Gestão de Sistemas de Alarmes em Processos de Granulação de Adubos

Os sistemas de alarmes industriais que não são monitorizados e geridos continuamente, originam perdas avultadas para as empresas, tanto a nível financeiro como a nível de produ- ção. Atualmente, a aplicação e implementação de boas práticas de gestão de alarmes repre- senta um aumento de competitividade em diversos tipos de indústrias e uma alavancagem em termos da Indústria 4.0. O presente trabalho tem como objetivo principal a aplicação de boas práticas de gestão de alarmes, segundo as normas internacionais ISA 18.2 e EEMUA 191, num processo de gra- nulação de adubos. Inicialmente foi realizada a etapa de Identificação do ciclo de vida da gestão de alarmes. Nesta etapa, foi aplicada a metodologia Análise de Modos de Falha e seus Efeitos (FMEA) de modo a obter uma quantificação dos riscos existentes, através do Número de Prioridade do Risco (NPR), no processo e sugerir ações de melhoria. De forma a complementar esta análise, foi realizado um estudo do impacto de cada melhoria na redução do risco. No processo de granulação considerado, foram identificadas 661 potenciais causas de falha. Destas causas, 54,8% apresentava grau de risco baixo, 23,4% grau de risco moderado, 16,5% grau de risco elevado e 5,3% apresentava grau de risco crítico. Através da análise de criticidade e da relação entre o NPR e a criticidade, identificaram- se 29 causas de modos de falha. Ao implementar as sugestões de melhoria, estima-se uma redução total do NPR de 12,2%, em que 8,2% está associado a planos de inspeção periódica. Estima-se um custo médio de 26 500€. Adicionalmente, foi avaliado o desempenho do sistema de alarmes, em períodos distin- tos de estabilidade do processo, através da aplicação de Key Performance Indicator's. Em perí- odo de instabilidade, no sistema de alarmes atual, foram gerados, em média, 139 alarmes por dia e o sistema apresentava uma instabilidade de 4%. O alarme mais frequente foi do tipo "Falta de Carga", correspondendo a 15,7% da totalidade de alarmes gerados. No período de estabilidade do processo, existiu uma redução de 29,5% na quantidade de alarmes por dia, o sistema apenas apresentava instabilidade de 1,8% e o tipo de alarme mais frequente perma- necia o mesmo. Nos dois períodos de análise, o sistema de alarmes apresentava-se como ro- busto.Industrial alarm systems that are not monitored and managed continuously cause heavy losses for companies, both financially and in terms of production. Currently, the appli- cation and implementation of good alarm management practices represents an increase in competitiveness in several types of industries and a leverage in terms of Industry 4.0. The main purpose of this thesis is the application of good alarm management practices, according to the international standards ISA 18.2 and EEMUA 191, in one fertilizer granula- tion processes. Initially the Identification stage of the alarm management life cycle was carried out. In this stage, the Failure Mode and Effect Analysis (FMEA) methodology was applied in order to obtain a quantification of the existing risks, through the Risk Priority Number (RPN), in the process and to suggest improvement actions. In order to complement this analysis, a study of the impact of each improvement on risk reduction was performed. In the granulation process, 661 potential causes of failure were identified. Of these causes, 54,8% presented low risk level, 23,4% medium risk level, 16,5% high risk level and 5,3% presented critical risk level. Through the criticality analysis and the relationship between NPR and criticality, 29 failure mode causes were identified. By implementing the suggestions for improvement, we estimate a total reduction in NPR of 12,2%, of which 8,2% is associated with periodic inspec- tion plans. The average cost is estimated to be 26,500€. Additionally, the performance of the alarm system was evaluated, in different periods of process stability, through the application of Key Performance Indicators. In periods of in- stability, in the current alarm system, an average of 139 alarms per day were generated and the system presented an instability of 4%. The most frequent alarm was of the "No Load" type, corresponding to 15,7% of all alarms generated. In the period of process stability, there was a reduction of 29,5% in the quantity of alarms per day, the system only presented instability of 1,8% and the most frequent type of alarm remained the same. In both periods of analysis, the alarm system presented itself as robust

Diagnostic de systèmes complexes par comparaison de listes d’alarmes : application aux systèmes de contrôle du LHC

In the context of the CERN Large Hadron Collider (LHC), a large number of control systems have been built based on industrial control and SCADA solutions. Beyond the complexity of these systems, a large number of sensors and actuators are controlled which make the monitoring and diagnostic of these equipment a continuous and real challenge for human operators. Even with the existing SCADA monitoring tools, critical situations prompt alarms avalanches in the supervision that makes diagnostic more difficult. This thesis proposes a decision support methodology based on the use of historical data. Past faults signatures represented by alarm lists are compared with the alarm list of the fault to diagnose using pattern matching methods. Two approaches are considered. In the first one, the order of appearance is not taken into account, the alarm lists are then represented by a binary vector and compared to each other thanks to an original weighted distance. Every alarm is weighted according to its ability to represent correctly every past faults. The second approach takes into account the alarms order and uses a symbolic sequence to represent the faults. The comparison between the sequences is then made by an adapted version of the Needleman and Wunsch algorithm widely used in Bio-Informatic. The two methods are tested on artificial data and on simulated data extracted from a very realistic simulator of one of the CERN system. Both methods show good results.Au CERN (Organisation européenne pour la recherche nucléaire), le contrôle et la supervision du plus grand accélérateur du monde, le LHC (Large Hadron Collider), sont basés sur des solutions industrielles (SCADA). Le LHC est composé de sous-systèmes disposant d’un grand nombre de capteurs et d’actionneurs qui rendent la surveillance de ces équipements un véritable défi pour les opérateurs. Même avec les solutions SCADA actuelles, l’occurrence d’un défaut déclenche de véritables avalanches d’alarmes, rendant le diagnostic de ces systèmes très difficile. Cette thèse propose une méthodologie d’aide au diagnostic à partir de données historiques du système. Les signatures des défauts déjà rencontrés et représentés par les listes d’alarmes qu’ils ont déclenchés sont comparées à la liste d’alarmes du défaut à diagnostiquer. Deux approches sont considérées. Dans la première, l’ordre d’apparition des alarmes n’est pas pris en compte et les listes d’alarmes sont représentées par un vecteur binaire. La comparaison se fait à l’aide d’une distance pondérée. Le poids de chaque alarme est évalué en fonction de son aptitude à caractériser chaque défaut. La seconde approche prend en compte l’ordre d’apparition des alarmes, les listes d’alarmes sont alors représentées sous forme de séquences symboliques. La comparaison entre ces deux séquences se fait à l’aide d’un algorithme dérivé de l’algorithme de Needleman et Wunsch utilisé dans le domaine de la Bio-Informatique. Les deux approches sont testées sur des données artificielles ainsi que sur des données extraites d’un simulateur très réaliste d’un des systèmes du LHC et montrent de bons résultats

The intelligent alarm management system

10.1109/MS.2003.1184170IEEE Software20266-71IESO