Analysis of Operating Conditions and Failure Detection in Industrial PROFINET Networks

This paper presents a methodology for analyzing industrial PROFINET networks. It focuses on integrating automated topology reconstruction based on collected data and data preprocessing to estimate cumulative distribution functions and probability density functions for failures. Results establish a foundational framework for predictive maintenance through probabilistic analysis. The approach enables temporal error pattern visualization and supports statistical reliability threshold estimation. The work contributes to predictive maintenance in Industry 4.0 environments.