Computational intelligence has become increasingly important for fault detection and isolation in a wide range of systems, including manufacturing processes, power grids, and transportation systems. By analyzing large volumes of data generated by these systems, machine learning algorithms can identify patterns and detect anomalies that might indicate the presence of faults or errors. These algorithms can also be used to predict when faults might occur, allowing for preventative maintenance and reducing the risk of downtime or safety hazards. In addition, computational intelligence techniques can aid in isolating the cause of a fault, which can be crucial for efficient repairs and minimizing the impact on the overall system. As such, computational intelligence for fault detection and isolation represents a promising approach for improving the safety, reliability, and efficiency of complex systems.
Special Session Chairs
- Alma Y. Alanis
alma.alanis@academicos.udg.mx
Universidad de Guadalajara, Mexico - Juan Anzurez-Marin
j.anzurez@ieee.org
Universidad Michoacana de San Nicolas de Hidalgo, Mexico