Streamlining Engineering Data to Drive Digital Twin Accuracy for a European Energy Major

“Without automated quality control, ensuring consistency remains a manual, time-intensive process.”

Background and Business Challenges

Solution and Benefits

When Data Mismatches Create Digital Twin Challenges

Digital Twins rely on accurate, synchronized data to provide reliable insights for monitoring, simulation, and optimization. However, discrepancies between engineering data sources often lead to inconsistencies, reducing confidence in the system.

Piping and Instrumentation Diagrams (P&ID) serve as the master reference for engineering schematics, but variations in data across applications create misalignment. Without automated quality control, ensuring consistency remains a manual, time-intensive process. As a result, organizations struggle with trust issues in Digital Twin adoption, limiting operational efficiency and decision making.

Ensuring Digital Twin Accuracy With
AI-driven Data Validation

Infosys developed an AI-powered solution leveraging computer vision to extract, interpret, and validate engineering data from P&ID diagrams. By identifying engineering symbols, mapping extracted content, and cross-referencing it with Digital Twin applications, the system detects mismatches and inconsistencies with precision.

This automated approach eliminates the need for labor-intensive quality control, ensuring that Digital Twin data remains accurate, up-to-date, and aligned with real-world systems.

Benefits:

Increased Productivity: Automates P&ID content extraction, reducing manual effort.
Enhanced Quality Control: Identifies mismatches and discrepancies, improving data integrity.
Greater Confidence in Digital Twins: Ensures data synchronization, strengthening trust in decision making.