An Introduction to Alarm Analysis and Design

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

doi:10.3182/20090630-4-es-2003.00107

Cited by 94 publications

(39 citation statements)

References 4 publications

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“…The correlation coefficient of the i-th dimensional and the j-th dimensional variables X T Multidimensional variable alarm thresholds vector P NA The probability of normal situations variable values fall in PDFs of abnormal situations P AN The probability of abnormal situations variable values fall in PDFs of normal situations e An infinitely small number x 1 MAP weight x 2 FAP weight P SM…”

Section: Discussionmentioning

confidence: 99%

Optimization of process alarm thresholds: A multidimensional kernel density estimation approach

Zhou

2014

Process Safety Progress

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Rationality of alarm systems enormously impacts safety and economic performances of process plants, which definitely demands for process alarm threshold optimization. In this work, first, we analyze the assignable causes of missed alarms and false alarms from the probability theory perspectives before formulating corresponding calculation metrics based on Bayesian Inference. Then, we minimize missed alarm probability (MAP) and false alarm probability (FAP) in a multidimensional space, where the kernel density estimation method is invoked to estimate joint probability density functions using process historical data. Mathematical models associated with multivariable process alarm threshold optimization are established on the basis of density functions, and gradient descent algorithms are employed to achieve advisable alarm thresholds. An industrial application shows that this approach effectively reduces MAP of the plant, as well as lowers FAP to a relatively reasonable level.

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Section: Discussionmentioning

confidence: 99%

Optimization of process alarm thresholds: A multidimensional kernel density estimation approach

Zhou

2014

Process Safety Progress

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“…In Izadi et al (2009) the results of an study considering alarm management in diagnosis systems of large plants are outlined. Between 36 and 48 average alarms per hour are typical values for alarm systems in industrial environment.…”

Section: Algorithm 1 Evaluator Algorithmmentioning

confidence: 99%

Fault detection and isolation in manufacturing systems with an identified discrete event model

Roth

Schneider

Lesage

et al. 2012

International Journal of Systems Science

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“…It occurs when the frequency of alarm annunciations is so high that it exceeds the operators capability of understanding the situation. This creates a dangerous situation where the operator might overlook critical alarms that could lead to significant downtime, irreversible damage or even loss of life [10].…”

Section: Introductionmentioning

confidence: 99%

Validation of similarity measures for industrial alarm flood analysis?

Fullen

Schüller

Niggemann

2018

IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency

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Abstract. The aim of industrial alarm flood analysis is to assist plant operators who face large amounts of alarms, referred to as alarm floods, in their daily work. Many methods used to this end involve some sort of a similarity measure to detect similar alarm sequences. However, multiple similarity measures exist and it is not clear which one is best suited for alarm analysis. In this paper, we perform an analysis of the behaviour of the similarity measures and attempt to validate the results in a semi-formalised way. To do that, we employ synthetically generated floods, based on assumption that synthetic floods that are generated as 'similar' to the original floods should receive similarity scores close to the original floods. Consequently, synthetic floods generated as 'not-similar' to the original floods are expected to receive different similarity scores. Validation of similarity measures is performed by comparing the result of clustering the original and synthetic alarm floods. This comparison is performed with standard clustering validation measures and application-specific measures.

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An Introduction to Alarm Analysis and Design

Cited by 94 publications

References 4 publications

Optimization of process alarm thresholds: A multidimensional kernel density estimation approach

Optimization of process alarm thresholds: A multidimensional kernel density estimation approach

Fault detection and isolation in manufacturing systems with an identified discrete event model

Validation of similarity measures for industrial alarm flood analysis?

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