Industrial Control Logic Anomaly Resolution Using LLM Feedback
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Solution Overview
Problem
Industrial automation environments face inefficiencies and resource-intensive challenges in identifying and resolving anomalies caused by flawed control logic, which can lead to negative effects on efficiency, profitability, and safety.
Innovation Solution
An automated anomaly detection and resolution system that uses a central anomaly index to identify and generate tailored solutions for anomalies by comparing target anomalies with reference anomalies, deploying modified control logic to industrial controllers to rectify issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to identify and resolve anomalies in control logic, then solutions can be developed to remedy anomalies, but the process is tedious and resource intensive requiring multiple rounds of revision and observation
Solution Approach 1:
The system performs preliminary analysis of control logic to detect anomalies before they cause significant disruptions. By proactively identifying anomalies through automated monitoring of device operations and control logic execution, the system prepares solutions in advance, reducing the time required for resolution and eliminating the need for multiple revision rounds.
Solution Approach 2:
The system implements continuous feedback loops where anomaly detection results automatically trigger solution generation and deployment processes. The monitoring system feeds anomaly data back to the resolution engine, which generates corrected control logic and deploys it immediately. This closed-loop feedback mechanism eliminates manual intervention cycles and achieves rapid anomaly resolution.
2Difficulty of detecting and measuring
If manual analysis of control logic codebase is performed to detect anomalies, then anomalies can be identified, but the process is resource intensive and duplicative effort is spent on similar anomalies
Solution Approach 1:
The system creates digital replicas or models of control logic behavior that can be analyzed without disrupting actual operations. By copying control logic into a virtual environment for analysis, the system can detect anomalies through simulation and testing, reducing the need for complex manual code analysis and eliminating duplicative effort on similar anomaly patterns.
Solution Approach 2:
The system implements a universal anomaly detection framework that can analyze multiple types of control logic across different devices and applications using the same core engine. This multi-functional approach eliminates the need for separate detection systems for each device type, reducing overall system complexity while maintaining comprehensive anomaly detection capability.
3Reliability
If multiple rounds of revision and observation are performed to debug anomaly solutions, then solutions can be refined, but resource and labor costs are compounded
Solution Approach 1:
The system performs preliminary validation and testing of anomaly solutions before deployment by simulating their effect in a virtual environment. This pre-validation step ensures solution accuracy is verified in advance, eliminating the need for multiple post-deployment revision rounds and maintaining high solution reliability while accelerating the resolution process.
Solution Approach 2:
The system implements self-service debugging capabilities where the anomaly detection engine automatically generates, tests, and deploys corrected control logic without requiring manual intervention for each revision cycle. The system self-corrects by comparing detected anomalies against a knowledge base of known issues and automatically applies proven solutions, maintaining accuracy while dramatically reducing resolution time and resource costs.
Data Source
AI summary
Disclosed herein are methods and systems for automated anomaly detection and resolution for industrial device function. Where anomalies are detected, anomaly information is collected, including an anomaly description, an anomaly context, and a code block source of the anomaly. Anomaly information is used in generating prompts to be sent to a generative large language model (LLM) trained on anomaly data. The LLM is configured to accept a prompt containing anomaly information and return a reference anomaly solution selected for its similarity to the target anomaly. The reference anomaly solution, containing control logic that governs industrial device function, is tailored for the target anomaly. The tailored reference anomaly solution, now acting as the target anomaly solution, is deployed to the industrial controller associated with the industrial device experiencing anomalous function. The control logic of the target anomaly solution replaces existing control logic, thereby eliminating the anomaly.