One of the biggest challenges in soft computing approaches related to modelling, diagnosis and prognosis solutions, applied to machines or structures, is the dynamic behavior given by their real-life operating conditions. This situation represents a complex phenomenon, where computational techniques in machine learning, computer science and some engineering disciplines need to overcome in order to guarantee acceptable performance. Such techniques, together with advanced control/management techniques, seek for establishing suitable operating conditions of the considered systems towards showing desired performance while handling with unexpected effects given by external disturbances, model mismatching and other sources of uncertainty. This session assesses the state-of-the-art of Artificial Intelligence methodologies and applications that address dynamic-based challenges: Concept drift, feature subset selection, high-speed data streams processing, algorithm computation efficiency, among others. The application field covers, but not limited to, condition monitoring, fault diagnosis detection, isolation and identification, energy management and advanced control systems.
Session topics include, but are not limited to, the following:
- Fault detection and diagnosis
- Prognosis and health monitoring
- Structural health monitoring
- Predictive maintenance
- Control techniques for energy efficiency and optimization
- Soft computing-based novelty detection
- AI-enabled real-time IoT data analytics
- AI-enabled sensing and decision-making for IoT
- Chair: Javier Diaz-Rozo (Aingura IIoT, Spain)
- Co-Chair: Carlos Ocampo-Martinez (Universidad Politécnica de Cataluña, Spain)
- Co-Chair: Filippo Mantovani (Barcelona Supercomputing Center, Spain)
For further information please contact Dr. Javier Diaz-Rozo (firstname.lastname@example.org)
Aingura IIoT S.L.