Forthcoming and Online First Articles

International Journal of Quality Engineering and Technology

International Journal of Quality Engineering and Technology (IJQET)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Quality Engineering and Technology (1 paper in press)

Regular Issues

  • A new rotating machinery fault diagnosis method based on data driven and expert knowledge   Order a copy of this article
    by Zhenghui Li, Na Zhang, Feiya Lv, Jingya Yang, Rui Wang 
    Abstract: This paper presents data driven and expert knowledge method for diagnosing rotating machinery fault. The belief rule based (BRB) inference method is used to model the complex non-linear relationship between the abnormal vibration features of rotating machinery and its fault type. If the data of all features are used for diagnosis, then computation burden will be too large to realise real-time diagnosis. First, the inputs of BRB model are reduced and weighted through neural network based on data driven algorithm. The outputs of BRB model are fault types of rotating machinery. The belief rules activated by the inputs are combined by the evidential reasoning (ER) algorithm so as to obtain the fused belief structure about the fault, and then, the accurate diagnose result can be calculated from the fused result. The diagnosis results cannot only judge the fault type, but also give the probability of potential fault. The model parameters are open and interpretable. Finally, in the experiment of fault diagnosis of motor rotor, the effectiveness of the proposed method is illustrated.
    Keywords: fault diagnosis; rotating machinery; data driven; expert knowledge.
    DOI: 10.1504/IJQET.2023.10060526
     

 

Return to top