Reduction of unscheduled shutdowns (80%)
- Reduction of preventive maintenance cost (50%).
- Reduction of corrective maintenance cost (20%).
- Reduction of energy consumptions (5%).
- PRI: 1,5 years
We champion a down-top approach where maintenance improvements are initially generated at the producer/consumer level rotodynamic systems (conveyor belts, pumps, HBS, automatic stairs...) and can subsequently be integrated into centralized corporate systems (GMAO, Máximo, SAP, Scada...).
Author: Miguel Bermeo-Ayerbe, Vincent Cocquempot, Carlos Ocampo-Martinez, and Javier Diaz-Rozo
Title: Remaining useful life estimation of ball-bearings based on motor current signature analysis
Publication: Reliability Engineering &System Safety, vol. 235, p. 109209
Date: July 2023
Author: Carlos Puerto-Santana, Concha Bielza, Javier Diaz-Rozo, Guillem Ramirez-Gargallo, Filippo Mantovani, Gaizka Virumbrales, Jesús Labarta and Pedro Larrañaga
Title: Asymmetric HMMs for Online Ball-Bearing Health Assessments
Publication: IEEE Internet of Things Journal, vol. 9, p. 20160-20177
Date: October 2022
Author: M. Bermeo-Ayerbe, Carlos Ocampo-Martinez, and J. Diaz-Rozo
Title: Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems
Publication: Journal of Energy, vol. 238, p. 121691
Date: January 2022
Author: J. Diaz-Rozo, C. Bielza, y P. Larrañaga
Title: Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering
Publication: Engineering Applications of Artificial Intelligence, vol. 89, p. 103434
Date: March 2020
Author: J. Diaz-Rozo
Title: Clustering probabilístico dinámico para la búsqueda de patrones de degradación de elementos de máquina en el ámbito del Industrie 4.0
Publication: PhD Thesis, Universidad Politécnica de Madrid
Date: September 2019
Author: J. Diaz-Rozo, C. Bielza, y P. Larrañaga
Title: Clustering of data streams with dynamic Gaussian mixture models: An IoT application in industrial processes
Publication: IEEE Internet of Things Journal, vol. 5, n.o 5, pp. 3533-3547
Date: October 2018
Author: J. Diaz-Rozo, C. Bielza, y P. Larrañaga
Title: Machine learning-based CPS for clustering high throughput machining cycle conditions
Publication: Procedia Manufacturing, vol. 10, pp. 997-1008
Date: January 2017