UC5 - Failure Prevention for Manufacturing Industry

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Authors: Borja Tornos
Cited by: 0

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