Master Thesis Automated Sensor Recalibration & Failure Classification for Intelligent Sensor Networks

Company Description At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference. The Robert Bosch GmbH is looking forward to your application! Job Description This project focuses on optimizing the reliability of industrial machinery through advanced sensor monitoring and automated calibration. During your thesis, you will investigate two main types of sensor failures: physical hardware anomalies and software-related drifts. By combining hands-on test bench measurements with data analysis, you will develop a system that distinguishes between these failures and automatically applies software-based corrections where possible. Your final concept will help develop self-calibrating sensor systems. • During your thesis you will conduct physical measurements on a ball screw drive (BSD) test bench to record and analyze both healthy ("good") sensor data and hardware-related sensor failures ("bad"). You will analyze a provided, pre-detected dataset containing software-related sensor deviations (e.g., sensor drift). • You will determine the criteria that distinguish a software-correctable drift from a physical hardware failure, making the sensor eligible for a purely software-based recalibration. • Furthermore, you will research and evaluate various software-based recalibration techniques. • You will design a robust concept for an automated workflow that, based on your classification, triggers the appropriate software-based recalibration and subsequently tests if the sensor is operating correctly again. • Moreover, you will validate your concept using both your newly recorded hardware datasets and the provided software drift datasets. • Finally, you will identify cases where software recalibration is not sufficient (specifically focusing on the recorded hardware failures) and propose a strategy for how the system should handle these physical anomalies (e.g., flagging for maintenance or triggering hardware replacement). Qualifications • Education: Master studies in the field of Computer Science, Data Science, Electrical Engineering, Mechatronics, Engineering, Physics or comparable with very good academic records • Experience and Knowledge: very good programming skills in Python and proficiency with data science libraries (e.g., Pandas, NumPy, Scikit-learn); strong theoretical knowledge and initial practical experience in data analysis and machine learning concepts, especially in classification; basic understanding of physical measurement principles, sensor technology, or industrial automation; knowledge of collaborative software development and platforms like Git/GitHub • Personality and Working Practice: you are highly motivated, open to collaboration and teamwork, excel at combining an autonomous, systematic working practice with sharp analytical thinking • Work Routine: our hybrid model provides you with a balanced mix of on-site presence and remote work • Enthusiasm: you have a strong interest in solving real-world industrial problems and are fascinated by the idea of creating intelligent, self-maintaining systems • Languages: fluent in English, German is a plus Additional Information Start: according to prior agreement Duration: 6 months Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit. Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity. Need further information about the job? Abbas Hassoun (Functional Department) +49 178 2363207 Work #LikeABosch starts here: Apply now! #LI-DNI