Dietrich Brunn

The mission is to improve the complete measurement chain of vibration-based Condition Monitoring. This includes piezo-based accelerometers, signal processing, post-processing, interactive visualization, data management, and the failure analysis of rotating machines. In addition to Python, the main tools employed are methods from signal processing, time-frequency and time-series analysis, as well as techniques from machine learning, mechanical engineering, and metrology.

As part of the "Research and Mathematical Methods" group, the responsibilities involved enhancing measurement devices used for aligning rotating shafts and ring laser gyroscopes used for roll alignment, utilizing new technologies. The primary focus encompassed metrological characterizations, calibration methods, and mathematical modeling within these domains.

My field of research was stochastic information processing focusing on Bayesian state estimation for non-linear and distributed systems. Besides participating in a publicly funded industry project for developing measurement devices for multi-ax CNC machines, I supervised the lecture "Lokalisierung mobiler Agenten" (Localization of mobile agents) as well as numerous student research projects and diploma theses.