In his doctoral thesis, Maik Wolf developed a smart measurement concept for recording and analysing the condition-describing vibrations of tram drive elements
On 26 May 2025, Dr Maik Wolf successfully defended his doctoral thesis on "Smart vibration diagnosis on drive elements of trams for condition monitoring" at Chemnitz University of Technology under the supervision of Prof. Dr Ralf Werner. Olfa Kanoun, Chair of Measurement and Sensor Technology, Chemnitz University of Technology and Prof Dr Mathias Rudolph, Chair of Industrial Measurement Technology, HTWK Leipzig.
The dissertation
In his dissertation, Dr Maik Wolf dealt with the question of how the drive elements of trams can be monitored by means of inline vibration diagnostics in order to optimise their maintenance in the long term. As trams are one of the most environmentally friendly forms of local public transport and are experiencing increasing utilisation, new approaches to recording their technical condition must be pursued in order to continue to ensure safe and punctual operation.
As a research assistant in Prof Rudolph's team at the Faculty of Engineering at HTWK Leipzig, Dr Maik Wolf has dedicated himself to this topic as part of his research work together with local industry players and Leipzig's public transport operators as an associated partner.
Research for reliable tram operation
Trams are an integral part of Leipzig's constantly growing transport infrastructure. For the safe operation of rail-bound local public transport, the maintenance of, for example, engines, gearboxes, roller bearings and wheels at cyclical intervals is the current state of the art. As vehicle and infrastructure utilisation increases, the maintenance strategy must be able to adapt and react flexibly to unforeseen faults. To achieve this goal, Wolf has developed a smart measurement concept that consists of energy self-sufficient sensor systems based on energy harvesting - i.e. the harvesting of ambient energy to supply the sensor systems with electricity - and classifies vibration measurement data from the drive elements using machine learning with regard to the need for maintenance.
On the basis of a large measurement database, Wolf was able to show what the determining influencing factors are on the vibration level of the tram drive elements. The resulting measurement concept is based on a wireless sensor network that is jointly capable of recording the variables that describe the condition. These include, for example, the non-invasive estimation of the speed based on vibration measurement data and the detection of disruptive rail influences.
For wireless application and reliable, energy-autonomous sensor system operation, a simulation approach was also developed during the development phase in order to estimate the recoverable ambient energy before the sensor system is used, even under the strong influence of variable tram operation.
Finally, the recorded vibration measurement data provides "patterns" that allow the drive elements to be classified using statistical methods and machine learning processes. The technical condition recorded in this way, e.g. of a tram in need of maintenance as a result of otherwise undetected component damage, allows recommendations for action to be derived for condition-based maintenance measures.
A continuation of the research work at the HTWK Leipzig is planned.
Congratulations
The Faculty of Engineering congratulates Dr Maik Wolf on the successful completion of his dissertation and wishes him all the best for his future career and life as well as continued success in his work at HTWK Leipzig.
