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Project Overview

Project Code: ED 06

Project name:

AI-based Condition Monitoring for Sensor-integrated Gears

TUM Department:

ED - Mechanical Engineering

TUM Chair / Institute:

Institute of Automation and Information Systems

Research area:

Condition Monitoring, ML, IIoT, Embedded Systems

Student background:

Computer EngineeringComputer ScienceComputer Science/ InformaticsElectrical EngineeringMechanical Engineering

Further disciplines:

Participation also possible online only:

Planned project location:

Institute of Automation and Information Systems, Boltzmannstraße 15, 85748 Garching near Munich

Project Supervisor - Contact Details


Title:

Given name:

Cedric

Family name:

Wagner

E-mail:

cedric.wagner@tum.de

Phone:

+49 89 289 16446

Additional Project Supervisor - Contact Details


Title:

Given name:

Dominik

Family name:

Hujo-Lauer

E-mail:

dominik.hujo@tum.de

Phone:

+49 89 289 16451

Additional Project Supervisor - Contact Details


Title:

Prof. Dr.-Ing.

Given name:

Birgit

Family name:

Vogel-Heuser

E-mail:

vogel-heuser@tum.de

Phone:

+49 89 289 16400

Project Description


Project description:

In our team you can contribute to the research of ultra-low-power embedded systems integrated into gears for condition monitoring and damage detection based on machine-learning. Functional prototypes have already demonstrated the capability to detect specific gear anomalies, providing the foundation and research environment for further experimental development, algorithm refinement, and system optimization.

Within this research project you can engage in the following tasks, working at the intersection of hardware, AI, and industrial applicability:
- Development of highly modular and reconfigurable (embedded) software for sensor-integrating machine elements
- Optimization of data processing and machine learning algorithms to improve damage classification capabilities while reducing overall power consumption
- Research and validation of operation strategies for resource constrain energy harvesting (IIoT) devices

Working hours per week planned:

37

Prerequisites


Required study level minimum (at time of TUM PREP project start):

2 years of bachelor studies completed

Subject related:

Strong interest and experience with embedded programming in C (Rasberry Pi, Arduino, ESP32, …), machine-learning or model-based systems engineering beneficial

Other:

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