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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Strategic Asset Management Health Index for Predicting Power Transformer Health Conditions

Al-Romaimi, Khamis, Baglee, David and Dixon, Derek (2024) Strategic Asset Management Health Index for Predicting Power Transformer Health Conditions. Int. J. of Strategic Engineering Asset Management. ISSN 1759-9741 (In Press)

Item Type: Article


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 prioritize 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 4324 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 (KNN) 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.

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2024_IJSEAM-180586_AAV (002).pdf - Accepted Version
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More Information

Related URLs:
Depositing User: Khamis Al-Romaimi


Item ID: 17340
ISSN: 1759-9741
Official URL:

Users with ORCIDS

ORCID for Khamis Al-Romaimi: ORCID iD
ORCID for David Baglee: ORCID iD
ORCID for Derek Dixon: ORCID iD

Catalogue record

Date Deposited: 12 Feb 2024 17:13
Last Modified: 12 Feb 2024 17:15


Author: Khamis Al-Romaimi ORCID iD
Author: David Baglee ORCID iD
Author: Derek Dixon ORCID iD

University Divisions

Faculty of Technology > School of Engineering


Engineering > Electrical Engineering

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