Industrial Automation Code Analysis for Rule-Based Project Validation
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Solution Overview
Problem
Designing and troubleshooting industrial automation systems is inefficient due to the lack of design guidelines, compatibility checks, and naming conventions, leading to human error and resource-intensive manual updates, which often requires specialized expertise and external contractors for maintenance.
Innovation Solution
Implementing AI and machine learning to enforce design rules, suggest compatible components and connections, automate naming conventions, and provide a light engineering client environment for operators to make minor adjustments, thereby reducing human error and enhancing system maintainability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If designers manually review previous designs and specifications to ensure compatibility, then design accuracy is improved, but design time and productivity deteriorate
Solution Approach 1:
The system performs compatibility checks, naming convention validations, and connection verifications automatically during the design process rather than requiring manual review later. The analysis engine continuously monitors design actions and provides real-time feedback, preventing incompatible configurations before they cause problems.
Solution Approach 2:
Manual design review and troubleshooting processes are replaced with an automated code analysis system that uses AI/ML models to analyze project code, detect issues, and suggest resolutions. The system automatically parses code files, builds dependency graphs, and identifies potential problems without human intervention.
2Stability of the object's composition
If designers manually update component names according to naming conventions, then naming consistency is improved, but time consumption and complexity increase
Solution Approach 1:
The code analysis system automatically detects naming convention violations and suggests corrections without requiring manual intervention. When components are added, removed, or relocated, the system automatically analyzes the impact on naming conventions and provides targeted suggestions for maintaining consistency, eliminating the need for designers to manually update each component name.
Solution Approach 2:
The system continuously monitors component names against defined naming conventions and provides real-time feedback when violations occur. The analysis engine tracks naming patterns across the project and alerts designers to inconsistencies, enabling automatic or semi-automatic correction of naming issues.
3Adaptability or versatility
If designers use incompatible objects or invalid connections without warnings, then design flexibility is improved, but system reliability deteriorates
Solution Approach 1:
The system performs preliminary compatibility analysis between objects and connections before they are finalized in the design. The code analysis engine detects potential compatibility issues, invalid connections, and configuration errors early in the design process and provides warnings or errors to prevent problematic configurations from being implemented.
Solution Approach 2:
An intermediary analysis layer is introduced between the designer and the final system configuration. The code analysis system acts as a mediator that validates design decisions, checks object compatibility, and verifies connection validity without restricting design freedom. The system provides guided feedback that helps designers make reliable choices while maintaining flexibility.
4Measurement precision
If specialized designers are required for system maintenance, then troubleshooting accuracy is improved, but operational continuity deteriorates
Solution Approach 1:
The code analysis system enables operators to perform self-diagnosis and troubleshooting of automation systems without requiring specialized designers. The system automatically analyzes project code, identifies issues, and provides actionable recommendations that empower operators to resolve problems independently, maintaining operational continuity while preserving troubleshooting accuracy.
Solution Approach 2:
The need for specialized designer intervention is replaced with an automated troubleshooting system that continuously monitors system configuration, analyzes code for errors, and provides diagnostic recommendations. The AI/ML-powered analysis engine detects and diagnoses issues automatically, enabling operators to maintain systems without specialized expertise.
Data Source
AI summary
A system includes a processor and a memory accessible by the processor. The memory stores instructions that, when executed by the processor, cause the processor to receive an industrial automation project code file, wherein the industrial automation project code file defines one or more operations of an industrial automation system during performance of an industrial automation process, retrieve a set of industrial automation rules associated with a set of best practices for project code files, analyze the industrial automation project code file based on the set of industrial automation rules, including identifying one or more instances of inefficient tag usage, and identifying one or more sets of parallel overlapping tasks, and generate a report based the analysis of the industrial automation project code file based on the set of industrial automation rules.