Intelligent Anomaly Detection in Industrial Networks: A Predictive Evaluation of the SmartSense AI Architecture

Kommu Tharun, V.Vijayalakshmi, Dr.S.Usharani

Predictive Maintenance; Anomaly Detection; Autoencoder; Isolation Forest; Industrial IoT.

The rapid advancement of industrial automation and the increasing deployment of sensor-based monitoring systems have enabled data-driven approaches for intelligent equipment maintenance. This paper presents SmartSense AI, an industrial predictive anomaly detection framework designed to identify abnormal patterns in machine sensor data using advanced machine learning techniques. Traditional maintenance strategies, including reactive and preventive maintenance, often fail to detect early-stage equipment degradation, leading to unexpected failures and increased operational costs. The proposed system utilizes unsupervised learning methods, specifically an autoencoder-based deep learning model and the Isolation Forest algorithm, to analyze multivariate time-series sensor data. The autoencoder learns the normal behavior of machine operations and identifies anomalies based on reconstruction error, while the Isolation Forest detects statistically isolated data points. A dual-model approach enhances detection reliability and reduces false positives. Additionally, the system incorporates a severity scoring mechanism to quantify anomaly intensity and a root cause analysis module to identify contributing sensor variables. An interactive Streamlit-based dashboard enables real-time visualization of anomaly detection results, severity levels, and sensor insights. The system is implemented using Python, TensorFlow, and Scikit-learn, ensuring scalability and efficiency. Experimental evaluation demonstrates that SmartSense AI effectively detects early anomalies, reducing unplanned downtime and maintenance costs. The modular architecture supports future enhancements such as IoT integration, cloud deployment, and predictive failure estimation.

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