Enabling the implementation of intelligent maintenance processes by efficient data integration

This paper reports on the results from two case studies concerning data integration for improving maintenance performance at a Distribution System Operator (DSO). The two case studies are: 1) earth fault work process and 2) inspection planning for secondary substations. In addition to reporting on experiences from the two case studies, the paper also discusses the potential gains and challenges for efficient data integration for DSOs in general. As the case studies exemplify, the potential for improving maintenance performance through efficient data integration is substantial.

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Publication Details

Journal
IET conference proceedings.
Published
2025-10-14
DOI
https://doi.org/10.1049/icp.2025.2153
Primary Topic
Engineering Diagnostics and Reliability
Type
article
Field-Weighted Citation Impact
0.00
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article

Enabling the implementation of intelligent maintenance processes by efficient data integration

Tom Ivar Pedersen, Maren Istad, Hilde J. L. Haugse, Line Bergfjord
IET conference proceedings.
Engineering Diagnostics and Reliability
article

Enabling the implementation of intelligent maintenance processes by efficient data integration

Tom Ivar Pedersen, Maren Istad, Hilde J. L. Haugse, Line Bergfjord
article en

Abstract

This paper reports on the results from two case studies concerning data integration for improving maintenance performance at a Distribution System Operator (DSO). The two case studies are: 1) earth fault work process and 2) inspection planning for secondary substations. In addition to reporting on experiences from the two case studies, the paper also discusses the potential gains and challenges for efficient data integration for DSOs in general. As the case studies exemplify, the potential for improving maintenance performance through efficient data integration is substantial.

IET conference proceedings.Vol. 2025(14)
SINTEF (NO), Bergenshalvøens Kommunale Kraftselskap (Norway) (NO)
Openalex Percentile: Top 67%
Engineering Diagnostics and Reliability
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