QUT ePrints

Development of an online condition monitoring system for slow speed machinery

Kim, Eric, Tan, Andy, Mathew, Joseph, & Yang, Bo-Suk (2010) Development of an online condition monitoring system for slow speed machinery. In Kiritsis, Dimitris, Emmanouilidis, Christos, Koronios, Andy, & Mathew, Joseph (Eds.) Proceedings of the 4th World Congress of Engineering Assets Management (WCEAM 2009), Springer-Verlag London Ltd, Ledra Marriott Hotel, Athens.

[img] Conference Paper (PDF 1MB)
Accepted Version.

    View at publisher

    Abstract

    One of the main challenges of slow speed machinery condition monitoring is that the energy generated from an incipient defect is too weak to be detected by traditional vibration measurements due to its low impact energy. Acoustic emission (AE) measurement is an alternative for this as it has the ability to detect crack initiations or rubbing between moving surfaces. However, AE measurement requires high sampling frequency and consequently huge amount of data are obtained to be processed. It also requires expensive hardware to capture those data, storage and involves signal processing techniques to retrieve valuable information on the state of the machine. AE signal has been utilised for early detection of defects in bearings and gears. This paper presents an online condition monitoring (CM) system for slow speed machinery, which attempts to overcome those challenges. The system incorporates relevant signal processing techniques for slow speed CM which include noise removal techniques to enhance the signal-to-noise and peak-holding down sampling to reduce the burden of massive data handling. The analysis software works under Labview environment, which enables online remote control of data acquisition, real-time analysis, offline analysis and diagnostic trending. The system has been fully implemented on a site machine and contributing significantly to improve the maintenance efficiency and provide a safer and reliable operation.

    Impact and interest:

    Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

    These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

    Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

    Full-text downloads:

    483 since deposited on 20 Mar 2011
    229 in the past twelve months

    Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

    ID Code: 40825
    Item Type: Conference Paper
    Keywords: Condition Monitoring, Diagnosis, Prognosis, Low Speed
    ISBN: 9781849960021
    Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300) > Automation and Control Engineering (091302)
    Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300) > Mechanical Engineering not elsewhere classified (091399)
    Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
    Past > Schools > School of Engineering Systems
    Copyright Owner: Copyright 2010 Springer-Verlag London Ltd.
    Copyright Statement: This is the author-version of the work. Conference proceedings published by Springer Verlag will be available via SpringerLink. http://www.springerlink.com
    Deposited On: 21 Mar 2011 09:48
    Last Modified: 29 Feb 2012 23:59

    Export: EndNote | Dublin Core | BibTeX

    Repository Staff Only: item control page