Innovation Center Prof. Dr.-Ing. Clemens Gühmann

Electronic measurement and diagnostic technology

The Innovation Center Electronic Measurement and Diagnostics deals with practical problems in mechatronics, especially in automotive and railroad engineering. Methods of modeling, signal processing and pattern recognition (machine learning) are researched and applied to increase the availability or quality of mechatronic systems. One focus is on the application of modern, AI-based methods for system diagnosis and for estimating the remaining service life of mechatronic systems, making predictive maintenance possible.

portrait photo of Prof. Dr.-Ing. Clemens Gühmann


Prof. Dr.-Ing. Clemens Gühmann

Berlin University of Technology
Institute for Energy and Automation Technology
Department of Electronic Measurement and Diagnostics
Einsteinufer 17
10587 Berlin

Short Vita

Professional background:

  • since 2003 Professorship in the field of "Electronic Measurement and Diagnostic Technology" in the Faculty IV of Electrical Engineering and Computer Science at the Technical University of Berlin
  • 1996 - 2003 Development engineer at Ingenieurgesellschaft Auto und Verkehr - IAV GmbH, most recently head of department in the transmission systems division
  • 1994 - 1995 Development engineer in the Whirlpool Group (Bauknecht)
  • 1995 Doctorate - Diagnosis of electrical machines
  • 1989 - 1994 Scientific assistant at the Institute for General Electrical Engineering (Measurement Technology Department) at the Technical University of Berlin
  • 1989 Diploma in Electrical Engineering at the Technical University of Berlin, majoring in measurement and control technology

Research and development

  • Modern methods of measurement data processing e.g. denoising with wavelets, spectral analysis, digital filtering or feature finding in MATLAB and Python.
  • Modeling of mechatronic systems for diagnosis for the fault-free and faulty case- fault modeling.
  • Application and further development of neural networks for fault diagnosis with physical a priori knowledge.
  • Identification of nonlinear systems
  • Development of virtual sensors


  • Development of data-based diagnostic systems with statistical methods and neural networks
  • Development of physics-based diagnostic systems
  • Prediction models for estimating the remaining service life of mechatronic systems
  • Concepts for measurement data acquisition - from the sensor to the processing of signals
  • Development and application of modern methods of digital measurement data processing
  • Modeling and simulation of mechatronic systems with Modelica, MapleSim and Simulink

Reference projects

  • BerDiBa - Berlin Digital Railway Operation. Focuses: Simulation of train operation, prediction of maintenance needs and adaptive state models for infrastructure components - vegetation and clearance profile.
  • SiCWell - Influence of SiC inverters on the lifetime of traction batteries, BMBF
  • Lifetime and reliability models for automotive friction clutches for control and regulation, IAV.
  • StreetProbe - Cooperative cloud-based road condition monitoring, BMWi
  • Bearing arrangement in rotating systems (LAROS), condition monitoring of plain bearings, Rolls-Royce
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