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ÌâÄ¿£ºFault Diagnosis for Marine Engine System Based on Complex Network
Cluster Method
×÷ÕߣºAiping Zhang, Guang Ren, and Baozhu Jia
ÂÛÎļ¯CA£ºAdvanced Materials Research Vols. 655-657 (2013) pp 801-805
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Accession number:       
20130916061971
        Title:        Fault diagnosis for marine engine system based on complex network cluster method
        Authors:         Zhang, Aiping1 ; Ren, Guang1 ; Jia, Baozhu1
        Author affiliation:        1 College of Marine Engineering, Dalian Maritime University, Dalian, China
        Corresponding author:         Zhang, A. (zhangaiping_86@yahoo.com.cn)
        Source title:        Advanced Materials Research
        Abbreviated source title:        Adv. Mater. Res.
        Volume:        655-657
        Monograph title:        Engineering Solutions for Manufacturing Processes
        Issue date:        2013
        Publication year:        2013
        Pages:        801-805
        Language:        English
        ISSN:         10226680
        ISBN-13:         9783037856482
        Document type:        Conference article (CA)
        Conference name:        2012 3rd International Conference on Advances in Materials and Manufacturing Processes, ICAMMP 2012
        Conference date:        December 22, 2012 - December 23, 2012
        Conference location:        Beihai, China
        Conference code:         95671
        Sponsor:        University of Wollongong, Australia; Northeastern University, China; University of Science and Technology Beijing; Hong Kong Industrial Technology Research Centre
        Publisher:        Trans Tech Publications, P.O. Box 1254, Clausthal-Zellerfeld, D-38670, Germany
        Abstract:        Fault diagnosis can be achieved by the cluster analysis process. We give a complete similarity measurement method and a cluster criterion function defined by modularity increment in complex network community detection. An agglomerative cluster method is proposed and the diagnosis rules are extracted to fault diagnosis for the marine engine system. Using the samples collected from self-manufactured marine engine room simulator, fault diagnosis simulation experiment is carried out to verify the algorithm performance. The results show that the method is accurate and less time-consuming, and is able to recognize the fault pattern which does not exist in the fault history data. © (2013) Trans Tech Publications, Switzerland.
        Number of references:        8
        Main heading:         Marine engines
        Controlled terms:         Cluster analysis  -  Complex networks  -  Failure analysis  -  Industrial engineering  -  Production engineering
        Uncontrolled terms:         Algorithm performance  -  Cluster  -  Cluster method  -  Criterion functions  -  Fault patterns  -  History data  -  Marine engine room simulators  -  Network cluster  -  Network communities  -  Similarity measurements
        Classification code:         921 Mathematics -  913.1 Production Engineering -  912.1 Industrial Engineering -  922 Statistical Methods -  723 Computer Software, Data Handling and Applications -  612 Engines -  421 Strength of Building Materials; Mechanical Properties -  722 Computer Systems and Equipment
        DOI:        10.4028/www.scientific.net/AMR.655-657.801
        Database:        Compendex
                Compilation and indexing terms, © 2013 Elsevier Inc.
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