This article examines the practical implementation of a methodology based on Bayes' theorem in the field of technical diagnostics and residual life forecasting for industrial equipment. Emphasis is placed on the ability of this approach to support engineers' effective work under the uncertainty inherent in real-world production processes. Using a vibration monitoring system for multi-axis milling machines, which are critical for the aerospace industry in the production of high-precision aluminum aircraft components, as an example, the feasibility of quantitatively updating the probability of failures as new sensor data arrives is demonstrated. Initial signals, such as vibration, temperature, or acoustic emission levels, are transformed into probabilistic risk assessments with practical justification, providing a reliable basis for management decision-making.

 

Keywords: "Technical diagnostics, residual life forecasting, predictive maintenance, engineering systems, decision theory, aircraft engineering, repair economics, uncertainty management, probabilistic models, monitoring systems, adaptive algorithms