Jun 2026· Journal of Computers· Vol 37, pp. 383-400· 0 citations
TL;DR
A real-time fault diagnosis framework based on Edge-IoT that combines high-precision onboard signal acquisition, resource-efficient incremental learning, and joint optimization of edge and cloud systems for intelligent maintenance of railway drive systems is proposed.
Abstract
Reliable condition monitoring of traction drive units in rail transit is crucial for the safe and stable operation of rail vehicles. However, vibration signals acquired under actual operating conditions often exhibit significant randomness, load fluctuations, and speed fluctuations, leading to unclear fault characteristics and high non-stationarity, thus reducing the effectiveness of traditional autonomous diagnostic models. To overcome these limitations, this study proposes a real-time fault diagnosis framework based on Edge-IoT. This framework combines high-precision onboard signal acquisition, resource-efficient incremental learning, and joint optimization of edge and cloud systems. The edge layer efficiently extracts features and performs low-latency inference, while the cloud layer provides long-term knowledge integration to continuously improve the model’s adaptability. The incremental learning strategy enables the diagnostic model to adapt to new operating conditions and constantly changing fault types without requiring large amounts of labeled data or complete retraining. Experiments on traction drive unit datasets under various operating conditions demonstrate that, compared with existing methods, the proposed method achieves higher diagnostic accuracy, lower latency, and significantly reduced communication overhead. This work provides a practical and scalable solution for the intelligent maintenance of railway drive systems.
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.
Y. Ouyang, W. Liang· Advanced Electromagnetics· 0 citations
Results indicate that the proposed framework can identify bearing fault components under real compound machine fault interference and provide a practical solution for HSR bearing health monitoring, early fault warning, and maintenance decision support when labeled field data are limited.
Hanwei Zheng, Hongyi Zhang, Ming Yang et al.· PeerJ Computer Science· 0 citations
The fast changing nature of connected vehicles requires smart systems that can monitor health in real-time and predict failures. This paper introduces an IoT-based Smart Vehicle Monitoring and Predictive Maintenance System that involves onboard sensors, edge computing, and cloud analytics to ensure high reliability and reduce unscheduled downtimes. Multi-modal transportation data (temperature, vibration, fuel consumption, and brake parameters) are continuously measured with the help of ESP32/Arduino devices connected to the CAN bus. An Extended Kalman Filter (EKF) is an algorithm that does nonlinear sensor fusion at the edge to enhance data reliability and noise minimization. The degradation prediction and Remaining Useful Life estimation of the fused time-series data are done with a Long Short-Term Memory (LSTM) network. A new adaptive thresholding system is used to dynamically regulate anomaly sensitivity according to driving context and historical trends. It was evaluated experimentally on 100,000 real-time sample divided into 70% training, 15% validation and 15% testing sample. The proposed framework was found to have 97.4% prediction accuracy with Precision (94.8%), Recall (95.6%), and F1-score (95.2%). EKF preprocessing minimized RMSE by 0.84, and it is a 56-percent improvement in estimation accuracy. The adaptive detection module reduced false positive rate to 4.1% which was 63% lower than the approaches to static threshold. The system forecasted failures almost 22 minutes earlier than it was actually detected which enhanced early detection by almost 30 percent. The edge deployment decreased latency by a factor of 4 to 160 ms, and thus, allowed the creation of nearly real-time alerts. The implementation of visualization and alert management was realized based on the use of Node-RED and Grafana dashboards, along with MQTT-based secure communication. All in all, the framework is statistically proven to be robust, scaled and applicable to next-generation intelligent vehicular IoT ecosystems.
Biswaraj Roy, B. Prasath, Ajit Kumar Singh et al.· 2026 6th International Confe...· 0 citations
Results indicate that artificial intelligence can significantly strengthen the resilience and automation of next-generation smart grid infrastructures.
T. Anvesh, Akshaya Chelpuri, Ambati Chandu· International Scientific Jou...· 0 citations
An Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology is proposed that contributes to the development of intelligent and self-optimizing industrial environments aligned with Industry 4.0 objectives.
Gajula Prasad Gajula Prasad, Bolloju Divya Sri Bolloju Divya Sri, Dr B Ramprasad Dr B Ramprasad· International Journal of Sci...· 0 citations
This research proposes an AI-driven, edge-based system for electric motor health monitoring and predictive maintenance using multi-sensor data, and achieves low latency, reduced bandwidth usage, and fast on-site decision-making using TinyML, an edge AI framework.
F. Haruna, M. Abdulraheem, I. O. Durotoye et al.· SPE Nigeria Annual Internati...· 0 citations