DCS Troubleshooting Assistant for Correlating IT and OT Anomalies
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Solution Overview
Problem
Diagnosing software and hardware problems in large distributed systems is challenging due to the complex interplay of thousands of components, leading to delayed troubleshooting and poor user experience.
Innovation Solution
A DCS Software Troubleshooting Assistant that monitors DCS and automation equipment data for anomalies, correlates this data with production process data, and utilizes a large language model (LLM) for diagnostics and recommendations, providing a conversational user interface for refining the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If observability software focuses only on software component data (logs, metrics, traces), then software problem detection is improved, but hardware problem detection capability deteriorates
Solution Approach 1:
The patent creates a unified observability platform that handles both software and hardware monitoring through a single system. The system ingests diverse data types including logs, metrics, traces from software components, and sensor data, alarm data, maintenance records from hardware components, enabling one system to perform multiple diagnostic functions for both software and hardware problems
Solution Approach 2:
The patent introduces an intermediary layer consisting of unified data models and abstraction layers that translate heterogeneous hardware and software data into a common format. This intermediary enables the diagnostic engine to process both software logs and hardware sensor data through the same analysis mechanisms, bridging the gap between software-focused tools and hardware monitoring needs
2Measurement precision
If manual correlation of IT and OT data is performed, then diagnostic accuracy is improved, but troubleshooting time increases
Solution Approach 1:
The patent merges IT (information technology) and OT (operational technology) data streams into a unified data model that automatically correlates software logs with hardware sensor data, alarm data, and maintenance records. This combination eliminates the need for manual data correlation while maintaining comprehensive diagnostic accuracy through automated multi-source data integration and analysis
Solution Approach 2:
The system performs self-service automated diagnostics by automatically ingesting, correlating, and analyzing diverse data sources without requiring manual intervention. The diagnostic engine autonomously processes software and hardware data, generates insights, and provides recommendations, freeing operators from time-consuming manual data correlation tasks
3Loss of information
If comprehensive monitoring of thousands of components is implemented, then system observability is improved, but system complexity increases
Solution Approach 1:
The patent segments the monitoring system into distinct modular components: data ingestion modules for different data types, unified data models for structured representation, a diagnostic engine for analysis, and presentation layers for output. This segmentation allows comprehensive monitoring of thousands of components while managing complexity through organized, reusable modules that can be independently configured and maintained
Solution Approach 2:
The patent transforms unstructured and semi-structured data from thousands of components into structured parameters through unified data models. By converting diverse data formats into standardized parameters, the system achieves comprehensive observability while simplifying data processing and analysis through consistent parameter representations that can be uniformly handled by the diagnostic engine
Data Source
AI summary
A method for supporting troubleshooting software and hardware issues in a distributed control system (DCS) associated with an automation equipment in industrial plant includes monitoring data; detecting an anomaly in the monitored data based on predetermined anomaly detection rules; based on a result of the detecting, performing, for a detected anomaly, a similarity search on historic anomaly data associated with the DCS and/or the automation equipment; based on a result of the performed similarity search, querying a large language model (LLM) for diagnosis and/or recommendation for troubleshooting the detected anomaly; based on the querying, obtaining an output from the LLM, wherein the output is indicative of a diagnosis and/or recommendation for troubleshooting the detected anomaly; and providing the output to a user.