A data geek, Houda is Data and knowledge state engineer and a recent Data science master graduate from Polytechnic University of Madrid. She has a background in data consulting and logistics. Her research interest lies in the application of deep learning and Bayesian inference in the field of social science and ergonomics.
About her CISC Research she says:
What would happen if you missed a red signal from your car’s dashboard?
The introduction of distributed control systems (DCS) changed the traditional alarm systems in the process industry. It becomes so easy to add new alarms for instruments linked to a DCS, leading to an alarm overload on the operating console of the DCS. Operators are overloaded with hundreds of alarms that may not be relevant, interfering with their ability to understand and respond correctly to the situation which can lead in some cases to catastrophic accidents.
Thus, analysis of the alarm flood events from both the system and the operator’s perspectives is crucial for improving the alarm management practices in process industries, especially the oil & gas industry.
My research is aiming to build a model for a real-time assessment of human performance in the context of human-machine interaction. In the first steps of the project, I investigated the current practices of alarm management for petrochemical industries and analyzed alarm data to detect alarm flood events patterns and their causal factors. This will allow me to design an experiment for human cognitive performance assessment based on specific alarm flood scenarios. In the experiment, I intend to analyze EEG signals of the operators to investigate how the flood of alarm affects the operators’ workload and situational awareness.