Real-Time Anomaly Detection in Industrial IoT Systems Using Hybrid AI Models
Abstract
Industrial IoT (IIoT) systems generate massive streams of data, making real-time anomaly detection critical for ensuring operational efficiency and safety. This paper proposes a hybrid AI model combining deep learning and statistical methods for detecting anomalies in IIoT systems. The deep learning component captures complex patterns in high-dimensional data, while statistical techniques provide robustness to noise and outliers. The model is tested on datasets from manufacturing and energy sectors, showcasing its ability to detect anomalies with high precision and recall. The findings highlight the potential of hybrid AI models to enhance predictive maintenance and reduce downtime in industrial settings.
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