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Talk · Asia Conference on Cognitive Engineering and Intelligent Interaction · December 1, 2025

Detection of Industrial Process Anomalies Using Isolation Forest and DBSCAN in a Real Manufacturing Environment

Shyrailym Adilkyzy, Amirkhanova Gulshat, Liya Erbolkyzy Bektemir, Bauyrzhan Amirkhanov, Tomiris Nurgazy, Alikhan Amirkhanov

Liya Erbolkyzy Bektemir
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Abstract

Modern manufacturing requires robust methods for monitoring complex industrial systems to ensure operational efficiency and safety. This paper presents a comparative analysis of two distinct unsupervised machine learning algorithms—Isolation Forest and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)—for anomaly detection in industrial environments. We evaluate their performance in detecting faults and defining operational states using real-world industrial datasets. The study leverages a data-driven strategy to address issues such as fault monitoring and process optimization. Results indicate that while Isolation Forest is highly effective for identifying outliers in high-dimensional data (Accuracy 0.91), DBSCAN provides superior interpretability for clustering distinct operational modes and defining signal segmentation. This research contributes to the broader implementation of Digital Twins and Industry 4.0 standards.

Anomaly Detection Techniques and ApplicationsFault Detection and Control SystemsIndustrial Vision Systems and Defect DetectionIsolation ForestDBSCANunsupervised anomaly detection

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