Publication2025
Lube oil life prediction for heavy earth moving machinery (HEMM): A machine learning approach
- Industry, Innovation and Infrastructure
- Responsible Consumption and Production
- Partnerships for the Goals
- Thriving and Sustainable Economy
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- International — held abroad
- Journal
- Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering
- Published
- 1 January 2025
- DOI
- 10.1177/09544089241311377
- Scopus ID
- 85215129441
- ISSN
- 09544089
- Authors
- 4
- Open access
- No
- Citations
- 6
- International co-authorship
- Yes
- Co-author countries
- IN, SE
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
12.5 Substantially reduce waste generation through prevention, reduction, recycling and reuse
Artificial neural network model predicts lubricant lifespan for heavy earth moving machinery, reducing premature oil disposal and cutting operating costs by 19%.
- 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
AI-powered approach to equipment maintenance optimization enhances industrial operational efficiency and asset management.
- 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
17.6 Enhance North-South, South-South and triangular cooperation on science, technology and innovation, and knowledge sharing
Structural signal: co-authorship outside India.
- Rule-derived
- Emitted by a deterministic rule from structural signals rather than judged from the record’s text. The rule carries a version, so rule-derived and judged labels stay separable.
- Produced by
- sdg17-structural-v1, under kortex-publication-v1
Viksit Bharat @2047
vb-2 Thriving and Sustainable Economy
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 12 label. Artificial neural network model predicts lubricant lifespan for heavy earth moving machinery, reducing premature oil disposal and cutting operating costs by 19%.
- 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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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Lube oil life prediction for heavy earth moving machinery (HEMM): A machine learning approach”. Kortex.