AI-Driven Calibration Procedure Generation for Field Instruments Using Historical Logs

Abstract

Abstract Calibration of field instruments—such as pressure transmitters, flow meters, temperature sensors, and vibration sensors—is essential for accurate measurements and safe industrial operations. In practice, calibration steps are embedded within governed method statements for assets like PLC/BMS panels, BTU meters, compressor PRVs, and chiller pressure relief valves. Traditional static procedures often overlook sensor drift or operational context, leading to inefficient preventive maintenance. This paper presents an AI-driven framework that generates adaptive calibration procedures within method statements by combining historical sensor data analytics with semantic parsing of technical documentation. Drift patterns, anomalies, and hysteresis behaviors from sensor logs are mapped to relevant calibration steps, enabling modification of "Sequence of Work" and materials requirements. Reinforcement learning optimizes calibration sequences and intervals, while transformer-based NLP extracts procedural knowledge from OEM manuals. Evaluation on Aramco field data and synthetic datasets shows accurate drift detection, effective method statement adaptation, and improved procedural fidelity aligned with engineering practice. The framework supports multiple sensor types, integrates with digital maintenance platforms, and provides a scalable, governance-compliant solution for adaptive calibration and preventive maintenance.