Visual Analytics Dashboard for Real-Time Monitoring of Industrial Equipment
Year: 2026
Type: other
Abstract: The current paper is the design and implementation of a Visual Analytics Dashboard to monitor the industrial equipment in real-time, combining the data streams of the IoT-based sensor with the edge analytics and machine learning engine. The suggested system will provide the maintenance teams with a high-resolution dashboard and enables visualization of the live data, anomaly detection, and human-in-the-loop interaction. A sensor emulator, edge gateway and Python-based ML engine were created to form an experimental testbed, which recorded vibration, voltage, and temperature measurements in real-time. Statistical thresholding and pattern learning are used to detect anomalies, and the dashboard includes interactive zooming, annotation, and alert recognition features.The proposed dashboard proves to be much more responsive, more usable and more successful in detecting anomalies compared to traditional SCADA-based systems. Compared performance analysis indicates that there is a 47% decrease in latency, 16% improvement in the accuracy of anomaly detection, and 38% improvement in the score of usability. The architecture encourages scalability, edge processing, and operator-oriented feedback loops, which is why it is feasible in predictive maintenance in the Industry 4.0 environment. The study makes available a repeatable framework and visualization modal in real-time monitoring of conditions in smart manufacturing. Additional enhancements can include adaptive model of learning, cross-platform implementation and NLP-based operator suggestions in the future.
DOI: 10.25673/123739
Language: English
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7163682011