Optimize service availability and improve user experience

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...).



    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
  • Adaptability

    Adaptable to different environments

    • Less susceptible to failure.
    • Non intrusive.
    • More economical


The system generates a Health Index for electromechanical systems

Based on energy consumption and vibrations correlation, enabling early detection of abnormal behavior, priorizing energy consumption as analysis signal to detect both mechanical and electrical anomalies.

Minimizing errors in consumption analysis

The algorithm optimally associates multiple consumer switch-o  events with a single switch-on event.

Enhances early anomaly detection and energy efficiency

The system infers mechanical behavior from energy consumption, monitoring multiple devices with one energy meter, allowing the execution of maintenance routines avoiding corrective maintenance when the anomaly has not yet caused the system to shut down.


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

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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

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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

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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

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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

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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

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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

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Case studies

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