Our Research Group

The Reliability Engineering & Prognostics and Health Management (PHM)Research Group is headed by Prof. Zeiler and deals with issues relating to the failure mode and the service life of technical systems. It addresses elements of classical reliability engineering such as statistical reliability assurance with accelerated service life tests, as well as topics from the relatively new research field of PHM. This research field also encompasses the individual service life prognosis of a technical system with of machine learning methods.

These topics are to be found in our research and also in our teaching. The Research Group has several publicly funded projects. It also supervises doctoral projects in collaboration with the University of Stuttgart. Furthermore, great importance is attached to knowledge transfer into industry. This is achieved by undertaking research projects with partner companies and also by incorporating our current research topics into the courses we offer our students. Our Research Group provides lectures and the accompanying lab. work in addition to regular student projects and projects for final theses.

Our Research Group

The main fields of research relating to reliability engineering are:

  • Reliability under operational conditions
  • Modelling and simulation of the reliability and availability of complex systems
  • Reliability models and methods


The main fields of research of the Research Group relating to PHM are:

  • Data-driven and hybrid methods of condition diagnosis and prognosis
  • Extension of data-driven methods through knowledge of the degradation process
  • Reduction in the quantity of training data needed through the use of Similar System Data/ Transfer Learning
  • Development and utilisation of ensemble models
  • Consideration of the uncertainties with data-driven diagnostic and prognostic methods
  • Methodology for selecting prognostic methods taking account of typical industry-specific constraints
  • Compilation of a database for publicly accessible sets of degradation data
  • Analysis of high-frequency drive data of machine tools for condition diagnosis
  • Vibration analysis of drive components


A detailed description can be found in Main research topics and fields.

The Reliability Engineering & Prognostics and Health Management Research Group has modern offices and several test stands, which are used for both research and teaching. The test stands available can be used to analyse vibrations (structure-borne sound) to identify different types of damage to roller bearings, for example. The Research Group has also built a degradation test stand for the rapid and flexible generation of degradation data which are needed particularly to develop prognostic methods using machine learning methods.

The Research Group has access to a wide range of powerful hardware and to subject-specific software licences to help it carry out its research and teaching activities. We have several Developer PCs at our disposal, and for tasks requiring considerable computational resources, such as those involving data analysis and artificial intelligence, we can fall back on a computer cluster. To analyse substantial quantities of data, we can use commercial software which is widely used in industry. It can be used for data analysis or the statistical design of experiments (DoE) and also for the reliability assurance of technical systems, for example. Furthermore, the Research Group also utilises widely used Open Source Software (e.g. Python with its associated libraries). The Research Group has access to:

  • Matlab with comprehensive Toolboxes including a Parallel Server Licence
  • Established reliability software for calculations and analyses (statistical test planning, data analysis, simulation, FMEA, Markov, system calculations, fault tree analysis)
  • Software for modelling and simulating complex systems

to name but a few.


Interested? Apply! for the Wintersemester 2023/24