The Industrial Informatics research team focuses on the application of recent methods of artificial intelligence and machine learning in electrical engineering. The main application areas are those researched at RICE by other groups. The team is composed of researchers with diverse backgrounds, ranging from mathematicians to computer scientists and electrical engineers. The team provides support to other RICE teams and industrial partners.
The research in the field of applied AI mainly focuses on:
- optimization of the design of electrical machines
- optimal operation of electrical machines, power networks - control, diagnostics, fault detection
- data-driven identification of mathematical models of complex phenomena
- design of expensive and time-consuming experiments, e.g., in material research.
Main areas of application:
- Power electronics and drives
Research on optimization of various aspects of drive and converter design, using blackbox optimization of numerical solvers. Research of optimal control strategies (time-optimal, energy efficiency, etc.). Advanced signal processing providing feedback from noisy signals, e.g., torque ripple. Model predictive control and its automatic tuning. Thermal models of power modules and drives.
- Manufacturing technology
Data-driven mathematical models of technology processes, such as inductive metal heating and material degradation (e.g., aging).
- Electric networks
Identification of network state and detection of earth faults.
The algorithms developed in the team have been successfully delivered to industrial partners.
What we offer
- Consultation and development of custom algorithms based on optimization, machine learning, and artificial intelligence.
We have significant experience with the design and development of advanced algorithms for real-time processing. The customization often aims to reduce advanced methods' computational requirements while preserving their accuracy.
- Data analysis of large data sets using machine learning.
