolt-state recognition. First, an Adaptive Spectral Masking mechanism is introduced as a data augmentation strategy. Instead of rigid zero-padding, it dynamically blends local spectral energies to encourage the learning of more robust and noise-invariant representations. Furthermore, rather than simply concatenating multiple modules, PCT-Net adopts a synergistic feature extraction framework to decouple complex acoustic signatures. A perceptual frontend is used to establish acoustically meaningful representation priors. To handle the highly dispersed characteristics of loosening signals, cascaded convolutional modules progressively suppress redundant environmental interference while capturing high-frequency local transient impacts. Meanwhile, to overcome the limited receptive field of convolutional operations, an embedded Transformer mechanism is introduced to model long-range temporal dependencies and low-frequency structural variations throughout the tapping cycle. By integrating local fine-grained transient modeling with global structural dependency modeling, the proposed network can better distinguish subtle decision boundaries among different loosening states. Extensive experiments show that PCT-Net achieves a classification accuracy of 97.12% under standard conditions and maintains stable performance under severe noise scenarios. These results demonstrate the effectiveness of the proposed method and highlight its potential for intelligent industrial safety monitoring. Full article

By applying nonlinear vibration-to-sound modulation technology to bolt loosening detection, this paper proposes a new experimental setup and signal-processing method. A linear swept-frequency signal is used to excite low-frequency vibrations, while a fixed-frequency sine wave is used for high-frequency ultrasonic excitation. First, a fractional Fourier transform is applied to the vibration-acoustic modulation signal to transform it into the optimal fractional domain where the energy of the swept-frequency signal is concentrated; next, the swept-frequency signal undergoes a masking filter, and the filtered signal is transformed back to the time domain; finally, the time-domain signal is transformed back to the frequency domain, and the amplitudes of the sum and difference frequency components of the high-frequency signal are extracted as damage parameters. The effectiveness of this method in bolt loosening detection was verified through bolt connection tests, with the applied tightening torque ranging from 10 to 30 N·m. This method is simple to operate and highly robust, making it a reliable approach for detecting the degree of loosening in bolt connections. Full article

Bolt-nut fasteners are critical components of substation equipment, and their integrity directly affects the operational reliability of power systems. In practical inspection scenarios, however, the small physical scale of bolt-nut fasteners, together with complex background structures, often obscures their discriminative visual features, making accurate automated detection particularly challenging. Reliable detection is a prerequisite for downstream tasks such as loosening identification and defect diagnosis. To address these challenges, this paper proposes YOLOv8n-ALC, an enhanced detection network built upon the lightweight YOLOv8n framework. The backbone is redesigned by integrating the AdditiveBlock from CAS-ViT and a Convolutional Gated Linear Unit (CGLU) to strengthen fine-grained feature extraction and suppress background interference without increasing computational burden. In addition, an improved Large Separable Kernel Attention (LSKA) module is introduced to expand the effective receptive field while maintaining efficiency, enabling more robust multi-scale feature representation. To further alleviate feature degradation of small bolt-nut fasteners in deep layers, a Context-Guided Reconstruction Feature Pyramid Network (CGRFPN) is employed in the neck to optimize cross-layer feature fusion and enhance localization accuracy. Experimental results demonstrate that YOLOv8n-ALC achieves an mAP@0.5 of 92.1%, with precision and recall of 93.5% and 87.1%, respectively, outperforming the baseline by clear margins. These results confirm the effectiveness and robustness of the proposed method for intelligent substation inspection and bolt-nut fastener condition monitoring. Full article

Brake disc fastening bolts endure complex thermomechanical loads and are susceptible to loosening during emergency braking, making comprehensive analysis of their thermomechanical response and failure mechanism critical. This study developed and experimentally validated a thermal-mechanical coupling model for a C/C-SiC axle-mounted brake disc system, investigating the bolt’s thermodynamic responses under emergency braking at different initial speeds and pressures. During the emergency braking process, a non-uniform temperature field and deformation field are generated in the bolt, causing it to bend slightly towards the brake flange. Significant stress concentration consistently occurs at the root of the first engaged thread, identifying it as the critical region for loosening initiation. Higher initial speeds elevate bolt maximum temperature and equivalent stress. Under 400 km/h emergency braking condition, the bolt reaches a maximum temperature of 159.91 °C, a maximum equivalent stress of 849.00 MPa, and a stress amplitude of 98.66 MPa. Braking pressure also exerts significant effects. The optimal strategy for a 400 km/h emergency braking is determined as follows: a braking pressure of 26 kN is applied when the speed exceeds 300 km/h, and 28 kN when the speed drops below 300 km/h. With this strategy, the bolt’s maximum temperature, maximum equivalent stress, and stress amplitude are reduced by 17.02%, 1.41%, and 4.33%, respectively. Full article