Research of autoencoder-based predictive maintenance for elevators
This work examines the use of autoencoders for predictive maintenance of elevator systems. By analyzing sensor data collected by three acceleration sensors, an autoencoder is used to detect anomalies at an early stage and make maintenance more efficient. The project offers an in-depth exploration of autoencoder architectures and their training strategies for developing accurate anomaly detection. Students have the opportunity to gain practical experience in the field of machine learning and develop their own models.
- Research Project or Bachelor/Master Thesis
- Supervisor: M. Sc. Carlo Schafflik