Optimization and Improvement of Operation and Maintenance Efficiency of Condition Monitoring Technology for Offshore Wind Turbines

An Internet of Things-based intelligent monitoring and fault detection method for the operation and maintenance of offshore wind turbines

The rapid expansion of offshore wind farms has introduced significant challenges to operation and maintenance (O&M), particularly under harsh marine environments where reliable electromagnetic information transmission and constrained wireless communication resources directly affect intelligent monitoring performance. Traditional Supervisory Control and Data Acquisition (SCADA) systems relying on cloud-centric architectures often encounter excessive latency and bandwidth bottlenecks when transmitting high-frequency vibration signals, limiting real-time fault diagnosis. To address these issues, this study proposes an Internet of Things (IoT)-based intelligent monitoring and fault detection method built upon an Edge-Cloud collaborative architecture. A lightweight Adaptive One-Dimensional Convolutional Neural Network (A-1D-CNN) is developed for deployment on edge gateway devices, enabling direct extraction of fault characteristics from raw vibration signals without manual feature engineering. Combined with an “ Edge-Training, Cloud-Update” strategy, the proposed framework continuously optimizes diagnostic performance while substantially reducing communication overhead across wireless sensing and electromagnetic transmission infrastructures. Experimental evaluation on a standard bearing fault dataset demonstrates that the proposed method achieves a fault diagnosis accuracy of 99.25% with a compact model size of only 0.45 MB, providing an effective balance between diagnostic precision and deployment efficiency. The results indicate that the proposed framework offers a practical solution for real-time intelligent monitoring in bandwidth-limited offshore environments and provides technical support for reliable electromagnetic-enabled sensing networks and distributed fault diagnosis in next-generation offshore energy systems.

A Comprehensive Review on Real-Time Performance Assessment & Operational Fault Monitoring in Hydromachinery

Hydromachinery is vital for clean and sustainable power generation, where reliable and efficient operation directly supports the stability of hydropower plants. To achieve this, real-time performance tracking and fault monitoring are becoming increasingly important. This review summarizes recent techniques and technologies used for monitoring turbines and their components in operation. Key areas include sensor-based data collection, modern signal processing tools, and artificial intelligence methods for detecting issues such as cavitation, vibration irregularities, pressure fluctuations, and mechanical wear. Methods like wavelet analysis, principal component analysis (PCA), support vector machines (SVM), and digital twins are discussed for their roles in fault diagnosis and performance evaluation. Advances in IoT-enabled monitoring and predictive maintenance are also highlighted, demonstrating their potential to enhance reliability and minimize downtime. The paper further outlines challenges such as harsh operating conditions, large data handling, and the need for accurate predictive models. Future directions are suggested, focusing on hybrid machine learning approaches, adaptive monitoring strategies, and digital twins for smart, autonomous health management of hydro machinery.

Health Monitoring of Offshore Wind Structures: Sensing Technology, Uncertainty, and Artificial Intelligence

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.

Vibration-induced failures in industrial ventilation systems: A predictive maintenance approach

Operational Availability Assessment of Tidal Stream Turbines Using Environmental Data and Fuzzy Logic Inference

In hybrid energy systems, maintaining an optimal scheduling strategy for real-time distribution systems, particularly in triple hybrid power generation units, remains a critical challenge. The lack of an efficient real-time observability platform for off-grid hybrid units directly impacts scheduling priorities. In this work, a novel operational condition monitor that has a data-driven predictive mechanism for determining the instant states of each tidal stream turbine is proposed. Environmental variables are first preprocessed using a multivariate fuzzy logic system to generate informative features, which in turn are used by a machine learning classifier to identify the turbine availability states. The classifier is evaluated using K-fold cross-validation and robustness under increasing environmental noise levels. The main contributions of this work are the reduction in uncertainty and the association with real-time operating conditions, which enable optimal scheduling decisions. The baseline XGBoost classifier achieved an F1-score that increased after adding fuzzy-derived features. Comparative evaluation under noise-free and increasing noise levels demonstrates that the proposed framework consistently outperformed the baseline model while maintaining robust classification performance.

Research on Innovative Application of Energy Self-Powering Technology in Health Monitoring of Wind Turbine Tower Bolts

To reduce the operation and maintenance losses caused by critical component failures in wind turbines, this study proposes a piezoelectric-electromagnetic multi-source energy self-powering scheme for tower bolt health monitoring, providing an efficient solution for long-term autonomous sensing in intelligent electromechanical systems. Considering the growing demand for low-power electromagnetic energy harvesting and reliable wireless monitoring technologies, the bolt failure mechanism and tower operating environment are systematically analyzed to develop an innovative composite architecture integrating an arc-shaped piezoelectric harvesting unit with a micro electromagnetic energy acquisition module and a low-power energy management system. A three-level validation framework consisting of simulation, laboratory experiments, and engineering deployment is established to evaluate the proposed approach. Experimental results demonstrate that the device achieves an energy conversion efficiency of 28.7% under vibration frequencies of 0.1–5 Hz and continuously powers monitoring nodes for more than 30 days. Field deployment in a northern wind farm enables 72-hour early warning of bolt loosening and reduces the annual maintenance cost of a single turbine by approximately 32,000 yuan. The proposed self-powered monitoring framework provides a compact, long-life, and cost-effective solution for structural health monitoring of critical infrastructure and offers valuable technical references for electromagnetic energy harvesting, intelligent sensing networks, and wireless monitoring applications in advanced engineering systems.