Maximilian Benker

PhD student at Technical University of Munich (TUM)


I am an engineer currently pursuing a PhD in the field of manufacturing at the Institute for Machine Tools and Industrial Management of the Technical University of Munich (TUM). My research is about condition moitoring of machine tool feed drives and methods for the estimation of remaining useful life (RUL). Check out my publications for details. Prior to the PhD program at TUM I studied industrial engineering and management at Technical University of Berlin and did interships in the automotive and consumer goods industry. My research interests include manufacturing, condition monitoring of machine tools, applied Bayesian methods, applied machine learning and data analytics.

Opinions are my own and not the views of my employer.


Jan 11, 2023 New publication: Automatic modal analysis using Bayesian optimization.
Oct 31, 2022 Hello World! My first personal website is drafted.

Selected publications

  1. Condition Monitoring of Ball Screw Feed Drives Using Convolutional Neural Networks
    Maximilian Benker, and Michael F. Zaeh
    CIRP Annals 2022
  2. A Gaussian Process Based Method for Data-Efficient Remaining Useful Life Estimation
    Maximilian Benker, Artem Bliznyuk, and Michael F. Zaeh
    IEEE Access 2021
  3. Utilizing Uncertainty Information in Remaining Useful Life Estimation via Bayesian Neural Networks and Hamiltonian Monte Carlo
    Maximilian Benker, Lukas Furtner, Thomas Semm, and 1 more author
    Journal of Manufacturing Systems 2021