Multi-Agent Systems for Anomaly Detection and Quality Control in Industrial Environments: A Comprehensive Review
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Abstract
The rapid adoption of Industry 4.0 technologies has transformed traditional manufacturing systems into highly interconnected and data-driven environments. Modern production facilities generate large volumes of sensor, machine, and operational data, creating opportunities for intelligent quality control and predictive decision-making.[8],[10],[39] However, conventional quality assurance techniques often struggle to process such heterogeneous data streams in real time. Consequently, anomaly detection has emerged as a critical research area for identifying defects, process deviations, and equipment failures before they impact production outcomes. Simultaneously, Multi-Agent Systems (MAS) have gained significant attention due to their decentralized architecture, scalability, adaptability, and fault-tolerant characteristics. [3],[5], [38]
This literature review examines the evolution of anomaly detection techniques and their integration with MAS for industrial quality control applications. The review covers statistical methods, machine learning algorithms, deep learning approaches, sensor fusion strategies, edge-cloud computing architectures, and contemporary agent frameworks. Furthermore, the paper analyzes current challenges related to real-time performance, interoperability, scalability, and deployment in industrial environments. Existing research demonstrates that while anomaly detection models have achieved remarkable improvements in accuracy, their integration into distributed industrial systems remains limited. Multi-agent architectures offer a promising solution by enabling autonomous monitoring, localized decision-making, and collaborative intelligence across manufacturing environments. Finally, research gaps and future directions are identified to support the development of intelligent, resilient, and scalable industrial quality-control systems.
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