Forthcoming and Online First Articles

International Journal of Strategic Engineering Asset Management

International Journal of Strategic Engineering Asset Management (IJSEAM)

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 Articles will not be published until the final proofs are validated by their authors.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.

International Journal of Strategic Engineering Asset Management (One paper in press)

Regular Issues

  • Strategic asset management health index for predicting power transformer health conditions   Order a copy of this article
    by Khamis Salim Al-Romaimi, David Baglee, Derek Dixon 
    Abstract: Asset management assists in operating electrical utilities at high performance and low cost. The Power Transformer Health Index (PTHI) is considered a good health condition evaluation and decision-making tool. PTHI is used to prioritise maintenance decisions, drive maintenance strategy, manage failure impact before it occurs, asset lifecycle planning, deferral big capitals, manage spare parts plan, and extend power transformer life. This paper presents the PTHI models investigation which was conducted on 4,324 transformer records using various artificial intelligent machine learning (ML) algorithms: artificial neural network (ANN), support vector machine (SVM), Naïve Bayes (NB), decision tree (DT), random forest (RF) and k-nearest neighbours (K-NN) in R programming language. Several evaluation metrics present comparable analyses using accuracy, sensitivity, specificity, and F1-score. According to the results, the SVM model was found applicable to local electrical utility transformers’ health condition assessment. The paper addressed integrating international best practices and AM into the HI model.
    Keywords: power transformer; asset management; health index; electrical utility; decision making; strategic investment planning; R programming language; Python programming language; machine learning.
    DOI: 10.1504/IJSEAM.2024.10064593
     

 

Return to top