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HERRENKNECHT AG is now offering a
Masterthesis (m/f/d) | reference number 760
Technology & Innovation Digitalisation

The early detection of anomalies in time-series data -- e.g., caused by mechanical faults -- is an important aspect to ensure the operational readiness of tunnel boring machines. The systematic acquisition of sensor data, together with the availability of machine learning technologies, has opened new possibilities for developing effective systems for monitoring the status of equipment e.g., hydraulic power units. Consequently, the development of a system for monitoring the health status of tunnel boring machines signifies the traversing research initiative.  
Within this field, the master thesis should cover the following points: Explore, examine, and analyze the data of the numerous sensors placed on tunnel boring machines, measuring e.g., the contact force while excavating. With the assistance of tunneling experts, identify a relevant and promising sensor set including anomalies such as engine breakdowns, defective sensors, or corrupted data.  Develop a self-supervised online-algorithm to detect anomalies. Conduct performance benchmarks encompassing both quantitative and qualitative results, for instance, an expert blind test or using labeled data. Overall, an industrial deployment and scalability across multiple projects should be taken into consideration.

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