An intelligent fault diagnosis system on ship machinery systems based on support vector machine principles

U. Ozturk; K. Cicek; M. Celik

An intelligent fault diagnosis system on ship machinery systems based on support vector machine principles

U. Ozturk, K. Cicek, M. Celik

Istanbul Technical University

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

2 Citations (Scopus)

Abstract

This study proposes an intelligent system to perform fault diagnosis actions in ship machinery systems. Considering the cost limitations, the main goal is to optimize the machinery system availability. The model takes the advantage of a classification tool based on support vector machines (SVM) principles. Statistical assumptions are considered for validity of the analysis. The test and statistical demonstration phases are also supported with the data, gathered from the specifically created operational scenarios in ship engine room simulator. Different faulty conditions other than the observed malfunction were inserted to the system in order to provided more realistic approach to simulate real world problem.

Original language	English
Title of host publication	Risk, Reliability and Safety
Subtitle of host publication	Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016
Editors	Lesley Walls, Matthew Revie, Tim Bedford
Publisher	CRC Press/Balkema
Pages	318
Number of pages	1
ISBN (Print)	9781138029972
Publication status	Published - 2017
Event	26th European Safety and Reliability Conference, ESREL 2016 - Glasgow, United Kingdom Duration: 25 Sept 2016 → 29 Sept 2016

Publication series

Name	Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016

Conference

Conference	26th European Safety and Reliability Conference, ESREL 2016
Country/Territory	United Kingdom
City	Glasgow
Period	25/09/16 → 29/09/16

Bibliographical note

Publisher Copyright:
© 2017 Taylor & Francis Group, London.

Cite this

Ozturk, U., Cicek, K., & Celik, M. (2017). An intelligent fault diagnosis system on ship machinery systems based on support vector machine principles. In L. Walls, M. Revie, & T. Bedford (Eds.), Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016 (pp. 318). (Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016). CRC Press/Balkema.

Ozturk, U. ; Cicek, K. ; Celik, M. / An intelligent fault diagnosis system on ship machinery systems based on support vector machine principles. Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016. editor / Lesley Walls ; Matthew Revie ; Tim Bedford. CRC Press/Balkema, 2017. pp. 318 (Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016).

@inproceedings{370d0d918e684e68a919167bd8dbc241,

title = "An intelligent fault diagnosis system on ship machinery systems based on support vector machine principles",

abstract = "This study proposes an intelligent system to perform fault diagnosis actions in ship machinery systems. Considering the cost limitations, the main goal is to optimize the machinery system availability. The model takes the advantage of a classification tool based on support vector machines (SVM) principles. Statistical assumptions are considered for validity of the analysis. The test and statistical demonstration phases are also supported with the data, gathered from the specifically created operational scenarios in ship engine room simulator. Different faulty conditions other than the observed malfunction were inserted to the system in order to provided more realistic approach to simulate real world problem.",

author = "U. Ozturk and K. Cicek and M. Celik",

note = "Publisher Copyright: {\textcopyright} 2017 Taylor \& Francis Group, London.; 26th European Safety and Reliability Conference, ESREL 2016 ; Conference date: 25-09-2016 Through 29-09-2016",

year = "2017",

language = "English",

isbn = "9781138029972",

series = "Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016",

publisher = "CRC Press/Balkema",

pages = "318",

editor = "Lesley Walls and Matthew Revie and Tim Bedford",

booktitle = "Risk, Reliability and Safety",

}

Ozturk, U, Cicek, K & Celik, M 2017, An intelligent fault diagnosis system on ship machinery systems based on support vector machine principles. in L Walls, M Revie & T Bedford (eds), Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016. Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016, CRC Press/Balkema, pp. 318, 26th European Safety and Reliability Conference, ESREL 2016, Glasgow, United Kingdom, 25/09/16.

An intelligent fault diagnosis system on ship machinery systems based on support vector machine principles. / Ozturk, U.; Cicek, K.; Celik, M.
Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016. ed. / Lesley Walls; Matthew Revie; Tim Bedford. CRC Press/Balkema, 2017. p. 318 (Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016).

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

TY - GEN

T1 - An intelligent fault diagnosis system on ship machinery systems based on support vector machine principles

AU - Ozturk, U.

AU - Cicek, K.

AU - Celik, M.

PY - 2017

Y1 - 2017

N2 - This study proposes an intelligent system to perform fault diagnosis actions in ship machinery systems. Considering the cost limitations, the main goal is to optimize the machinery system availability. The model takes the advantage of a classification tool based on support vector machines (SVM) principles. Statistical assumptions are considered for validity of the analysis. The test and statistical demonstration phases are also supported with the data, gathered from the specifically created operational scenarios in ship engine room simulator. Different faulty conditions other than the observed malfunction were inserted to the system in order to provided more realistic approach to simulate real world problem.

AB - This study proposes an intelligent system to perform fault diagnosis actions in ship machinery systems. Considering the cost limitations, the main goal is to optimize the machinery system availability. The model takes the advantage of a classification tool based on support vector machines (SVM) principles. Statistical assumptions are considered for validity of the analysis. The test and statistical demonstration phases are also supported with the data, gathered from the specifically created operational scenarios in ship engine room simulator. Different faulty conditions other than the observed malfunction were inserted to the system in order to provided more realistic approach to simulate real world problem.

UR - https://www.scopus.com/pages/publications/85016189939

M3 - Conference contribution

AN - SCOPUS:85016189939

SN - 9781138029972

T3 - Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016

SP - 318

BT - Risk, Reliability and Safety

A2 - Walls, Lesley

A2 - Revie, Matthew

A2 - Bedford, Tim

PB - CRC Press/Balkema

T2 - 26th European Safety and Reliability Conference, ESREL 2016

Y2 - 25 September 2016 through 29 September 2016

ER -

Ozturk U, Cicek K , Celik M. An intelligent fault diagnosis system on ship machinery systems based on support vector machine principles. In Walls L, Revie M, Bedford T, editors, Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016. CRC Press/Balkema. 2017. p. 318. (Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016).

An intelligent fault diagnosis system on ship machinery systems based on support vector machine principles

Abstract

Publication series

Conference

Bibliographical note

Other files and links

Fingerprint

Cite this