Publication2025
Enhanced incipient fault identification in underground distribution cable implementing Random Forest classifier
The record
- Type
- Publication
- Date
- 1 July 2025
- Level
- National
- Journal
- Microsystem Technologies
- Volume
- 31
- Issue
- 7
- Pages
- 1763-1773
- Published
- 1 July 2025
- DOI
- 10.1007/s00542-024-05778-w
- Scopus ID
- 85206645364
- ISSN
- 09467076
- Authors
- 4
- Open access
- No
- Citations
- 7
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
7 Ensure access to affordable, reliable, sustainable and modern energy for all
Machine learning approach using Random Forest classifier with discrete wavelet transform achieves 99.06% accuracy in detecting incipient faults in underground distribution cables, improving electrical infrastructure reliability.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Application of advanced algorithms for industrial infrastructure diagnostics demonstrates research innovation in fault detection technology and machine learning applications in power systems.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2.4 Infrastructure and connectivity
Enhanced fault detection in distribution infrastructure strengthens connectivity and power system reliability essential for rural and urban infrastructure resilience across India.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “Enhanced incipient fault identification in underground distribution cable implementing Random Forest classifier”. Kortex.