Technology

As the IIoT technology is in continuous development, our commitment is to disseminate through high end publications, such as, indexed journal articles, books, patents and more. It will also help us to showcase our deep knowledge in our core technology activities in the field of Artificial Intelligence and High Performance Computing applied to different sectors.

Know how

Our know how is based on deep knowledge about Artificial Intelligence and High Performance Computing together with a well grounded industrial expertise.

We see what other do not see

We see what other do not see

Our solutions are based on high quality data oriented to the specific application. This approach helps us to see better than others all added value information.

We find what others do not find

We find what others do not find

Our extensive knowledge in Artificial Intelligence helps us to detect critical information for the specific tasks in terms of anomalies, degradation and failures. This approach of applied Artificial Intelligence to industry help us to find information better than others.

We understand what others do not understand

We understand what others do not understand

Our well grounded industrial expertise mixed our multidisciplinary team, being a mixture of different engineering disciplines, mathematicians and data analysts, help us to correlate what is found in the data with real specific solutions.

We reduce costs

We reduce costs

Our technology helps us to detect specific pattern behaviors that potential create an increase in costs, such as, failures, early degradation or sub optimal behavior patterns.

We increase productivity

We increase productivity

Our technology helps to reduce downtimes with direct impact on the productivity. This technology help to increase the productivity with condition monitoring and maintenance applications.

We optimize resources

We optimize resources

Our technology analyze and monitors different resource consumption pattern in order to detect potential anomalies and unexpected large resource consumer, for example, if the element has a potential malfunction.

Aingura Insights

The Aingura Insights (AI) computing module, 100% design and developed by Aingura IIoT , provides a unique platform that guarantees the data quality. This embedded technology system used proprietary top notch technologies for distributed and high performance computing. The AI module complies with all requirements needed for data acquisition, pre processing, processing and actionable insight delivery phases performed at the Edge.

Aingura Insights

Aingura insights
Aingura insights

Publications

Industrial Applications of Machine Learning

Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems.

More information

Author: J. Diaz-Rozo , J. J. Gabilondo

Title: Device and system including multiple devices for supervision and control of machines in industrial installation

Number: DE112018001085T5

Date: November 2019

Author: J. Diaz-Rozo , J. J. Gabilondo

Title: Device and system including multiple devices for supervision and control of machines in industrial installation

Number: US10317888B2

Date: June 2019

More information

Author: J. Diaz-Rozo , J. J. Gabilondo

Title: Device and system including multiple devices for supervision and control of machines in industrial installation

Number: US10031500B1

Date: July 2018

More information

Author: M. Bermeo-Ayerbe, Carlos Ocampo-Martinez, and J. Diaz-Rozo

Title: Adaptive predictive control for peripheral equipment management to enhance energy efficiency in smart manufacturing systems

Publication: Journal of Cleaner Production, vol. 291, p. 125556

Date: April 2021

More information

Author: D. Atienza, C. Bielza, J. Diaz-Rozo, and Pedro Larrañaga

Title: Efficient Anomaly Detection in a Laser-surface Heat-treatment Process via Laser-spot Tracking

Publication: IEEE/ASME Transactions on Mechatronics

Date: September 2020

More information

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

More information

Author: M. A. Montealegre, B. Arejita, P. Alvarez, C. Laorden, y J. Diaz-Rozo

Title: Control quality on process of laser heat treatment

Publication: Materials Science Forum, vol. 941, pp. 1860–1866.

Date: January 2019

More information

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

More information

Author: J. L. Diaz, M. Bermeo, J. Diaz-Rozo, y C. Ocampo-Martinez

Title: An optimization-based control strategy for energy efficiency of discrete manufacturing systems

Publication: ISA Transactions, vol. 93, pp. 399-409

Date: October 2019

More information

Author: Ying hao Xu, Miquel Vidal, Beñat Arejita, Javier Diaz-Rozo, Carlos Alvarez, Daniel Jiménez-González, Xavier Martorell, Filippo Mantovani

Title: Implementation of the K-Means Algorithm on Heterogeneous Devices: A Use Case Based on an Industrial Dataset

Publication: vol. 32, pp. 642-651

Date: January 2018

More information

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

More information

Author: A. Ogbechie, J. Diaz-Rozo, P. Larrañaga, y C. Bielza

Title: Dynamic Bayesian network-based anomaly detection for in-process visual inspection of laser surface heat treatment

Publication: Machine Learning for Cyber Physical Systems, Springer, pp. 17-24

Date: November 2016

More information

Author: B. Kamp, A. Ochoa, y J. Diaz-Rozo

Title: Smart servitization within the context of industrial user-supplier relationships: contingencies according to a machine tool manufacturer

Publication: Int J Interact Des Manuf, vol. 11, n.o 3, pp. 651-663

Date: August 2017

More information

Author: D. Isaacs, J. Diaz-Rozo, A. Astarola, y B. Arejita

Title: Making Factories Smarter Through Machine Learning

Publication: Industrial Internet Consortium

Date: January 2017

More information

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

More information

Author: J. Diaz-Rozo, C. Bielza, J. L. Ocaña, y P. Larrañaga

Title: Development of a Cyber-Physical System based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control

Publication: Machine Learning for Cyber Physical Systems, Springer Vieweg, Berlin, Heidelberg, pp. 1-8

Date: February 2016

More information

Author: D. Atienza, C. Bielza, J. Diaz-Rozo, y P. Larrañaga

Title: Anomaly Detection with a Spatio-Temporal Tracking of the Laser Spot

Publication: Frontiers in Artificial Intelligence and Applications, pp. 137–142

Date: January 2016

More information

IoTwins

Linked 3rd Party to Etxetar

 

IoTwins is a European project that aims to build a reference architecture for the development of efficient and distributed digital twins for specific manufacturing and facility management domains.

12 dedicated large-scale testbeds will collect large amounts of data to generate and refine the associated digital twins, including optimized models of resources, systems, and processes involved. IoTwins digital twins will be used to improve the efficiency of production processes and of facility management, as well as to demonstrate the replicability of the achieved results in similar scenarios and to determine new application areas and business models.

All the IoTwins testbeds share the same methodology: models that exploit big data and domain expert knowledge to accurately represent a complex system, such as an industrial plant, or a process, or a facility, with the ambition of predicting its temporal evolution and dynamics. The underlying technologies ground on the concept of distributed IoT-/edge- /cloud-enabled hybrid twins.

More information

Dstreams

RESEARCH AND DEVELOPMENT OF AN ARTIFICIAL INTELLIGENCE-BASED METHODOLOGY ORIENTED TO PROVIDE CONTINUOUS AND ULTRA-HIGH SPEED DATA ANALYSIS IN REAL INDUSTRIAL SCENARIOS

 

  • Public Financing Agency: Ministerio de Ciencia e Innovación
  • Program: Retos RTC2019-0006871-1
  • Beneficiaries: Aingura IIoT, S.L. (líder), Titanium Industrial Security, S.L., Universidad Politécnica de Madrid, Universidad de la Iglesia de Deusto, Barcelona Supercomputing Center.
  • Total budget: 771.963,70€

More information

Contact us