Integration of Physical and Probabilistic Measures in Stochastic Measurements of Manufacturing Processes

Deterministic and probabilistic models of measured quantities, processes, and fields in production process control systems, as well as physical and probabilistic measures, enable the formation of measurement results and confer them the properties of objectivity and reliability. The issue of improving and developing models and measures in measurement methodology plays an increasingly important role in achieving high measurement accuracy in control systems and the reliability of decision-making by expert systems in production processes. The measurement result is formed by many factors, most of which are random in nature. The stochastic approach in measurement theory is particularly important for the measurement of probabilistic physical quantities and for the construction of decision rules for expert systems. Probabilistic measures play a key role in both the measurement of physical quantities and the construction of decision rules when using a stochastic approach. The main contribution of this paper is a measure-centred formulation of stochastic measurement and decision support, in which physical and probabilistic measures are treated as an explicit intermediate layer between the model and the algorithm. This is not presented as a new entropy or distance metric, but as a methodological integration that clarifies uncertainty handling, improves traceability of measurement results, and supports decision rules for production-process monitoring. The approach is illustrated on air-quality monitoring data from a real control system.