Real name: 
Gianluca Manca
First Name: 
Gianluca
Last Name: 
Manca
Affiliation: 
Helmut-Schmidt-University / Institute of Automation Technology
External links: 
ORCID
Short Bio: 
GIANLUCA MANCA was born in Buchholz i.d.N., Germany in 1989. He received the B.Sc. degree in mechanical engineering and the M.Sc. degree in mechatronics from the Helmut-Schmidt-University, Hamburg, Germany, in 2013 and 2014, respectively, and the B.Sc. degree in information systems from the University of Hagen, Germany, in 2019. He is currently pursuing the Ph.D. degree in engineering at the Helmut-Schmidt-University, Hamburg, Germany. His main research interests are the analysis and reduction of alarm floods in industrial alarm systems as well as the application of data mining and machine learning methods to industrial processes.

Datasets & Competitions

Nuclear Power Plant Alarm Dataset

We introduce a novel dataset specifically designed for the evaluation of “alarm flood classification” (AFC) methods within process plants. The growing complexity of industrial systems and the heightened demands for operational safety and efficiency underscore the critical need for advanced diagnostic tools capable of handling alarm floods—situations where numerous alarms are triggered simultaneously.

Categories:
Artificial Intelligence
Signal Processing
Machine Learning
206 Views

"Tennessee-Eastman-Process" Alarm Management Dataset

This is an alarm management dataset based on the “Tennessee-Eastman-Process” (TEP). The presented dataset aims to provide a suitable benchmark for the development and validation of alarm management methods in complex industrial processes using both quantitative data and qualitative information from different sources. Unlike real industrial processes, the simulation of the TEP allows to design and generate abnormal situations, which can be repeated and varied without risking the loss of equipment or harming the environment.

Categories:
Signal Processing
Machine Learning
3847 Views