UC5 - Failure Prevention for Manufacturing Industry
Abstract
In the industry-driven manufacturing sector, unplanned downtime directly translates into significant financial loss-every minute a machine is stopped drops productivity and increases costs. At UC5-Failure prevention for manufacturing; mechanical/electrical anomaly detection in machine components is currently post-mortem: data manually downloaded after events, analyzed by data scientists reactively. ExtremeXP automates the full workflow for real-time decisions: machine sensor data through predefined movements → high-frequency gathering for training → multiple deep learning models (varying architectures/hyperparameters) evaluated systematically → anomaly predictions → expert validation. UC5-Failure Prevention for Manufacturing Industry. Enables identifying best models for critical component failure/breakage prevention. Part of the ExtremeXP project, co-funded by the European Union Horizon Program HORIZON CL4-2022-DATA-01-01 under Grant Agreement No. 101093164. Project website: https://extremexp.eu/.
Fields of Study & Subjects
Machine Fault Diagnosis Techniques
Fault Detection and Control Systems
Reliability and Maintenance Optimization
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 26 February 2026
- Language
- EN
- Document type
- Dataset