Distributed Ledger Self-Testing for Industrial Anomaly Detection
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Solution Overview
Problem
Current anomaly detection systems in industrial automation technology are reactive, prone to false positives, lack transparency, and are centrally managed, leading to inefficiencies and increased risk of failures due to their inability to proactively address anomalies and prevent unauthorized changes or attacks.
Innovation Solution
A decentralized automation technology system utilizing a distributed ledger database with participant nodes that autonomously check for rule violations, allowing dynamic negotiation of rules and incorporating AI-based algorithms for anomaly detection, enabling proactive and reliable anomaly detection without a central unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a centralized anomaly detection system is used, then management and control are simplified, but the system becomes vulnerable to single points of failure and lacks transparency
Solution Approach 1:
The patent divides the centralized anomaly detection system into multiple distributed participant nodes that each independently execute anomaly detection. This segmentation eliminates the single point of failure risk while maintaining coordinated system-wide monitoring through the distributed ledger that records all participant actions and states.
2Measurement precision
If historical data and pattern recognition are used for anomaly detection, then the system can identify abnormal behavior, but the large variance in state space leads to numerous false positives
Solution Approach 1:
The patent implements a feedback mechanism where detected anomalies and their contexts are recorded in the distributed ledger and used to continuously refine the rule sets and AI algorithms. This feedback loop enables the system to learn from false positives and improve detection precision over time while adapting to the specific characteristics of each participant node.
3Productivity
If current reactive anomaly response measures are implemented, then responses can be executed after anomalies are detected, but no preventive action is taken and operator burden increases
Solution Approach 1:
The patent implements preliminary action by continuously monitoring participant nodes against predefined rule sets and AI-based anomaly detection algorithms before critical failures occur. The system proactively identifies and responds to early signs of anomalies, preventing issues rather than reacting to them, thereby reducing operator burden and response time.
4Ease of operation
If centralized anomaly control systems are used, then coordination is simplified, but participants cannot dynamically define or negotiate their properties and relationships
Solution Approach 1:
The patent implements dynamics by allowing participant nodes to dynamically define and negotiate their properties, relationships, and rule sets through the distributed ledger. Each participant can adapt its behavior and parameters in real-time based on system conditions and interactions, while the distributed consensus mechanism maintains coordinated control across the network.
Data Source
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AI summary
The invention relates to a self-testing automation system comprising: - a decentralized distributed ledger-type database (DB), in particular a blockchain, comprising a plurality of subscriber nodes (TK1,…, TK6), wherein the subscriber nodes (TK1,..., TK6) are designed to exchange data, or information, with one another per transaction (TA), and the database (DB) is designed to store the transactions (TA), in particular in data blocks (BL1, BL2, BL3) which are linked together; - a regulating mechanism (RG) which is implemented into each of the subscriber nodes (TK1,..., TK6), said regulating mechanism (RG) comprising information on the number and identity of all of the subscriber nodes (TK1,..., TK6) as well as rules relating to actions, properties, and states of each of the subscriber nodes (TK1,..., TK6); and - a plurality of automation components (AK1,..., AK4) which are subscriber nodes (TK1,…, TK6) of the decentralized database (DB). Each of the subscriber nodes (TK1,…, TK6) is designed to test or validate transactions (TA) between the subscriber nodes (TK1,…, TK6) at all times using the regulating mechanism (RG), and each of the subscriber nodes (TK1,…, TK6) is designed to carry out at least one measure if a violation of the regulating mechanism (RG) is detected.