Jul 2026· Italian National Conference on Sensors· Vol 26, pp. 4697· 0 citations· 213 references
Medicine
Abstract
Offshore wind farms are rapidly expanding into deeper and more remote ocean regions. Their structural safety and operational reliability in harsh marine environments have garnered widespread global attention. Sensing technologies capture structural and environmental conditions and are indispensable to structural monitoring. Accordingly, this review examines the applications of environmental monitoring, supervisory control and data acquisition, condition monitoring, and structural health monitoring systems covering both the horizontal-axis and vertical-axis types of fixed and floating offshore wind turbines. It also summarizes key technologies for data transmission and optimal sensor placement. However, uncertainty in sensing data can significantly affect monitoring results, yet existing studies lack an adequate summary and in-depth discussion. We therefore focus on sources of sensing uncertainty, including the marine environment, the host platform, variations in environmental and operational conditions, and sparse sensing. By analyzing their effects on monitoring data, we explore key methods for overcoming data uncertainties and improving sensing accuracy. This paper also evaluates the application potential of cutting-edge artificial intelligence and digital twin technologies. Furthermore, the study points out that fusing multi-source signal data to establish a highly reliable intelligent decision-making and early warning framework is likely to become an important development direction for offshore wind power monitoring. This review aims to provide valuable support for the safe development of offshore wind farms towards deep-sea regions over the coming decades.
This study proposes an integrated condition-monitoring and predictive-maintenance framework for offshore wind turbines operating in harsh marine environments. To address the challenges of signal degradation, environmental interference, and limited fault-warning capability, a multi-source sensing architecture is developed based on risk-driven sensor deployment, edge-side signal fusion, and intelligent health assessment. Vibration, temperature, strain, and operational signals are adaptively processed through variance-weighted fusion and denoising strategies to improve data reliability. A CNN– LSTM hybrid model is employed for fault feature extraction and temporal degradation analysis, while a digital-twin-driven health assessment framework is used to quantify health indices and remaining useful life. Maintenance scheduling is further optimized by integrating equipment health conditions, resource constraints, and operational windows. Field validation in an offshore wind farm demonstrates that the proposed diagnostic model achieves a fault identification accuracy of 96.2%, while the predicted remaining useful life of the main bearing decreases from 180 days to 16 days before failure. The proposed framework establishes a closed-loop process linking signal acquisition, intelligent diagnosis, lifetime prediction, and maintenance decision-making, providing an effective engineering solution for reliable condition monitoring and intelligent operation of offshore energy systems.
Yanqing Ouyang, W. Liang· Advanced Electromagnetics· 0 citations
One challenge in structural health monitoring (SHM) is to make reliable statements about the condition of the structure using only a few sensors, which is desirable from both technical and economic perspectives, as wind turbines often already have one acceleration sensor installed at the nacelle. Virtual sensing methods are used for this purpose, enabling the estimation of the structural response at non-instrumented locations using a structural model and measurement data. Knowledge of the structural response of wind turbines is desirable, as it provides the foundation for lifetime extension or load-optimised operation.
The method applied here uses only one DC-capable acceleration sensor to estimate the full-field strain across the entire support structures of wind turbines. It utilises the tilt error present in the measured acceleration of tower structures to estimate the displacement in the frequency range below 0.05~Hz. The strains are subsequently estimated from the displacements using a transfer function based on a multi-band modal decomposition and expansion.
For this contribution, the proposed method is applied to measurement data from an operating offshore wind turbine. The focus lies on the influence of uncertainties in the structural model on the accuracy of the strain estimation. The investigated modelling errors include parameters that are usually known to the operators with high levels of uncertainty, such as modelling of the scour protection, the soil stiffness, and the rotor. The influence of the selected parameters on the accuracy of strain estimation is compared using the damage-equivalent strains.
The results showcase which parameters have a significant influence on the accuracy of strain estimation and indicate whether measures should be taken to reduce the uncertainty in the modelling to increase the strain estimation accuracy.
Jonathan Thurn, Clemens Jonscher, G. Zorzi et al.· e-Journal of Nondestructive...· 0 citations
Environmental contamination is a critical global challenge affecting ecosystems, human health, and climate stability. Rapid industrialization and urbanization have increased pollution in air, water, and soil, while traditional monitoring methods based on manual sampling and laboratory analysis are insufficient to address complex and dynamic pollution patterns. This paper reviews technological advancements transforming pollution detection, including sensor technologies, artificial intelligence, remote sensing, IoT architectures, and data-driven modeling. Modern systems emphasize real-time monitoring, distributed sensing networks, and predictive analytics. Emerging sensing platforms integrate nanotechnology, biosensors, spectroscopy, and MEMS to enhance sensitivity and response time, while satellite imaging and UAVs enable large-scale environmental monitoring. Machine learning algorithms further support anomaly detection, source identification, and forecasting. The study categorizes detection technologies and proposes an integrated monitoring framework evaluated on accuracy, scalability, latency, and robustness. Findings highlight the effectiveness of hybrid systems combining physical sensors with AI-based inference models. Despite improvements in detection accuracy and cost efficiency, challenges remain in calibration stability, data heterogeneity, energy efficiency, cybersecurity, and ethical data use. Future research should focus on self-organizing sensor networks, edge intelligence, multi-modal data fusion, and sustainable monitoring infrastructures
Sneha Banerjee· International Journal of Eme...· 0 citations
Monitoring mining infrastructure is essential for ensuring operational safety and minimizing environmental risks associated with subsidence and structural instability. However, existing approaches often rely on single-source data, limiting their ability to capture complex and dynamic deformation processes. This study proposes an integrated multi-source monitoring framework that combines satellite-based Interferometric Synthetic Aperture Radar (InSAR), unmanned aerial vehicle (UAV) observations, ground-based measurements, and advanced digital analysis techniques. The methodology integrates multi-temporal InSAR time-series analysis with high-resolution UAV-derived data within a unified geospatial environment, supported by machine learning models for pattern recognition and prediction. The results reveal clear spatial and temporal deformation patterns in mining areas, with subsidence rates up to −50 mm/year in active extraction zones. Time-series analysis shows that deformation evolves nonlinearly, underscoring the importance of continuous monitoring for early risk detection. Validation against ground-based measurements confirms the reliability of the proposed approach, with root-mean-square error (RMSE) values in the range of 1–2 mm. The integration of multi-source data significantly improves monitoring accuracy and enables detailed analysis across different spatial scales. Furthermore, the application of machine learning techniques enhances predictive capability, allowing identification of potential instability zones before critical failure occurs.
Nurbek Abdixamidov, Maksuda Juraeva, Nafosat Meyliyeva et al.· Geotechnology, Mining and Ra...· 0 citations
Structural Health Monitoring (SHM) is essential for ensuring the safety, serviceability, and longevity of civil infrastructure through continuous condition assessment. Among various SHM techniques, the Electromechanical Impedance (EMI) method using piezoelectric (PZT) transducers has gained considerable attention owing to its high sensitivity, simplicity, and capability for real-time local damage detection. This paper reviews the fundamental principles, sensing mechanisms, technical requirements, and practical applications of the EMI technique. The effects of key parameters, including PZT dimensions, excitation frequency, adhesive properties, sensing range, temperature variations, and damage assessment metrics, on monitoring performance are discussed. Applications of EMI-based SHM in concrete, steel, composite, bridge, pipeline, aerospace, and retrofitted structures are also reviewed. Recent developments in low-cost instrumentation, wireless sensing, energy harvesting, and dual-PZT configurations are examined, along with existing challenges and future research directions. The EMI technique offers significant potential for robust, cost-effective, and adaptable structural health monitoring and smart infrastructure management.
Samapika Samadarsani Karan, Satyajit Mahapatra, Samyukta Choudhury et al.· International Research Journ...· 0 citations
Structural Health Monitoring (SHM) has become an important science to help keep civil infrastructure like bridges, buildings, dams, and industrial plants safe, reliable and lasting. Traditional methods of SHM have depended primarily on wired sensor networks and periodic manual inspections that are regularly expensive, labor-intensive and lack real-time responsiveness. The present-day breakthroughs in Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have made it possible to create intelligent, scalable, and energy-efficient SHM systems that would allow continuous monitoring and remote diagnostics. This paper provides an extensive exploration of Structural Health Monitoring with wireless IoT sensors in terms of system architecture, sensing modalities, communication protocols, data processing methods, and damage detection methods. The intricate literature survey reveals the development of the SHM technologies and outlines the gaps in the research. The suggested methodology will combine low-power wireless sensors, edge computing, and cloud-based analytics to track such structural parameters as strain, vibration, displacement, and temperature. Damage detection and feature extraction mathematical models are discussed and the experimental validation strategies. The findings support the idea that IoT-based SHM systems can greatly improve the accuracy of fault detection, decrease the cost of maintenance, and predictive maintenance. The paper has reached the conclusion that wireless IoT-based SHM is an innovative solution to smart infrastructure management and offers the research perspectives on its future development in terms of scalability and artificial intelligence incorporation.
Chloe King, Liam Walker, Grace Young· International Journal of Dat...· 0 citations
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