IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Remote Fault Diagnosis System for Marine Power Machinery System

Remote Fault Diagnosis System for Marine Power Machinery System
View Sample PDF
Author(s): Chengqing Yuan (Reliability Engineering Institute, Wuhan University of Technology, China), Xinping Yan (Reliability Engineering Institute, Wuhan University of Technology, China), Zhixiong Li (Reliability Engineering Institute, Wuhan University of Technology, China), Yuelei Zhang (Reliability Engineering Institute, Wuhan University of Technology, China), Chenxing Sheng (Reliability Engineering Institute, Wuhan University of Technology, China)and Jiangbin Zhao (Reliability Engineering Institute, Wuhan University of Technology, China)
Copyright: 2013
Pages: 20
Source title: Diagnostics and Prognostics of Engineering Systems: Methods and Techniques
Source Author(s)/Editor(s): Seifedine Kadry (American University of the Middle East, Kuwait)
DOI: 10.4018/978-1-4666-2095-7.ch015

Purchase

View Remote Fault Diagnosis System for Marine Power Machinery System on the publisher's website for pricing and purchasing information.

Abstract

Marine power machinery parts are key equipments in ships. Ships always work in rigorous conditions such as offshore, heavy load, et cetera. Therefore, the failures in marine power machinery would badly threaten the safety of voyages. Keeping marine power machineries running reliably is the guarantee of voyage safety. For the condition monitoring and fault diagnosis of marine power machinery system, this study established the systemic condition identification approach for the tribo-system of marine power machinery and developed integrated diagnosis method by combining on-line and off-line ways for marine power machinery. Lastly, the remote fault diagnosis system was developed for practical application in marine power machinery, which consists of monitoring system in the ship, diagnosis system in laboratory centre, and maintenance management & maintenance decision support system.

Related Content

David Zelinka, Bassel Daher. © 2021. 30 pages.
David Zelinka, Bassel Daher. © 2021. 29 pages.
Narendranath Shanbhag, Eric Pardede. © 2021. 31 pages.
Marc Haddad, Rami Otayek. © 2021. 20 pages.
Reem A. ElHarakany, Alfredo Moscardini, Nermine M. Khalifa, Marwa M. Abd Elghany, Mona M. Abd Elghany. © 2021. 23 pages.
Sanjay Soni, Basant Kumar Chourasia. © 2021. 35 pages.
Lina Carvajal-Prieto, Milton M. Herrera. © 2021. 20 pages.
Body Bottom