Legacy Infrastructure Digital Transformation: Analog gauge reading and data assetization with Vision AI

Many large manufacturing plants and energy facilities still operate analog instruments that cannot be connected directly to digital systems. That forces operators to enter hazardous zones and read values manually, increasing safety risk and making omission or transcription errors more likely. Because those environments also lack continuous real-time data collection, it is difficult to detect abnormal pressure changes or early equipment warning signs proactively.

Instead of replacing legacy analog equipment at high cost, the new approach applies high-performance Vision AI to read pointer gauges and instrument values in real time and convert them into digital signals. The system maintains stable text and pointer-recognition performance even in low light and heavy reflection, and extracted values are integrated into a single time-series database through a unified data pipeline. Threshold conditions can then trigger automatic escalation and follow-up workflows.

By automating manual reading work, the project reduced operating effort in hazardous inspection routes by more than 85% and pushed subjective reading errors close to zero. At the same time, single-point analog readings were converted into continuous digital datasets that support real-time monitoring and predictive maintenance. This became a practical digital-transformation model for upgrading legacy facilities at reasonable cost while strengthening manufacturing competitiveness.