An additional layer of protection through superalarms with diagnosis capability (original) (raw)
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Una nueva capa de protección a través de súper alarmas con capacidad de diagnóstico
2020
An alarm management methodology can be proposed as a discrete event sequence recognition problem where time patterns are used to identify the process safe condition, especially in the start-up and shutdown stages. Industrial plants, particularly in the petrochemical, energy, and chemical sectors, require a combined approach of all the events that can result in a catastrophic accident. This document introduces a new layer of protection (super-alarm) for industrial processes based on a diagnostic stage. Alarms and actions of the standard operating procedure are considered discrete events involved in sequences, where the diagnostic stage corresponds to the recognition of a special situation when these sequences occur. This is meant to provide operators with pertinent information regarding the normal or abnormal situations induced by the flow of alarms. Chronicles Based Alarm Management (CBAM) is the methodology used to build the chronicles that will permit to generate the super-alarms ...
Entropy
In automated plants, particularly in the petrochemical, energy, and chemical industries, the combined management of all of the incidents that can produce a catastrophic accident is required. In order to do this, an alarm management methodology can be formulated as a discrete event sequence recognition problem, in which time patterns are used to identify the safe condition of the process, especially in the start-up and shutdown stages. In this paper, a new layer of protection (a Super-Alarm), based on the diagnostic stage to industrial processes is presented. The alarms and actions of the standard operating procedures are considered to be discrete events involved in sequences; the diagnostic stage corresponds to the recognition of the situation when these sequences occur. This provides operators with pertinent information about the normal or abnormal situations induced by the flow of the alarms. Chronicles Based Alarm Management (CBAM) is the methodology used in this document to buil...
Alarm management based on diagnosis
IFAC-PapersOnLine, 2016
The transitions between operational modes (startup/shutdown) in chemical processes generate alarm floods and cause critical alarm saturation. We propose in this paper an approach of alarm management based on a diagnosis process. This diagnosis step relies on situation recognition to provide to the operators relevant information on the failures inducing the alarms flows. The situation recognition is based on chronicle recognition where we propose to use the hybrid causal model of the system and the expertise to generate the pattern event sequences from which the chronicles will be extracted using the Heuristic Chronicle Discovery Algorithm Modified HCDAM. An illustrative example in the field of petrochemical plants is presented in the article.
New concept of safeprocess based on a fault detection methodology: Super Alarms
IFAC-PapersOnLine, 2019
Industrial plants, especially on mining, metal processing, energy and chemi-cal/petrochemical processes require 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 paper, a new layer based on a diagnosis process is proposed over the typical layers of protection in industrial processes. 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 flow. The new concept of super alarms is based on a methodology with a diagnosis step that permits generate these types of superior alarms. For example, the Chronicle Based Alarm Management (CBAM) methodology involves different techniques to take the hybrid aspect and the standard operational procedures of the concerned processes into account.
Chronicle Based Alarm Management
2017
Industrial plant safety involves integrated management of all the factors that may cause incidents. Process alarm management is a requisite that can be formulated as a pattern recognition problem in which temporal patterns are used to characterize different typical situations, particularly at startup and shutdown stages. In this thesis, we propose a new approach of alarm management based on a diagnosis process. Assuming the alarms and the actions of the standard operating procedures as discrete events, diagnosis relies on situation recognition to provide the operators with relevant information about the faults inducing the alarm flows. Situation recognition is based on chronicles that are learned for every situation. We propose to use the hybrid causal model of the system and simulations to generate the representative event sequences from which the chronicles are learned using the Heuristic Chronicle Discovery Algorithm Modified (HCDAM). An extension of this algorithm is presented i...
Chronicle Based Alarm Management in Startup and Shutdown Stages
2015
The transitions between operational modes (startup/shutdown) in chemical processes generate alarm floods and cause critical alarm saturation. We propose in this paper an approach of alarm management based on a diagnosis process. This diagnosis step relies on situation recognition to provide to the operators relevant information on the failures inducing the alarms flows. The situation recognition is based on chronicle recognition. We propose to use the information issued from the modeling of the system to generate temporal runs from which the chronicles are extracted. An illustrative example in the field of petrochemical plants ends the article.
Alarm management via temporal pattern learning
Engineering Applications of Artificial Intelligence, 2017
Industrial plant safety involves integrated management of all the factors that may cause accidents. Process alarm management can be formulated as a pattern recognition problem in which temporal patterns are used to characterize different typical situations, particularly at startup and shutdown stages. In this paper we propose a new approach of alarm management based on a diagnosis process. Assuming 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 on the failures inducing the alarm flows. The situation recognition is based on chronicle recognition where we propose to use the hybrid causal model of the system and simulations to generate the representative event sequences from which the chronicles are learned using the Heuristic Chronicle Discovery Algorithm Modified (HCDAM). An extension of this algorithm is presented in this article where the expertise knowledge is included as temporal restrictions which are a new input to HCDAM. An illustrative example in the field of petrochemical plants is presented.
Industrial & Engineering Chemistry Research, 2010
A novel framework to model the chronology of incidents is presentedsdepicting the relationship of initiating events with the various regulating and protection systems of the processseventually leading to consequences, varying from zero to high severities. The key premise is that the departures and subsequent returns of process and product quality variables, from and to their normal operating ranges, are recognized as near-misses, which could have propagated to incidents. This leads to the availability of vast near-miss information recorded in distributed control and emergency shutdown systems databases that monitor the dynamics of the process. New performance indices, which utilize this abundant information, are introduced to conduct quantitative and qualitative (absolute and relative) assessment of the real-time safety and operability performances of an industrial fluidized-catalytic-cracking unit (FCCU) at a petroleum refinery. Also, new techniques for abnormal event tracking and recovery-time analysis are presented, which help to identify the variables that experience operational difficulties. It is shown how this information can be used to suggest improvements in the alarmsystem structures for the FCCU.
Gestion D'Alarme À Base De Chronique
2017
Thèse en cotutelle avec l'Universidad de los Andes, ColombieNational audienceThis thesis work was carried out in the framework of a co-tutelle between INSA, Toulouse, and the University of the Andes, Colombia, with financial support of Colciencias. 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 informat...
Alarm processing with model-based diagnosis of event discrete systems
Proceedings of the AI for an Intelligent Planet on - AIIP '11, 2011
Reliable and informative alarm processing is important for improving the situational awareness of operators of electricity networks and other complex systems. Earlier approaches to alarm processing have been predominantly syntactic, based on textlevel filtering of alarm sequences or shallow models of the monitored system. We argue that a deep understanding of the current state of the system being monitored is a prerequisite for more advanced forms of alarm processing. We use a model-based approach to infer the (unobservable) events behind alarms and to determine causal connections between events and alarms. Based on this information, we propose implementations of several forms of alarm processing functionalities. We demonstrate and evaluate the resulting framework with data from an Australian transmission network operator.