For our team, we are looking for a Master thesis student (m/f/d) who writes their thesis with us on the topic of "Predictive Maintenance for Connected Fitness Equipment” in Munich or remotely. EGYM is a Munich-based fitness technology company operating a global fleet of connected strength and cardio equipment, including the EGYM Smart Strength circle. Each muscle group is served by a single dedicated machine, which means that a failure on one device prevents members from training that muscle group entirely until a technician resolves the issue. Service today is largely reactive. As EGYM scales internationally the reactive model will not scale with it. Does this topic spark your interest, then take the chance and apply now!
- Data Discovery: You start your journey by conducting a deep dive into our data, mapping telemetry, service tickets, and machine metadata to build a labeled dataset linking sensor signals to historical failure events
- Method Benchmarking: You perform a rigorous method benchmarking by comparing classical anomaly detection methods (e.g., Isolation Forest, ARIMA) against modern time-series deep learning approaches (e.g., LSTMs, Transformers)
- Proof of Concept: You implement a robust proof of concept, applying your best model to specific machine classes to simulate the alert-to-ticket workflow and measure early breakdown detection
- Business Case Quantification: You quantify the concrete business case, analyzing cost reduction potentials, revenue upside opportunities in service contracting, and future product enhancements to capture additional value
- Academic Write-up & Publication: You are responsible for the evaluation, interpretation, and documentation of the results in English, preparing both your thesis and a co-authored research paper for publication in reputed journal