2026 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD 2026)

Drilling equipment health monitoring requires vibration signals, operating context, and data-quality information to remain synchronized across surface and downhole systems that are often supplied by different vendors. This paper develops an OPC Unified Architecture based sensing and edge-analytics framework that converts a heterogeneous drilling data backbone into a vibration-health information model. The architecture combines surface rotary speed, weight on bit, rate of penetration, pressure and flow with downhole rotational speed, toolface, and tri-axial vibration. High-rate waveform acquisition remains local, while quality-controlled features such as axial and lateral root-mean-square acceleration, kurtosis, band energy, shock count, and torsional-speed variation are published as standard OPC UA nodes together with timestamp quality, calibration state, packet loss, operating mode, and confidence. A context-conditioned normalization separates changes caused by operating conditions from persistent measurement or equipment anomalies, and the standardized feature layer supports rule-based diagnosis as well as machine-learning and cross-rig transfer models. A synthetic four-state study illustrates distinct feature signatures for normal drilling, stick-slip, bit bounce, and whirl without presenting the results as field validation. The framework therefore links advanced vibration sensing, multi-sensor data fusion, intelligent diagnosis, and interoperable deployment while preserving explicit boundaries between demonstrated communication architecture and proposed health analytics.
 

OPC Unified Architecture,vibration sensing,drilling automation,intelligent diagnosis,equipment health monitoring,edge analytics