Chronicle based alarm management (2017)
"This work is motivated by the need of the industry to detect abnormal situations in the plant startup and shutdown stages. Industrial plants involve integrated management of all the events that may cause accidents and translate into alarms. Process alarm management can be formulated as an event-based pattern recognition problem in which temporal patterns are used to characterize different typical situations, particularly at startup and shutdown stages. In this thesis, a new approach for alarm management based on a diagnosis process is proposed. Considering the alarms and the actions of the standard operating procedure as discrete events, the diagnosis step relies on situation recognition to provide the operators with relevant information about the failures inducing the alarm ow. The situation recognition is based on chronicles that characterize the situations of interest and are learned automatically. The chronicles are learned from representative event sequences obtained by simulation and given as input to an extended version of the Heuristic Chronicle Discovery Algorithm Modi ed (HCDAM). HCDAM has been extended in this thesis to account for expert knowledge in the form of specific temporal restrictions. A hybrid causal model of the process is used to verify the input event sequences and to explain and provide semantics to the learned chronicles. The Chronicle Based Alarm Management (CBAM) methodology proposed in this thesis involves different techniques to take the hybrid aspect and the standard operational procedures of the concerned processes into account. Compared to other approaches of alarm management, this approach uses information about the procedural actions related to the continuous variables behavior in a formal diagnosis process. Specific information is obtained in each step of the CBAM methodology."--Tomado del Formato de Documento de Grado