Dynamic risk and reliability assessment for ship machinery decision making

K. Dikis, I. Lazakis, A. L. Michala, Y. Raptodimos, G. Theotokatos

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

The proposed research, through INCASS (Inspection Capabilities for Enhanced Ship Safety) FP7 EU funded research project tackles the issue of predictive ship machinery inspection by enhancing reliability and safety, avoiding accidents, and protecting the environment. This paper presents the development of Machinery Risk/Reliability Analysis (MRA). The innovation of this model is the consideration and assessment of components’risk of failure and reliability degradation by utilizing raw input data. MRA takes into account the system’s dynamic state change, concerning failure rate variation over time. The presented methodology involves the generation of Markov Chains integrated with the advantages of Bayesian Belief Networks (BBNs). INCASS project developed a measurement campaign, where real time sensor data is recorded onboard a tanker, bulk carrier and container ship. The gathered data is utilized for MRA DSS tool validation and testing. Following research involves components and systems interdependencies and feed the continuous dynamic probabilistic condition monitoring algorithm.
LanguageEnglish
Title of host publicationRisk, Reliability and Safety
Subtitle of host publicationInnovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016)
EditorsLesley Walls, Matthew Revie, Tim Bedford
Place of PublicationLondon
Pages685-692
Number of pages8
ISBN (Electronic)9781315374987
Publication statusPublished - 13 Sep 2016
EventEuropean Safety and Reliability Conference 2016 - University of Strathclyde, Glasgow, United Kingdom
Duration: 25 Sep 201629 Sep 2016
http://esrel2016.org/ (Link to conference web site)

Conference

ConferenceEuropean Safety and Reliability Conference 2016
Abbreviated titleESREL 2016
CountryUnited Kingdom
CityGlasgow
Period25/09/1629/09/16
Internet address

Fingerprint

Machinery
Ships
Reliability analysis
Decision making
Inspection
Condition monitoring
Bayesian networks
Markov processes
Containers
Accidents
Dynamical systems
Innovation
Degradation
Sensors
Testing

Keywords

  • predictive ship machinery inspection
  • reliability
  • ship safety
  • machinery risk/reliability analysis
  • Markov chains
  • Bayesian belief networks

Cite this

Dikis, K., Lazakis, I., Michala, A. L., Raptodimos, Y., & Theotokatos, G. (2016). Dynamic risk and reliability assessment for ship machinery decision making. In L. Walls, M. Revie, & T. Bedford (Eds.), Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016) (pp. 685-692). London.
Dikis, K. ; Lazakis, I. ; Michala, A. L. ; Raptodimos, Y. ; Theotokatos, G. / Dynamic risk and reliability assessment for ship machinery decision making. Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016). editor / Lesley Walls ; Matthew Revie ; Tim Bedford. London, 2016. pp. 685-692
@inproceedings{ddf42c9922f04deab653ef49cb4c4a0b,
title = "Dynamic risk and reliability assessment for ship machinery decision making",
abstract = "The proposed research, through INCASS (Inspection Capabilities for Enhanced Ship Safety) FP7 EU funded research project tackles the issue of predictive ship machinery inspection by enhancing reliability and safety, avoiding accidents, and protecting the environment. This paper presents the development of Machinery Risk/Reliability Analysis (MRA). The innovation of this model is the consideration and assessment of components’risk of failure and reliability degradation by utilizing raw input data. MRA takes into account the system’s dynamic state change, concerning failure rate variation over time. The presented methodology involves the generation of Markov Chains integrated with the advantages of Bayesian Belief Networks (BBNs). INCASS project developed a measurement campaign, where real time sensor data is recorded onboard a tanker, bulk carrier and container ship. The gathered data is utilized for MRA DSS tool validation and testing. Following research involves components and systems interdependencies and feed the continuous dynamic probabilistic condition monitoring algorithm.",
keywords = "predictive ship machinery inspection, reliability, ship safety, machinery risk/reliability analysis, Markov chains, Bayesian belief networks",
author = "K. Dikis and I. Lazakis and Michala, {A. L.} and Y. Raptodimos and G. Theotokatos",
note = "This is an Accepted Manuscript of a book chapter published by CRC Press in Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016) on 13/09/2016, available : https://www.crcpress.com/Risk-Reliability-and-Safety-Innovating-Theory-and-Practice-Proceedings/Walls-Revie-Bedford/p/book/9781138029972",
year = "2016",
month = "9",
day = "13",
language = "English",
isbn = "9781138029972",
pages = "685--692",
editor = "Lesley Walls and Matthew Revie and Tim Bedford",
booktitle = "Risk, Reliability and Safety",

}

Dikis, K, Lazakis, I, Michala, AL, Raptodimos, Y & Theotokatos, G 2016, Dynamic risk and reliability assessment for ship machinery decision making. in L Walls, M Revie & T Bedford (eds), Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016). London, pp. 685-692, European Safety and Reliability Conference 2016, Glasgow, United Kingdom, 25/09/16.

Dynamic risk and reliability assessment for ship machinery decision making. / Dikis, K.; Lazakis, I.; Michala, A. L.; Raptodimos, Y.; Theotokatos, G.

Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016). ed. / Lesley Walls; Matthew Revie; Tim Bedford. London, 2016. p. 685-692.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - Dynamic risk and reliability assessment for ship machinery decision making

AU - Dikis, K.

AU - Lazakis, I.

AU - Michala, A. L.

AU - Raptodimos, Y.

AU - Theotokatos, G.

N1 - This is an Accepted Manuscript of a book chapter published by CRC Press in Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016) on 13/09/2016, available : https://www.crcpress.com/Risk-Reliability-and-Safety-Innovating-Theory-and-Practice-Proceedings/Walls-Revie-Bedford/p/book/9781138029972

PY - 2016/9/13

Y1 - 2016/9/13

N2 - The proposed research, through INCASS (Inspection Capabilities for Enhanced Ship Safety) FP7 EU funded research project tackles the issue of predictive ship machinery inspection by enhancing reliability and safety, avoiding accidents, and protecting the environment. This paper presents the development of Machinery Risk/Reliability Analysis (MRA). The innovation of this model is the consideration and assessment of components’risk of failure and reliability degradation by utilizing raw input data. MRA takes into account the system’s dynamic state change, concerning failure rate variation over time. The presented methodology involves the generation of Markov Chains integrated with the advantages of Bayesian Belief Networks (BBNs). INCASS project developed a measurement campaign, where real time sensor data is recorded onboard a tanker, bulk carrier and container ship. The gathered data is utilized for MRA DSS tool validation and testing. Following research involves components and systems interdependencies and feed the continuous dynamic probabilistic condition monitoring algorithm.

AB - The proposed research, through INCASS (Inspection Capabilities for Enhanced Ship Safety) FP7 EU funded research project tackles the issue of predictive ship machinery inspection by enhancing reliability and safety, avoiding accidents, and protecting the environment. This paper presents the development of Machinery Risk/Reliability Analysis (MRA). The innovation of this model is the consideration and assessment of components’risk of failure and reliability degradation by utilizing raw input data. MRA takes into account the system’s dynamic state change, concerning failure rate variation over time. The presented methodology involves the generation of Markov Chains integrated with the advantages of Bayesian Belief Networks (BBNs). INCASS project developed a measurement campaign, where real time sensor data is recorded onboard a tanker, bulk carrier and container ship. The gathered data is utilized for MRA DSS tool validation and testing. Following research involves components and systems interdependencies and feed the continuous dynamic probabilistic condition monitoring algorithm.

KW - predictive ship machinery inspection

KW - reliability

KW - ship safety

KW - machinery risk/reliability analysis

KW - Markov chains

KW - Bayesian belief networks

UR - https://www.crcpress.com/Risk-Reliability-and-Safety-Innovating-Theory-and-Practice-Proceedings/Walls-Revie-Bedford/p/book/9781138029972

UR - http://esrel2016.org/

M3 - Conference contribution book

SN - 9781138029972

SP - 685

EP - 692

BT - Risk, Reliability and Safety

A2 - Walls, Lesley

A2 - Revie, Matthew

A2 - Bedford, Tim

CY - London

ER -

Dikis K, Lazakis I, Michala AL, Raptodimos Y, Theotokatos G. Dynamic risk and reliability assessment for ship machinery decision making. In Walls L, Revie M, Bedford T, editors, Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016). London. 2016. p. 685-692