Intelligent risk prediction of storage tank leakage using an Ishikawa Diagram with probability and impact analysis

Favour Chidinma Ikwan; David Sanders; Malik Haddad; Mohamed Hassan Sayed; Peter Osagie Omoarebun; Mohamad Thabet; Giles Tewkesbury; Branislav Vuksanovic

doi:10.1007/978-3-030-55190-2_45

Intelligent risk prediction of storage tank leakage using an Ishikawa Diagram with probability and impact analysis

Favour Chidinma Ikwan, David Sanders, Malik Haddad, Mohamed Hassan Sayed, Peter Osagie Omoarebun, Mohamad Thabet, Giles Tewkesbury, Branislav Vuksanovic

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Intelligent probability and impact analysis are used with an Ishikawa diagram. Causes of tank leakage events are identified. Causes were ranked and weights assigned to show their relative importance in the diagram. A Risk Score for each category of causes is identified using probability and impact analysis. The application is explored to predict the risk of leakage in a storage tank. That risk can be mixed with real time data to create an intelligent system. Various methods can be used to predict future system states centred upon an analysis of trends within historic or past data. A simple human computer interface is presented to display the results by overlaying ‘Fail’ or ‘Warning’ states on a schematic of a storage tank. Important information can be flagged alongside conditions. As an example, a surface graph, representing the storage tank condition over a ten-week period is displayed. A continuing deterioration in the score connected with “lack of operating procedures” is presented.

Original language	English
Title of host publication	Intelligent Systems and Applications
Subtitle of host publication	Proceedings of the 2020 Intelligent Systems Conference (IntelliSys) Volume 3
Editors	Kohei Arai, Supriya Kapoor, Rahul Bhatia
Publisher	Springer
Pages	604-616
ISBN (Electronic)	978-3-030-55190-2
ISBN (Print)	978-3-030-55189-6
DOIs	https://doi.org/10.1007/978-3-030-55190-2_45
Publication status	Published - 25 Aug 2020
Event	Intelligent Systems Conference - London, United Kingdom Duration: 3 Sept 2020 → 4 Sept 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Volume	1252
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Intelligent Systems Conference
Abbreviated title	IntelliSys 2020
Country/Territory	United Kingdom
City	London
Period	3/09/20 → 4/09/20

Access to Document

10.1007/978-3-030-55190-2_45

Intelligent Risk Prediction
This is a post-peer-review, pre-copyedit version of an article published in Advances in Intelligent Systems and Computing. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-55190-2_45.
Accepted author manuscript (Post-print), 433 KB

Management and Business Decision Making
Haddad, M. & Sanders, D.
26/09/16 → 29/04/22
Project: Research

Cite this

Ikwan, F. C., Sanders, D., Haddad, M., Hassan Sayed, M., Omoarebun, P. O., Thabet, M., Tewkesbury, G., & Vuksanovic, B. (2020). Intelligent risk prediction of storage tank leakage using an Ishikawa Diagram with probability and impact analysis. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys) Volume 3 (pp. 604-616). (Advances in Intelligent Systems and Computing; Vol. 1252). Springer. https://doi.org/10.1007/978-3-030-55190-2_45

Ikwan, Favour Chidinma ; Sanders, David ; Haddad, Malik et al. / Intelligent risk prediction of storage tank leakage using an Ishikawa Diagram with probability and impact analysis. Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys) Volume 3. editor / Kohei Arai ; Supriya Kapoor ; Rahul Bhatia. Springer, 2020. pp. 604-616 (Advances in Intelligent Systems and Computing).

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abstract = "Intelligent probability and impact analysis are used with an Ishikawa diagram. Causes of tank leakage events are identified. Causes were ranked and weights assigned to show their relative importance in the diagram. A Risk Score for each category of causes is identified using probability and impact analysis. The application is explored to predict the risk of leakage in a storage tank. That risk can be mixed with real time data to create an intelligent system. Various methods can be used to predict future system states centred upon an analysis of trends within historic or past data. A simple human computer interface is presented to display the results by overlaying {\textquoteleft}Fail{\textquoteright} or {\textquoteleft}Warning{\textquoteright} states on a schematic of a storage tank. Important information can be flagged alongside conditions. As an example, a surface graph, representing the storage tank condition over a ten-week period is displayed. A continuing deterioration in the score connected with “lack of operating procedures” is presented.",

author = "Ikwan, {Favour Chidinma} and David Sanders and Malik Haddad and {Hassan Sayed}, Mohamed and Omoarebun, {Peter Osagie} and Mohamad Thabet and Giles Tewkesbury and Branislav Vuksanovic",

year = "2020",

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doi = "10.1007/978-3-030-55190-2_45",

language = "English",

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series = "Advances in Intelligent Systems and Computing",

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Ikwan, FC, Sanders, D, Haddad, M, Hassan Sayed, M, Omoarebun, PO, Thabet, M, Tewkesbury, G & Vuksanovic, B 2020, Intelligent risk prediction of storage tank leakage using an Ishikawa Diagram with probability and impact analysis. in K Arai, S Kapoor & R Bhatia (eds), Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys) Volume 3. Advances in Intelligent Systems and Computing, vol. 1252, Springer, pp. 604-616, Intelligent Systems Conference, London, United Kingdom, 3/09/20. https://doi.org/10.1007/978-3-030-55190-2_45

Intelligent risk prediction of storage tank leakage using an Ishikawa Diagram with probability and impact analysis. / Ikwan, Favour Chidinma; Sanders, David; Haddad, Malik et al.

Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys) Volume 3. ed. / Kohei Arai; Supriya Kapoor; Rahul Bhatia. Springer, 2020. p. 604-616 (Advances in Intelligent Systems and Computing; Vol. 1252).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Intelligent risk prediction of storage tank leakage using an Ishikawa Diagram with probability and impact analysis

AU - Ikwan, Favour Chidinma

AU - Sanders, David

AU - Haddad, Malik

AU - Hassan Sayed, Mohamed

AU - Omoarebun, Peter Osagie

AU - Thabet, Mohamad

AU - Tewkesbury, Giles

AU - Vuksanovic, Branislav

PY - 2020/8/25

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N2 - Intelligent probability and impact analysis are used with an Ishikawa diagram. Causes of tank leakage events are identified. Causes were ranked and weights assigned to show their relative importance in the diagram. A Risk Score for each category of causes is identified using probability and impact analysis. The application is explored to predict the risk of leakage in a storage tank. That risk can be mixed with real time data to create an intelligent system. Various methods can be used to predict future system states centred upon an analysis of trends within historic or past data. A simple human computer interface is presented to display the results by overlaying ‘Fail’ or ‘Warning’ states on a schematic of a storage tank. Important information can be flagged alongside conditions. As an example, a surface graph, representing the storage tank condition over a ten-week period is displayed. A continuing deterioration in the score connected with “lack of operating procedures” is presented.

AB - Intelligent probability and impact analysis are used with an Ishikawa diagram. Causes of tank leakage events are identified. Causes were ranked and weights assigned to show their relative importance in the diagram. A Risk Score for each category of causes is identified using probability and impact analysis. The application is explored to predict the risk of leakage in a storage tank. That risk can be mixed with real time data to create an intelligent system. Various methods can be used to predict future system states centred upon an analysis of trends within historic or past data. A simple human computer interface is presented to display the results by overlaying ‘Fail’ or ‘Warning’ states on a schematic of a storage tank. Important information can be flagged alongside conditions. As an example, a surface graph, representing the storage tank condition over a ten-week period is displayed. A continuing deterioration in the score connected with “lack of operating procedures” is presented.

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ER -

Ikwan FC, Sanders D, Haddad M, Hassan Sayed M, Omoarebun PO, Thabet M et al. Intelligent risk prediction of storage tank leakage using an Ishikawa Diagram with probability and impact analysis. In Arai K, Kapoor S, Bhatia R, editors, Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys) Volume 3. Springer. 2020. p. 604-616. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-55190-2_45

Intelligent risk prediction of storage tank leakage using an Ishikawa Diagram with probability and impact analysis

Abstract

Publication series

Conference

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Projects

Management and Business Decision Making

Cite this