Affiliations: ILTC, Instituto de Lógica, Filosofia e Teoria da Ciência, Niterói. Rio de Janeiro, Brazil | ADDLabs, Computer Science Department, Fluminense Federal University, Niterói. Rio de Janeiro, Brazil
Note: [] Corresponding author. Nayat Sanchez-Pi, ILTC, Instituto de Lógica, Filosofia e Teoria da Ciência, Niterói. Rio de Janeiro, Brazil. E-mails: [email protected] (Nayat Sanchez-Pi); [email protected] (Luiz Andre Paes Leme); [email protected] (Ana Cristina Bicharra Garcia).
Abstract: Alarm management is a fast-growing and important aspect in the petroleum operation domain. Alarm devices have become very cheap leading petroleum equipment manufacturers to overuse them transferring safety responsibility to operators. Not rarely, accident reports cite poor operators understanding of the actual plant status due to too many active alarms. Typical alarms for a process plant could average over fourteen thousand per day so, there is mandatory to have a filtering process to distinguish expected from non-expected behavior during emergency scenarios. Ambient Intelligence contributes by enriching the petroleum plant environment with technology (mainly sensors and devices interconnected through a network) and built a system to help plant operators to make decisions based on real-time information gathered and historical data accumulated. Ambient Intelligence puts together all these resources to provide flexible and intelligent services to users acting in their environment. Inspired by the distributed and encapsulated aspect of the process plant artifact physical model, we proposed a multi-agent-based alarm management system to synthesize the process plant situation during emergency situations and assisting operators to make sense of alarm avalanche scenarios.
