Predictive Maintenance Applications | Developing a Framework to Classify Industrial Aspects using Text Mining Methods
- Date:ab sofort
Prof. Dr. Hansjörg Fromm (KIT KSRI) und Yannic Wolf (TU München)
Predictive Maintenance (PdM) is a real-world industrial AI application, offering great potential for use. Additional to AI techniques, it demands a holistic embedding into industrial service systems. Especially in manufacturing systems, there are various best practices from researchers and practitioners. Through digitalization, the establishment of connected products allows an expansion of smart maintenance services escorting physical products. Vehicle applications are one example of a fast-growing aspect of smart maintenance services containing huge value potentials for customers and the automotive industry. A structure framework for PdM applications, especially focusing on specifics of product-service-systems, should provide an overview and decision support for researchers and practitioners.
This thesis aims at pursuing a categorization and classification of PdM areas of application. Using text mining methods, a literature data set should distinguish PdM applications according to their requirements and methods. Furthermore, topics and trends should be explored. In addition to the literature data set, a direct contact to industrial PdM development for vehicle applications is supplied for interviews and support. By working on this thesis you get the chance to build and develop useful text mining skills as well as getting an in-depth insight into real-world AI applications in the automotive industry.
- Interest in industrial AI research and applications
- Interest in working alongside with researchers and towards scientific publications
- Basic experience with python, ideally text mining techniques
- High motivation to work on interesting real-world problems
- Self-organized and goal-oriented working mode, including the motivation to bring own ideas
We offer you a challenging research topic, close supervision, and the opportunity to develop practical and theoretical skills. If you are interested, please send an email to Yannic Wolf (firstname.lastname@example.org) with cc: email@example.com along with a transcript of records and CV.