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Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
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Related Experiment Video

Updated: Dec 29, 2025

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Security in Process: Visually Supported Triage Analysis in Industrial Process Data.

Anna-Pia Lohfink, Simon D Duque Anton, Hans Dieter Schotten

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    Summary
    This summary is machine-generated.

    Cyber attacks on operation technology (OT) networks are increasing. This study presents a novel visualization system combining spiral plots and anomaly detection to identify these industrial cyber threats using sensor data.

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    Area of Science:

    • Cybersecurity
    • Industrial Control Systems
    • Data Visualization

    Background:

    • Operation technology (OT) networks, crucial for industrial processes, were once thought secure from cyber threats.
    • Recent cyber attacks demonstrate the vulnerability of OT networks, necessitating new security measures.
    • Traditional IT network anomaly detection methods are insufficient for OT environments due to unique data characteristics.

    Purpose of the Study:

    • To develop and evaluate a novel visualization system for detecting cyber attacks in industrial OT networks.
    • To address the specific challenges of anomaly detection in OT sensor data, considering its periodical nature.
    • To support both expert and non-expert users in analyzing and triaging potential security incidents.

    Main Methods:

    • Developed an interactive visualization system integrating spiral plots with anomaly detection algorithms.
    • Utilized inherent features of industrial process measurements for enhanced data insight.
    • Applied the system to real-world sensor and actuator data from a water treatment plant with simulated attacks.

    Main Results:

    • The visualization system effectively provides insights into OT network data, aiding in attack detection.
    • Demonstrated the system's capability to identify introduced attacks in a water treatment process dataset.
    • Analysis strategies for both laymen and experts were presented and exemplified.

    Conclusions:

    • The novel combination of spiral plots and anomaly detection offers a powerful approach for OT cybersecurity.
    • The developed system enhances the usability and effectiveness of attack detection in industrial environments.
    • The system's effectiveness and usability were validated through real-world data and expert evaluation.