Anomaly detection and diagnosis in multi-sensor systems refer to pinpointing abnormal status in specific periods and identifying the root causes. However, building such a diagnosis tool is challenging since it requires encoding both temporal and inter-sensor dependencies such as spatial sensor information. Also, the detector system should reflect the severity of different incidents.
Dr. Behzad Nobakht
He has a PhD in Petroleum Engineering from Heriot-Watt University. Before that he completed a research internship at Total E&P Pau, France. He also has a Master’s degree in Petroleum Engineering from Polytechnic University of Turin, and a Bachelor’s degree in Chemical Engineering from Sharif University of Technology.
Dr. Nobakht is also responsible for automating workflows using machine learning and advanced statistical methods for CBM modelling and Uncertainty Quantification (UQ).