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.
ABSTRACT -The increasing integration of renewable energy resources, distributed generation, electric vehicles, and intelligent monitoring devices has significantly enhanced the complexity of modern smart grids, making conventional fault detection techniques inadequate for ensuring reliable and secure power system operation. This paper presents an AI-based fault detection framework for smart grids that utilizes machine learning and deep learning techniques to identify, classify, and localize electrical faults in real time. The proposed framework collects operational data from intelligent electronic devices (IEDs), phasor measurement units (PMUs), smart meters, and IoT-enabled sensors. The acquired data undergo preprocessing, normalization, and feature extraction before being analyzed using a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The CNN effectively extracts spatial fault characteristics, while the LSTM captures temporal variations in electrical signals for accurate fault prediction. The trained model is deployed on an edge-cloud architecture to enable low-latency fault detection, rapid decision-making, and remote monitoring. Experimental evaluation demonstrates that the proposed system achieves high fault detection accuracy, reduced false alarm rates, and faster response times compared with conventional rule-based and statistical approaches. The framework also enhances grid reliability, minimizes outage duration, supports predictive maintenance, and improves operational efficiency under dynamic grid conditions. These results indicate that artificial intelligence can significantly strengthen the resilience and automation of next-generation smart grid infrastructures.
Keywords— Smart Grid, Artificial Intelligence (AI), Fault Detection, Deep Learning, Machine Learning, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Internet of Things (IoT), Predictive Maintenance, Edge Computing.
T. Anvesh, Akshaya Chelpuri, Ambati Chandu· International Scientific Jou...· 0 citations
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.
Y. Ouyang, W. Liang· Advanced Electromagnetics· 0 citations
Real-time environmental monitoring in industrial plants requires rapid signal processing, reliable data transmission, and accurate anomaly detection under complex operating conditions. To address the limitations of conventional centralized monitoring systems, this study proposes an industrial flue-gas monitoring and early-warning framework based on embedded edge computing. A distributed sensing architecture integrating multi-sensor arrays, edge computing nodes, and cloud-assisted management is developed to enable local real-time processing and low-latency response. Multi-sensor fusion algorithms are employed to improve measurement reliability for pollutant concentration, flow rate, and environmental parameters, while lightweight intelligent models deployed at edge nodes perform anomaly detection and trend prediction. A cloud–edge collaborative mechanism is further introduced to support continuous model optimization and distributed network management. The modular architecture incorporates wireless Mesh networking and fault-tolerant data synchronization, enhancing system robustness in environments affected by electromagnetic interference and unstable communication conditions. Compared with conventional centralized systems, the proposed framework significantly improves response speed, monitoring accuracy, and early-warning capability while reducing network dependence and deployment complexity. The proposed architecture provides an effective engineering solution for distributed sensing, intelligent monitoring, and real-time information processing in large-scale industrial environments.
With the continuous improvement of the networked layout of transportation infrastructure, the structural safety and operation efficiency of roads and bridges, as the core hubs, have become key factors affecting the sustainable development of transportation. Traditional monitoring methods have limitations such as weak real-time performance, insufficient data coverage, and delayed anomaly warnings, which render them unable to meet the requirements of precise management under complex conditions. Therefore, this paper designs an intelligent monitoring system for roads and bridges based on Internet of Things technology. This system integrates a multi-source sensor fusion architecture, edge-cloud collaborative computing, adaptive data processing algorithms, and an improved attention-mechanism Long Short-Term Memory (LSTM) anomaly warning model, achieving full-dimensional, high-precision, and real-time monitoring of bridge structural strain, vibration, settlement, and environmental parameters. The system constructs a five-level architecture comprising perception, transmission, processing, warning, and application, and introduces a sensor node dynamic deployment model, a multi-modal data weighted fusion algorithm, and a load-adaptive scheduling mechanism. This approach effectively solves problems such as heterogeneous data transmission conflicts, edge-node computing power bottlenecks, and low accuracy of anomaly identification under complex conditions. Experimental results show that the average error of sensor data collection is controlled within 0.32%, the data transmission delay is as low as 18.7 ms, and the anomaly warning accuracy reaches 97.6%, which is 15.3%, 42.6%, and 21.8% higher than those of traditional monitoring systems, respectively. In actual bridge operation scenarios, the system can effectively identify potential risks such as crack expansion and structural settlement, shorten the fault response time to within 3 min, and maintain a stable operation rate of 99.2% even under extreme weather conditions, such as heavy rain and strong winds, as well as under high-traffic conditions. This system provides an intelligent and scalable technical solution for the full life-cycle management of roads and bridges, applicable to the regular monitoring and emergency response of large-scale transportation infrastructure such as expressway bridges, urban overpasses, and long tunnels.
Motor drive systems operating in embedded environments are frequently affected by noise, dynamic loading conditions, and electromagnetic interference, making timely fault diagnosis difficult. To improve diagnostic accuracy and real-time performance, this study proposes an intelligent fault diagnosis framework based on embedded multi-source data acquisition and feature fusion. High-precision sensors are employed to synchronously collect vibration and current signals, while improved wavelet packet decomposition and principal component analysis are combined to extract discriminative multi-dimensional fault features and eliminate redundant information. A lightweight convolutional neural network optimized for embedded deployment is then developed to perform low-latency fault classification and edge inference. Experimental results show that the proposed method achieves an average F1-score exceeding 97% under complex mixed-fault conditions, while maintaining an average detection latency of approximately 45 ms. The proposed framework demonstrates strong robustness and computational efficiency, providing practical support for intelligent industrial maintenance and offering reference solutions for embedded sensing, signal processing, and electromagnetic compatibility environments.
This paper presents an intelligent protection framework for fault detection, classification, and location in power distribution networks by combining Discrete Wavelet Transform (DWT)-based feature extraction, Support Vector Machine (SVM)-based decision making, and Internet of Things (IoT)-enabled cloud monitoring. An IEEE 16-bus distribution system is modeled in MATLAB/Simulink, where transient current signals are processed using DWT to extract discriminative time–frequency features. A comparative evaluation of different mother wavelets and decomposition levels is performed to identify the most effective feature extraction configuration in terms of accuracy and computational efficiency. The extracted features are processed locally by SVM-based models for fault detection, classification, and location, while selected fault-related features are simultaneously transmitted to the ThingSpeak cloud platform for cloud-assisted monitoring and remote accessibility. The proposed framework is evaluated under a wide range of operating conditions, including different fault types, overload events, load switching, capacitor switching, and scenarios with integrated photovoltaic and wind generation. The results demonstrate 100% fault classification accuracy and fault-location accuracies ranging from 97.95% to 99.88% within the investigated simulation scenarios. Furthermore, the proposed approach effectively distinguishes faults from non-fault disturbances, thereby reducing the likelihood of false fault indications. The findings demonstrate that the proposed intelligent protection framework provides accurate and reliable fault detection, classification, and location through optimized DWT-based feature extraction and SVM-based decision making under the investigated simulation scenarios. Nevertheless, additional validation using noisy measurements and hardware-based experimental platforms is required to further assess the robustness and practical applicability of the proposed protection methodology under real operating conditions.
E. M. Shalby, A. Abdelaziz, Eman S. Ahmed et al.· Scientific Reports· 0 citations