ADAPTIVE UNCERTAINTY REDUCTION IN MULTICHANNEL INFORMATION-MEASUREMENT SYSTEMS FOR INDUSTRIAL PROCESS MONITORING

Abstract

The paper addresses the problem of uncertainty reduction in multichannel information-measurement systems for industrial process monitoring. A mathematically substantiated algorithmic approach is proposed for minimizing the integral uncertainty of an aggregated estimate based on covariance analysis and adaptive redistribution of measurement channel weights. An algorithm for real-time implementation of the proposed approach has been developed, which can be integrated into the software layer of existing systems without modifications to their hardware architecture.

The effectiveness of the proposed approach was evaluated through simulation-analytical modeling using synthetic data corresponding to typical metrological characteristics of sensors. The results demonstrate a significant reduction in standard uncertainty and root-mean-square error compared to conventional uniform weighting. The developed approach can be applied in PLC/SCADA and edge solutions to enhance measurement reliability and ensure system robustness against degradation of individual channels.