Graphical Representation of Industrial Alarm Data

doi:10.3182/20100831-4-FR-2021.00033

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Graphical Representation of Industrial Alarm Data

Journal Article•DOI•

Graphical Representation of Industrial Alarm Data

Sandeep R. Kondaveeti¹, Iman Izadi, Sirish L. Shah¹, Tim Black•Institutions (1)

University of Alberta¹

01 Jan 2010-Vol. 43, Iss: 13, pp 181-186

TL;DR: This work demonstrates some novel visualization tools that can be used for assessing the performance of alarm systems in terms of effectively identifying nuisance alarms and their utility is illustrated using real industrial alarm data.

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Abstract: Alarms are important for safe and reliable operation of a process Ideally, every alarm that is presented to the operator requires an action Owing to the ease in implementing alarms, many modern day process plants have a large number of alarms configured in their alarm system Many of these alarms are set without proper rationalization resulting in the generation of nuisance alarms During process upsets, the volume of alarms presented to the operator is often too large to facilitate appropriate and timely actions This work demonstrates some novel visualization tools that can be used for assessing the performance of alarm systems in terms of effectively identifying nuisance alarms The utility of the developed tools is illustrated using real industrial alarm data

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Journal Article•DOI•

Improved correlation analysis and visualization of industrial alarm data.

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Fan Yang¹, Sirish L. Shah¹, Deyun Xiao², Tongwen Chen¹•Institutions (2)

University of Alberta¹, Tsinghua University²

01 Jul 2012-Isa Transactions

TL;DR: The Gaussian kernel method is applied to generate pseudo continuous time series from the original binary alarm data to reduce the influence of missed, false, and chattering alarms.

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Abstract: The problem of multivariate alarm analysis and rationalization is complex and important in the area of smart alarm management due to the interrelationships between variables. The technique of capturing and visualizing the correlation information, especially from historical alarm data directly, is beneficial for further analysis. In this paper, the Gaussian kernel method is applied to generate pseudo continuous time series from the original binary alarm data. This can reduce the influence of missed, false, and chattering alarms. By taking into account time lags between alarm variables, a correlation color map of the transformed or pseudo data is used to show clusters of correlated variables with the alarm tags reordered to better group the correlated alarms. Thereafter correlation and redundancy information can be easily found and used to improve the alarm settings; and statistical methods such as singular value decomposition techniques can be applied within each cluster to help design multivariate alarm strategies. Industrial case studies are given to illustrate the practicality and efficacy of the proposed method. This improved method is shown to be better than the alarm similarity color map when applied in the analysis of industrial alarm data.

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127 citations

Cites methods from "Graphical Representation of Industr..."

...If we use the method proposed in (Kondaveeti et al., 2010), the ASCM obtained is shown in Fig....
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...The alarm similarity color map (ASCM), which is specially designed for alarm data analysis (Kondaveeti et al., 2010), has proved to be an effective method for visualization....
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...There are two ways to capture correlation from alarm data: one is to employ Pearson’s correlation coefficients as done for continuous data (Yang et al., 2010a); the other is to introduce similarity measures based on binary data (Kondaveeti, 2010)....
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...This process has been illustrated in (Kondaveeti et al., 2010)....
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...There are many similarity measures available for binary data (Choi et al., 2010); hence if we compute the similarity measures based on original alarm data, we can choose one of them, for example Kondaveeti et al. (2010) have used the Jaccard similarity measure....
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Journal Article•DOI•

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K. Ahmed, Iman Izadi¹, Tongwen Chen², D. Joe³, T. Burton³ - Show less +1 more•Institutions (3)

Isfahan University of Technology¹, University of Alberta², Imperial Oil³

24 Jan 2013-IEEE Transactions on Automation Science and Engineering

TL;DR: A new analysis method is proposed to investigate similar alarm floods from the historic alarm data and group them on the basis of the patterns of alarm occurrences, and a case study on real industrial alarm data is presented to demonstrate the utility of the proposed analysis.

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Abstract: Flooding of alarms is a very crucial problem in process industries. An alarm flood makes an operator ineffective of taking necessary actions, and often risking an emergency shutdown or a major upset. In this work, the flooding of alarms is discussed based on the standards presented in ISA 18.2. A new analysis method is proposed to investigate similar alarm floods from the historic alarm data and group them on the basis of the patterns of alarm occurrences. A case study on real industrial alarm data is also presented to demonstrate the utility of the proposed analysis.

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108 citations

Journal Article•DOI•

Pattern matching of alarm flood sequences by a modified Smith–Waterman algorithm

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Yue Cheng¹, Iman Izadi, Tongwen Chen¹•Institutions (1)

University of Alberta¹

01 Jun 2013-Chemical Engineering Research & Design

TL;DR: A modified Smith–Waterman algorithm considering the time stamp information is proposed to calculate a similarity index of alarm floods, helpful for root cause analysis of historical floods and for incoming flood prediction.

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Abstract: Alarm flooding is one of the main problems in alarm management. Alarm flood pattern analysis is helpful for root cause analysis of historical floods and for incoming flood prediction. This paper deals with a data driven method for alarm flood pattern matching. An alarm flood is represented by a time-stamped alarm sequence. A modified Smith–Waterman algorithm considering the time stamp information is proposed to calculate a similarity index of alarm floods. The effectiveness of the algorithm is validated by a case study on actual chemical process alarm data.

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107 citations

Cites methods from "Graphical Representation of Industr..."

...This clustering method is also applied in alarm data correlation analysis to cluster related types of alarms (Kondaveeti et al., 2010; Yang et al., 2011)....
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Journal Article•DOI•

Estimation of alarm chattering

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Elham Naghoosi¹, Iman Izadi, Tongwen Chen¹•Institutions (1)

University of Alberta¹

01 Oct 2011-Journal of Process Control

TL;DR: A method to estimate the chattering index based on statistical properties of the process variable as well as alarm parameters can be used for developing analytical methods to optimally design alarm parameters for minimal chattering.

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63 citations

Journal Article•DOI•

Criteria-based Alarm Flood Pattern Recognition using Historical Data from Automated Production Systems (aPS)

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Birgit Vogel-Heuser¹, Daniel Schütz¹, Jens Folmer¹•Institutions (1)

Technische Universität München¹

01 Oct 2015-Mechatronics

TL;DR: An approach is proposed, which allows automatic identification of alarm floods by using criteria-based search strategies and historical notification logs of real industrial aPS are analyzed, regarding its ability to identify causally dependent notifications.

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57 citations

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References

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Journal Article•

Alarm systems: a guide to design, management and procurement

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J. Noyes¹•Institutions (1)

University of Bristol¹

01 Oct 1999-Engineering Management Journal

210 citations

"Graphical Representation of Industr..." refers methods in this paper

...More information about the classification is available in standard manuals (EEMUA [2007] and ISA [2009])....
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...According to these standards (EEMUA [2007] and ISA [2009]), in the steady state operation of a plant, the operator should not receive more than one alarm in a 10 minute interval....
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Journal Article•DOI•

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M-J. Lesot, Maria Rifqi, H. Benhadda

01 Dec 2008-International Journal of Knowledge Engineering and Soft Data Paradigms

TL;DR: The diverse forms similarity measures can take are examined, as well as their relationships and respective properties, and their semantic differences are highlighted and numerical tools to quantify these differences are proposed.

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Abstract: Similarity measures aim at quantifying the extent to which objects resemble each other. Many techniques in data mining, data analysis or information retrieval require a similarity measure, and selecting an appropriate measure for a given problem is a difficult task. In this paper, the diverse forms similarity measures can take are examined, as well as their relationships and respective properties. Their semantic differences are highlighted and numerical tools to quantify these differences are proposed, considering several points of view and including global and local comparisons, order-based and value-based comparisons, and mathematical properties such as derivability. The paper studies similarity measures for two types of data: binary and numerical data, i.e., set data represented by the presence or absence of characteristics and data represented by real vectors.

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144 citations

"Graphical Representation of Industr..." refers background in this paper

...These properties are explained in more detail in Lesot et al. [2009]. The Jaccard similarity factor lies between 0 and 1 and a high value of Sjac(X, Y ) indicates that the two alarms are closely related....
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...Informally, similarity measures are functions that quantify the extent to which objects resemble one another (Lesot et al. [2009])....
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...Several handbooks (Rothenberg [2009], Hollifield and Habibi [2007]) are available as ready references for maintaining an efficient alarm system....
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...Informally, similarity measures are functions that quantify the extent to which objects resemble one another (Lesot et al. [2009]). Considering the properties of the binary sequences, Jaccard similarity measure (Lesot et al. [2009]) becomes an obvious choice for calculating the similarity between two unique alarms....
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Journal Article•DOI•

An Introduction to Alarm Analysis and Design

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Iman Izadi, Sirish L. Shah¹, David S. Shook¹, Tongwen Chen¹•Institutions (1)

University of Alberta¹

01 Jan 2009-IFAC Proceedings Volumes

TL;DR: In this paper, an overview on alarm analysis and design is given Some of the reasons for false and nuisance alarms are discussed and a few solutions to reduce them are studied False alarm rate, missed alarm rate and detection delay trade-offs in alarm design are also discussed

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121 citations

Journal Article•DOI•

GAP: A graphical environment for matrix visualization and cluster analysis

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Han-Ming Wu¹, Yin-Jing Tien², Chun-Houh Chen³•Institutions (3)

Tamkang University¹, National Central University², Academia Sinica³

01 Mar 2010-Computational Statistics & Data Analysis

TL;DR: GAP is a Java-designed exploratory data analysis (EDA) software for matrix visualization (MV) and clustering of high-dimensional data sets and provides direct visual perception for exploring structures of a given data matrix and its corresponding proximity matrices, for variables and subjects.

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112 citations

"Graphical Representation of Industr..." refers methods in this paper

...GAP (Wu et al. [2010]) is a very useful tool for visualizing data matrices and similarity matrices in particular....
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Journal Article•DOI•

Application of Multivariate Statistics for Efficient Alarm Generation

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Sandeep R. Kondaveeti¹, Sirish L. Shah¹, Iman Izadi•Institutions (1)

University of Alberta¹

01 Jan 2009-IFAC Proceedings Volumes

TL;DR: This work demonstrates the advantages of monitoring the PCA based T 2 and Q statistic over individual process variables overindividual process variables to reduce the false alarm and missed alarm rates and reduces the detection latency which is one of the main drawbacks of monitoring a filtered variable.

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32 citations