A Framework for Optimal Design of Alarm Systems

Izadi, Iman; Shah, Sirish L.; Shook, David S.; Kondaveeti, Sandeep R.; Chen, Tongwen

doi:10.3182/20090630-4-es-2003.00108

Cited by 81 publications

(30 citation statements)

References 2 publications

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“…Other than the increase in P P as a function of h, an unexpected 100 psi increase in P P also occurs from 11:32 AM to 11:55 AM due to the faulty conditions, moving the operation closer to underbalanced drilling conditions. Accordingly, the probability of underbalanced conditions increases as a function of h, which can be estimated using Equation Assuming that the P P measurements are intended to be used for warning generation, noise filtering is carried out for pressure measurements using the moving average filter in order to minimize false warnings [40]. The filtered measurements are shown in darker colour in Figure 8.…”

Section: Psimentioning

confidence: 99%

Multivariate probabilistic safety analysis of process facilities using the Copula Bayesian Network model

Hashemi

Khan

Ahmed

2016

Computers & Chemical Engineering

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Section: Psimentioning

confidence: 99%

Multivariate probabilistic safety analysis of process facilities using the Copula Bayesian Network model

Hashemi

Khan

Ahmed

2016

Computers & Chemical Engineering

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“…In order to minimize false warnings, noise filtering is carried out for all sensor measurements using the moving average filter which is commonly used for this purpose, see e.g. Izadi et al (2009b);Smith (2009). Further, a forecasting step is introduced to enhance robustness of the alarm system.…”

Section: Alarm Annunciationmentioning

confidence: 99%

Bayesian method for event-based alarm annunciation

2015

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The concept of an event-based alarm system is introduced and a Bayesian method to annunciate event alarms is presented in this article. Unlike the current variable-based system, event alarms are allocated to abnormal events and are annunciated based on the risk of such events. The event-based alarm system design involves identification of abnormal events, identification of the sets of variables associated with each event and development of a model to predict the risk of each event using process measurements. Risk estimation involves the estimation of probabilities of individual events and their consequences. This article presents a method for estimating the probability of occurrence of abnormal events for the purpose of annunciation of an alarm. The methodology is demonstrated using the example of a simulated continuous stirred tank reactor.

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“…Several case studies have shown success with advanced alarm management techniques, involving alarm history, process data and plant topology (e.g. [13,14]). However, the situation in many production facilities still requires improved methods to reduce the number of alarms presented to the operator.…”

Section: Related Workmentioning

confidence: 99%

A combined analysis of plant connectivity and alarm logs to reduce the number of alerts in an automation system

Schleburg

Christiansen

Thornhill

et al. 2013

Journal of Process Control

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a b s t r a c tA process alarm arises when normal operation limits are exceeded and an alarm management system alerts the operator of a process plant. Due to the material, energy and information flow in a plant, single disturbances can cause multiple consequent alarm messages, and the alarm messages may overload the operator by presenting many redundant alarms. This undesired situation is called an 'alarm flood'. In such situations, the operator might not be able to fulfil his required tasks to keep the plant within safe operation limits and to find the root cause of the disturbance. The aim of the work presented in this paper is to reduce the number of alerts presented to the operator. If alarms are related to one another, those alarms should be grouped and presented as one alarm problem. For the implementation of the concept, a software prototype has been developed to perform this reduction automatically. The analysis process starts with the alarm history which is a log containing all past alarm messages. This is combined with the plant topology of the controlled system and a set of rules. The rules describe typical interrelations between alarm messages which have a common cause. The combination of these three elements yields an effective alarm management strategy that can help plant owners and operators to comply with standards for alarm management such as ANSI /ISA 18.2 (2009) and EEMUA 191 (2007) which set limits on the number of alarms per unit time for an operator. The effectiveness of the approach is illustrated by two industrial examples where a significant reduction of alarms has been achieved.

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A Framework for Optimal Design of Alarm Systems

Cited by 81 publications

References 2 publications

Multivariate probabilistic safety analysis of process facilities using the Copula Bayesian Network model

Multivariate probabilistic safety analysis of process facilities using the Copula Bayesian Network model

Bayesian method for event-based alarm annunciation

A combined analysis of plant connectivity and alarm logs to reduce the number of alerts in an automation system

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