Factory Process Monitoring for Deep Learning Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Factory processes and control systems are vulnerable to sophisticated malware attacks that can evade conventional IT security solutions, leading to subtle yet catastrophic disruptions in equipment operation and output quality, necessitating a dynamic monitoring mechanism to detect anomalous activity before damage occurs.
Innovation Solution
A deep learning processor is employed to receive control signals from factory processes and equipment systems, generate expected response data, and compare it with actual production data to detect anomalies, initiating alert protocols or shutting down processes as necessary to address detected issues, including determining the type and confidence level of anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional IT security solutions and process control systems are used to monitor factory operations, then basic security coverage is provided, but subtle malware attacks that evade detection cannot be identified in time
Solution Approach 1:
An intermediary deep learning processor is introduced between the control signals and the monitoring system. This processor analyzes the behavioral patterns of control signals and production responses, detecting subtle anomalies that conventional systems miss while maintaining system architecture simplicity through a modular addition rather than complete system replacement
Solution Approach 2:
The patent replaces conventional rule-based IT security solutions and process control systems with an AI-based deep learning processor. This substitution enables the system to detect subtle malware attacks through pattern recognition and behavioral analysis rather than relying on predefined security rules, significantly improving detection precision
2Reliability
If deep learning processors and AI-based monitoring are implemented to detect subtle anomalies, then detection precision improves, but system complexity and computational requirements increase
Solution Approach 1:
The deep learning processor is trained in advance on normal operational patterns and malware attack patterns before deployment. This preliminary training enables the system to quickly and reliably detect anomalies during production without requiring complex real-time decision-making, improving reliability while managing system complexity through pre-computed knowledge
3Loss of time
If continuous monitoring of all control signals and production data is performed, then early detection of malware attacks is achieved, but data processing load and computational resources increase
Solution Approach 1:
The deep learning processor extracts and focuses only on the most critical features and behavioral patterns from the continuous stream of control signals and production data. By taking out and analyzing only the essential elements rather than processing all raw data, the system achieves early malware detection while significantly reducing computational energy requirements
Data Source
AI summary
A system including a deep learning processor receives one or more control signals from one or more of a factory's process, equipment and control (P/E/C) systems during a manufacturing process. The processor generates expected response data and expected behavioral pattern data for the control signals. The processor receives production response data from the one or more of the factory's P/E/C systems and generates production behavioral pattern data for the production response data. The process compares at least one of: the production response data to the expected response data, and the production behavioral pattern data to the expected behavioral pattern data to detect anomalous activity. As a result of detecting anomalous activity, the processor performs one or more operations to provide notice or cause one or more of the factory's P/E/C systems to address the anomalous activity.