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.
Our know how is based on deep knowledge about Artificial Intelligence and High Performance Computing together with a well grounded industrial expertise.
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.
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.
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.
DEVELOPMENT OF A DIAGNOSTIC SYSTEM BASED ON MACHINE LEARNING FOR REAL TIME DETECTION OF EARLY DEGRADATION IN THE PHARMACEUTICAL MANUFACTURING PROCESS.
Objective: The MLpharma project seeks to develop and implement, at the prototype level, a solution based on Machine Learning that allows the detection of deviations that may negatively affect pharmaceutical production processes to be detected in real time. This solution will improve the performance of maintenance activities, allowing the production elements causing these deviations to be detected. It will also allow online system efficiency monitoring, since the system will have the capacity to detect quality problems caused by anomalous deviations, enabling the decision making without having to wait to detect it at the end of the production cycle, generally several days.
Result: the final result will be a solution based on Machine Learning for data streams implemented on an Aingura Insights Edge Computing node that can be deployed in productive environments in the pharmaceutical sector for real-time monitoring and anomaly detection.
Public Financing Agency: Entidad Pública Empresarial Red.es
Program: Convocatoria de Ayudas 2020 sobre Desarrollo Tecnológico basado en Inteligencia Artificial y otras Tecnologías Habilitadoras Digitales C0007/20-ED (2020/0720/00099369)
Industrial Cluster Flexibility Platform for Sustainable Factories to Reduce CO2 Emissions and to Enable the Energy Transition
To achieve the goals of the EU Green Deal as well as the national energy agendas, an increasing need for flexibility to compensate for fluctuating generation from renewable energy sources is needed. The industrial sector as one of the largest consumers of energy has a mostly untapped potential of flexibility provision.
Increased flexibility of industrial production processes combined with on-site energy supply and storage technologies can offer new opportunities to improve sustainability of industrial sites and help integrate more renewable sources in the power grid. However, there are still some challenges to be tackled: tools need to be developed to better integrate energy storage solutions and renewable sources into industrial settings. Furthermore, solutions linking the flexibility potential of industrial sites with surrounding community and energy markets as a whole need to be developed to enable the realization of new business models.
FLEX4FACT will accompany industrial partners in achieving their energy transition by developing digital tools integrated in a holistic platform, thus paving the way for industrial flexibility provision benefiting various stakeholders along the value chain. This will increase the penetration of renewable sources, which will in turn decrease the dependency on energy fuels and reduce the energy bill of EU enterprises, leading to a competitive EU industry.