Classificação on-line de situações anormais em operação de processos industriais baseada em processamento de alarmes e variáveis de processos
Industrial processes are subject to failures in their thousands of components at any time and can lead to shutdowns, loss of product quality, equipment damage or even accidents. In this sense, the alarm system is necessary to aid in the identification of process abnormalities. However, during a pr...
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| Format: | doctoralThesis |
| Language: | por |
| Published: |
Brasil
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| Online Access: | https://repositorio.ufrn.br/jspui/handle/123456789/26508 |
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| Summary: | Industrial processes are subject to failures in their thousands of components at any
time and can lead to shutdowns, loss of product quality, equipment damage or even accidents. In this sense, the alarm system is necessary to aid in the identification of process
abnormalities. However, during a process failure it is common for the operator to be
subjected to hundreds of alarms causing overload beyond the human processing capacity.
This phenomenon is known as alarm flood and to treat them properly is a challenge for
the modern alarms systems. Thus, the present work aims at the development of an online alarm processing methodology capable of assisting the operator in the identification
and classification of abnormal situations of the process, especially in moments of alarm
overload. To validate the proposal, a case study was carried out on a process simulator
widely used and accepted by the scientific community called Tennessee Eastman Process.
The results indicate that it is important to identify and monitor the abnormality scenarios
underway in industrial processes. The results show that the methodology is efficient to
identify and follow the abnormality scenarios in progress in industrial processes. |
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