Data-Driven approach to parametric configuration of industrial alarms

• Ano de defesa: 2019
• Autor(a) principal: Nunes, Yuri Thomas Pinheiro
• Orientador(a): Oliveira, Luiz Affonso Henderson Guedes de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA ELÉTRICA E DE COMPUTAÇÃO
• País: Brasil
• Palavras-chave em Português: Alarm configuration; Industrial alarms; Alarm tuning; Industrial automation; Alarm management system
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/26830

Industrial plants are composed of processes that add up to thousands of variables. To ensure safety and quality of operation, these processes are monitored and alarms are configured to indicate a possible malfunction. Among the most common problems associated with industrial alarms we can mention: high occurrence of false alarms, missed alarms and chattering alarms, operator overload and alarm flooding. These problems are related to the process of selection of the monitored variables, the techniques of activation and deactivation of alarms, among other characteristics of the process and the alarm system. This work focuses on defining an approach to configure efficient and significant alarms for the operator. The approach proposed here is inspired by the workflow of a data scientist who initially needs to identify the characteristics of the databases used to then apply transformations that make the data more suitable allowing the extraction of valuable information. Many times the scientist is interested in creating a model that describes the data or makes predictions possible. This is a very similar task of alarm configuration where it is necessary to select the relevant variables and to configure the settings of each alarm in order to classify the operation of the process as appropriate or not and to help identify the fault. The approach proposed here consists of four parts: description of data, selection of variables, tuning and performance evaluation. During the description step, relevant information about the data is obtained, such as the presence of events, the number of different events, the duration of events, etc. In the selection stage, the relevant variables for detection of abnormalities are defined. The tuning of alarms is similar to a training process, where a model is built to describe the behavior of the data. Finally during the evaluation, the settings found are applied to a process history to asses whether the settings behave in a way that meets security and quality constraints. In order to validate the proposal, a case study for industrial alarm configuration was carried out using the Tennessee Eastman Process, which is a benchmark simulator widely used by the academic community.