Search Results (497)

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

While state-of-the-art massive acoustic arrays typically rely on costly, specialized FPGA architectures or rigid proprietary hardware, there is a growing need for modular, high-density sensing in complex aeroacoustics environments. This paper presents the electronic and acoustic design of a multiplexed, modular, scalable, and low-cost massive acoustic array (MxArray) founded on an embedded Linux system. The AM3358 SoC microprocessor collects audio data through its multichannel audio peripheral, where it simultaneously receives four Time-Division Multiplexing streams of 16 microphones each. This multiplexed scheme enables the handling of 64 microphones per module, whose acquisition synchronization is set with the Precision Time Protocol and a pulse injection hardware. The combination of both BeagleBone Black and microphones based on Micro-Electro-Mechanical Systems yields a cost-effective solution with built-in Ethernet connectivity and accessible software development through an embedded Linux environment with audio libraries for hardware control. Sensors are arranged in an Underbrink Spiral pattern on a four-layer printed-circuit board. The perforated thin layout minimizes any airborne disturbance, exploiting a distribution that simultaneously achieves a low sidelobe level and a narrow main lobe when used with a beamforming algorithm. Measurement results for the developed module are presented, as well as an evaluation of a full-scale system comprising 16 modules (1024 microphones) arranged in a honeycomb pattern. The resulting instrument offers a practical and scalable solution for applications that require a large number of simultaneous microphone measurements, such as beamforming technology for aeroacoustics applications. Full article

Introduction: The Atlantic bluefin tuna Thunnus thynnus (ABFT) is a large pelagic apex predator with adaptations for a life of ceaseless swimming during long-distance oceanic migrations. The environmental physiology and energetics of tunas have interested researchers for many decades, but they are notoriously challenging to study because they are so difficult to keep in captivity. Adult ABFT are, however, now fattened in cages at various sites in the Mediterranean, while juveniles are reared from hatching every year at the Unique Scientific and Technological Infrastructure for ABFT aquaculture (ICAR-IEO), near Cartagena in Spain. These facilities provide access to animals, but the fish remain very problematic to study because of their highly active but physiologically delicate nature and, for adults, their very large sizes. Objective: To study the in vivo physiology and behaviour of ABFT. Methodology: We used heart rate biologging and high residency acoustic tracking to follow cardiac and swimming activity over a year in n = 24 adult ABFT (mass range 25 to 200 kg) held in a cage off the coast of Malta (Malta Fish Farming). We then performed swim tunnel respirometry on young of the year juveniles (500g) at ICRA-IEO, but subsequently took a ‘hands-off’ approach, using video analyses and group respirometry on free-swimming animals. Results: The descriptive approach on the caged adults provided understanding of how seasonal water temperatures (15 to 28 °C) affect tuna physiology and behaviour. The swimming respirometry on juveniles revealed that their performance was constrained by confinement in the tunnel, compared to when they were swimming at their spontaneous preferred speed in their rearing tank. Video analyses provided insights into the effects of size (25 to 200 cm bodylength) on spontaneous swimming speeds and coupled with tank respirometry, revealed how progressive hypoxia affects the metabolic rate and schooling behaviour of juveniles. Conclusions: These opportunistic and disparate pieces of information are nonetheless valuable for such a fascinating but data-deficient species, and can be useful in mechanistic models for management of an extremely valuable fishery in a context of global change. Full article

Intelligent completion technologies, including Inflow Control Valves (ICVs), have become increasingly important for remotely managing zonal production in complex well architectures. However, quantifying flow rates and phase fractions in such systems remains challenging due to space constraints and the harsh downhole environment, which limit the deployment of conventional sensors. Distributed Acoustic Sensing (DAS) provides a promising solution by converting standard fiber-optic cables into dense arrays of acoustic sensors. While DAS has been successfully applied in applications such as integrity monitoring and leak detection, its use for direct two-phase flow characterization within intelligent completions remains largely unexplored. In this study, we present a DAS-based methodology to monitor and analyze oil–water two-phase flow in horizontal experiments that mimic field conditions. Acoustic data collected from DAS are transformed into time–frequency spectrograms using Short-Time Fourier Transform (STFT) to extract dynamic spectral features. These features are then correlated with pressure drop across the ICV and flow rate, revealing distinct frequency band behaviors associated with fluid changes. To quantify flow characteristics, a power-law model is trained using spectral features to predict flow rate and phase fractions. The results demonstrate strong predictive capability for pressure drop and flow rate under controlled laboratory conditions, highlighting the potential of DAS for multiphase flow diagnostics in field applications with intelligent completions, while water cut prediction remains challenging due to the complex and non-unique relationship between flow conditions and DAS response and is left for future work. This research not only provides new insights into the acoustic response of oil–water flows but also introduces a data-driven framework for leveraging DAS in real-time flow monitoring and control within ICV-equipped completions. Full article

Honeybees are indispensable pollinators for agricultural ecosystems, and a colony’s stability and reproductive capacity depend critically on the presence of a healthy queen. Acoustic monitoring has emerged as a promising non-invasive, lighting-independent approach for long-term colony observation. However, existing studies have largely been confined to single-apiary datasets or merged datasets from multiple similar apiaries for model training. Moreover, model evaluation has relied primarily on overall performance metrics, with insufficient attention to cross-region generalization and the detection of queen loss, a rare but critical condition. This study systematically investigates three complementary strategies: noise-augmented data diversification, lightweight convolutional neural network (CNN) architecture optimization via comprehensive ablation experiments, and transfer learning with fine-tuning to bridge the domain gap between source and target apiaries. Under cross-apiary evaluation, the proposed approach achieves an accuracy of 92.79%, a negative-class F1-score of 0.7900, and a negative-class recall of 0.7834 when only limited target-domain training samples are available. With full target-domain training data, the same strategy further attains an accuracy of 95.05%, a negative-class F1-score of 0.8596, and a negative-class recall of 0.8733. t-distributed Stochastic Neighbor Embedding (t-SNE) visualization demonstrates that noise augmentation effectively expands sample diversity in the feature space, while Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps confirm the successful transfer of source-domain acoustic features to the target domain. This work provides a practical approach for deploying acoustic-based queen status monitoring across diverse apiaries with minimal local data collection. Full article

Dolphins are widely recognized as intelligent marine mammals with sophisticated communication and echolocation. Accurately classifying their whistles is essential for understanding their communication patterns and monitoring their population size, structure, and distribution. In this study, we assembled a large, high-quality dataset of Indo-Pacific bottlenose dolphin (Tursiops aduncus) whistle signals collected at the Chimelong Ocean Kingdom. The dataset included multiple whistle categories, including a whistle type that has not previously been available for research. We then applied convolutional neural networks (CNNs) for classifying whistle signals, using five CNN architectures to analyze the signals. Model performance was evaluated using mean average precision (mAP), and the best-performing model achieved 0.929 in mAP on the test set, demonstrating that CNN-based approaches can effectively distinguish among different whistle classes. To probe robustness, we also introduced noise at defined SNR levels to increase testing complexity and assess the stability of the classifier. BELLHOP acoustic propagation modeling was used to generate channel impulse responses. These simulated signals were combined with the original signal data to construct an augmented training set. The results indicate that this augmentation enhanced the robustness of the classification model. Differentiating the whistle types is crucial as whistle categories may reflect variation in communication structure, behavioral context, or group-level acoustic patterns. Therefore, the proposed approach can support large-scale bioacoustic analysis and provide useful information for future studies on dolphin communication, behavior, and conservation. Full article

Real-time seismic reservoir monitoring of geologic reservoirs requires both large-scale data management and efficient computational workflows. Addressing these challenges is facilitated by developing techniques capable of selectively capturing critical geologic features, thereby increasing computational efficiency and reducing data storage requirements. This paper proposes a double-compression framework integrating Haar wavelet transforms with machine learning (ML) for efficient multiparameter seismic inversion. First, Haar wavelet compression significantly reduces the dimensionality of the input elastic models, preserving essential geologic structures while limiting data volumes. Next, a convolutional neural network with the long short-term memory (CNN-LSTM) architecture, including dual encoders and multi-decoders, compresses seismic data into a latent space to generate a multi-scale P-wave velocity estimate. By leveraging transfer learning to speed up convergence and enhance prediction accuracy, we fine-tune the latent representation to estimate the P-to-S-wave velocity ratio and acoustic impedance at multiple resolution scales. Tests on the synthetic ${\mathrm{CO}}_2$-injection Kimberlina model show that wavelet-based compression—including detuning large-scale trends—minimizes artifacts in simulated wavefields and accelerates neural-network training. The results demonstrate that combining wavelet-based pre-compression for reservoir models with data-driven latent encodings for seismic data achieves high compression ratios, reduces computational costs, and maintains the fidelity of subsurface imaging. Compared with a redundant-decimation baseline, the proposed framework reduces network training time by approximately 70% and GPU memory usage by 33–73%, achieves a wavefield energy loss below 0.1% at a 16:1 model-dimension reduction, and produces multi-resolution predictions of $V_{\mathrm{P}}$, $V_{\mathrm{P}}/V_{\mathrm{S}}$, and acoustic impedance with normalized errors below 0.04 across all six wavelet decomposition levels. Thus, the double-compression framework enables robust and scalable seismic monitoring of elastic reservoir parameters. Full article

This study addresses the critical challenge of sediment-driven degradation in aging hydropower infrastructure by implementing a novel Digital Operational Technology modernization framework at the AES Chivor Hydropower Plant in Colombia. While conventional sediment monitoring relies on sporadic manual campaigns, this research introduces a continuous, real-time sensing architecture that integrates hybrid acoustic–optical sensors, covering a range of 10 to 6000 mg/L, directly into the plant’s SCADA (Supervisory Control and Data Acquisition) system. The novelty of this approach lies in the seamless coupling of high-frequency physical data (15 min sampling) with an Operational Decision Support Module, enabling adaptive turbine management. Statistical validation against laboratory gravimetric standards yielded a robust correlation of 0.93, confirming the system’s precision in quantifying suspended sediment concentrations. By identifying critical fine particle fractions in real time, the proposed model enables a precision-based maintenance strategy that significantly reduces unscheduled production downtime and mitigates accelerated wear in Pelton turbines. These findings provide a scalable benchmark for extending the operational life of large-scale hydropower facilities facing advanced sedimentation risks through digital transformation. Full article

Permafrost extent and active layer thickness (ALT) have implications for polar-region infrastructure and communities. Much of the world’s permafrost is rich in ground ice and can become highly unstable during seasonal freeze–thaw cycling. Monitoring these dynamics is critical for quantifying infrastructure risk, informing new construction, and prioritizing essential repairs of existing infrastructure. Fiber optic distributed acoustic sensing (DAS) offers an alternative, providing high-resolution monitoring over large distances. This proof-of-concept study evaluates a surface-deployed DAS cable as a rapid, nondestructive tool for observing seasonal thaw in discontinuous permafrost in Fox, Alaska. During three field campaigns (May 2024, September 2024, and June 2025), a surface laid cable recorded active source sledgehammer strikes. Dispersion curves extracted from the surface wave data were aligned with theoretical curves using a simplified two-layer forward model, representing a seasonally thawed layer overlying hard frozen ground. Based on best fit estimates derived from this model, the active layer thickness was calculated at approximately 0.8 m in May 2024, thickening to 1.9 m in September 2024, and 0.65 m in June 2025. These results demonstrate that surface-deployed DAS can effectively observe changes in permafrost seasonal thaw. This technique could be used prior-to and/or in-addition-to performing more invasive, time-consuming subsurface investigation. Full article

Quantifying the detection efficiency of buoy-based sonar and optimizing deployment strategies in complex marine environments remain significant challenges. This study proposes a transceiver depth optimization method based on the Bellhop ray model to enhance underwater remote sensing data quality. For the first time, we validated the applicability of acoustic reciprocity in deep-sea environments exceeding 5000 m, characterized by non-uniform sound speed profiles, horizontal inhomogeneity, and steep seamount terrain, with a maximum relative error of <1.2%. This extends the applicable boundaries of the acoustic reciprocity theorem from idealized simple waveguides to complex, realistic deep-sea environments. Building on this validation, we developed a novel, equivalent, superposition modeling framework for bidirectional transmission loss (TL), which converts the computationally intractable TL from target to receiver into the calculable TL from receiver to target, thus significantly reducing computational complexity. Systematic simulations uncovered a depth-layered dependency mechanism: shallow sources (23.14~69.42 m) and deep sources (≥347.10 m) show robustness to large depth differences exceeding 500 m, whereas mid-layer sources (161.98~231.40 m) exhibit a distinct critical threshold effect. Static simulations identify a performance degradation cliff with an onset at an approximate depth difference of 185 m, leading to a 50% reduction in detection range and fragmented near-field detection coverage. To accommodate environmental temporal variability (e.g., internal waves), a conservative safety margin was incorporated, establishing a robust engineering threshold of 150 m. Accordingly, we define 160~350 m as the optimal detection depth window and propose a layered deployment protocol that fills a critical industry gap in quantitative deployment design for deep-sea acoustic detection. Specifically, transceiver depth differences should be strictly constrained to <150 m for mid-layer operations, while more-flexible depth configurations are permissible for shallow and deep sources. These findings furnish quantitative engineering criteria for the design of reliable underwater remote sensing networks, while balancing long-range detection stability and near-field coverage integrity. Full article

Additive manufacturing (AM) has emerged as a transformative technology across multiple industries, from aerospace to biomedical applications. The integration of artificial intelligence (AI) and machine learning (ML) into AM processes represents a paradigm shift toward intelligent, autonomous manufacturing systems. This comprehensive review synthesizes recent advances in AI/ML applications across the entire AM life cycle—from design optimization and process planning through in situ monitoring, closed-loop control, and post-process qualification. The analysis is organized by ISO/ASTM AM process families, including powder bed fusion (PBF), directed energy deposition (DED), material extrusion (MEX), vat photopolymerization (VP), binder jetting (BJ), material jetting (MJT), and sheet lamination (SL). For each process family, the review examines the specific AI/ML techniques employed, the data modalities utilized (thermal imaging, acoustic signals, in situ cameras, CT/NDE data), and the current state of deployment from research prototypes to industrial implementation. The analysis reveals that while significant progress has been made in single-stage ML applications such as defect detection and parameter optimization, truly integrated life cycle-wide AI-driven AM workflows remain largely aspirational. Key challenges are identified including data scarcity, model generalization across machines and materials, real-time control constraints, and certification requirements. Finally, future research directions are outlined toward autonomous AM systems enabled by physics-informed ML, digital twins, and hierarchical AI architectures. Full article

People spend the majority of their lives within environments shaped by multiple interacting exposures, including thermal conditions, acoustic noise, lighting, and air quality, yet remain largely unaware of how these settings affect their comfort. Existing comfort research treats domains in isolation under controlled laboratory conditions, leaving real-world multi-domain effects on personal comfort underexplored. This paper proposes a unified Comfort Framework that fuses three practical data layers: macro-environmental conditions retrieved via location-based APIs, kinematic and micro-environmental context captured through smartphone sensors, and physiological responses recorded by a chest-worn ECG sensor. Binary comfort states are labelled in real time using a minimal-disruption lap-button protocol on a consumer smartwatch. We validate the pipeline through a single-subject pilot of 18 free-living sessions. Random Forest classification across 10 valid leave-one-session-out folds achieved an F1 macro of 0.456 ± 0.151, indicating that consumer wearable comfort prediction in unconstrained free-living conditions is more challenging than controlled chamber studies suggest. Descriptive statistics showed dataset-level differences between comfort states in wrist skin temperature (31.9 vs. 33.3 °C), heart rate (70.7 vs. 77.1 bpm), and RMSSD (42.1 vs. 34.3 ms), with overlap between classes consistent with the modest classification performance. SHAP analysis identified acoustic features, HRV features, and wrist temperature as the strongest comfort signals. The framework is architecturally designed to address all four IEQ domains, though this pilot empirically validated only thermal and acoustic signals. Full article

Accurate classification of seal wear is essential for condition-based and predictive maintenance of hydraulic cylinders, where seal degradation can cause fluid leakage and impair normal system operation. This study investigates the adaptation of a Transformer-based audio model for classifying seal wear conditions using acoustic emission (AE) signals. Specifically, we adapt the Audio Spectrogram Transformer (AST), a convolution-free, purely attention-based model that operates directly on audio spectrograms. The Transformer architecture enables the modeling of long-range dependencies, while the model learns discriminative representations directly from AE data without relying on manually engineered features. A selective fine-tuning strategy was implemented by adding layer-freezing functionality to the AST training pipeline, enabling different freezing configurations during fine-tuning. This allowed earlier pretrained representations to be preserved while adapting the later layers to the target AE signals, thereby reducing the risk of overfitting in the small-data setting. In addition, validation-driven early stopping was implemented to further improve generalization during fine-tuning. The model was initialized with ImageNet and AudioSet pretrained weights to exploit general-purpose representations learned from large-scale datasets. The AE data were acquired under varying pressure conditions on a hydraulic test rig designed to simulate hydraulic cylinder leakage. The datasets were partitioned into fine-tuning, validation, and evaluation subsets and labeled into three wear states: unworn, semi-worn, and worn. In addition, data augmentation techniques were applied to the fine-tuning data to increase diversity and mitigate class imbalance. The adapted model achieved 97.92% classification accuracy across all wear conditions and pressure settings, demonstrating its ability to learn discriminative wear-related patterns directly from AE data. Furthermore, the framework’s versatility was further assessed on a bearing strip dataset acquired from the same hydraulic test rig. Using the same fine-tuning configuration, the model achieved 95.65% accuracy and 100% recall for the worn state. These findings highlight the potential of transformer-based architectures for data-efficient, end-to-end AE-based diagnostics across hydraulic system components. Full article

Compressed air systems (CASs) represent a significant portion of energy consumption in large-scale built environments and manufacturing facilities, suffering from both micro-level physical pipeline leaks and macro-level operational inefficiencies. This paper proposes a unified, dual-action artificial intelligence framework aimed at advancing building decarbonization by systematically integrating acoustic-vision leak quantification with schedule-aware machine learning. Specifically, the framework targets pneumatic pipe connection leaks, fitting leaks, and joint degradation faults within compressed air distribution networks, which are the primary sources of micro-level volumetric energy losses in industrial building systems. First, a probabilistic multimodal fusion algorithm (MPSF) using an ultrasonic camera is developed to detect and geometrically quantify physical leaks, successfully translating pixel areas into physical facility energy loss metrics (estimating 11.0 kW of wasted power from detected severe leaks). Second, to optimize the compressor’s supply matching the actual facility demand without risking data leakage from internal flow sensors, an eXtreme Gradient Boosting (XGBoost) model is proposed. By utilizing only external building environmental conditions and the real-time operational schedules of 13 distinct zones, the model achieves highly accurate dynamic power prediction (R² = 0.9698). Finally, comprehensive simulations based on real-world digital monitoring data from a facility-scale built environment demonstrate that only the concurrent application of both modules ensures stable end-point pressure. The integrated framework achieves a substantial system-wide building energy reduction of over 20% to 40% compared to baseline constant-pressure operations, yielding an estimated annual reduction of 116 tons of CO₂ emissions, thereby providing a direct pathway toward carbon-neutral building operations. Full article

Indoor environmental quality (IEQ) is increasingly recognized as a critical factor in shaping employee well-being, satisfaction, and work performance, particularly in hybrid workplace settings. This mixed-methods study examined how integrated IEQ conditions influence employee experience in a public-sector hybrid workplace through a case study of the WorkHub, a technology-enabled flexible workspace embedded within a large municipal utility. Quantitative data were collected from 93 valid survey responses using the Workplace Environment Satisfaction and Performance Questionnaire (WESP-Q™), and qualitative insights were obtained from a 90-min participatory think tank session with 24 employees. Results showed that WorkHub users reported significantly higher satisfaction across 15 of 18 environmental and spatial dimensions, including layout, thermal comfort, air quality, lighting, furnishings, cleanliness, and overall building experience. They also reported significantly stronger outcomes in collaboration access, work transition, focus support, work efficiency, workspace productivity, pride in work, and job satisfaction. Qualitative findings reinforced these results, highlighting technology integration, daylight, and spatial flexibility as key strengths, while identifying acoustics, thermal discomfort, and limited privacy as persistent challenges. These findings support a systems-oriented, human-centered approach to workplace design, demonstrating that integrated IEQ can enhance employee experience, collaboration, and organizational performance in hybrid public-sector environments. Full article