Thesis supervisor: Mátyás Andó
co-supervisor: Zoltán Pödör
Location of studies (in Hungarian): Szombathely, Budapest Abbreviation of location of studies: ELTE
Description of the research topic:
The aim of the research is to obtain useful information and new knowledge by studying sensors and sensor-systems in an industrial environment. Use science-based methods that focus on efficient and reliable storage and processing of data. One of the increasingly common failures of industrial equipment is when the sensor itself does not work properly. The critical issue is whether the sensor is not working properly (or the associated communication channel is faulty) or there is a problem with the basic manufacturing process. It is important to develop IT-based professional decision support that is able to recognize wrong sensors and provide artificial signals (instead of the sensors) until their fault is eliminated. In this case, a complete shutdown of the production process can be avoided. During the evaluation of the collected data, we are also looking for answer to whether the current location and function of the sensors justify their existence, also the provided data carries real, useful information for the system.
During the research and synthesis of the system, different kind of data analysis and programming tasks will be solved. The obtained results provide input data for system development and process optimization. As a result of the research, the functioning of the examined systems can be improved, and the productivity and throughput of the given industrial environment, i.e. the efficiency, can also be analysed and increased.
Current devices: Festo factory simulation models (different stations) with Festo or Siemens PLC
Applied technologies: CODESYS or TIA portal, SQL/No-SQL Database, R/Python (or other language)
Required language skills: angol Further requirements: The following skills are needed:
• Handling sensors
• PLC programing (Siemens or Festo)
• Communication channels implementation
• Databases
• Data processing, analysis and visualization
• Forecasting based on machine learning models
Number of students who can be accepted: 2
Deadline for application: 2024-05-31
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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