An expert system for condition assessment of ACSR conductors
Bibliographic record
Abstract
Energized ACSR conductors of overhead power transmission lines gradually degrade due to the thermal impact and environmental pollution over a long period of time. This paper introduces an expert system designed to diagnose the condition of overhead power conductors. The expert system is able to predict the deterioration level using a fuzzy inference paradigm based on important environmental phenomena including ambient weather conditions and atmospheric pollution. Weather parameters at each tower location of a transmission line were extracted from a weather database. The estimated amount of pollutants at each tower location were derived from the historical data sets of the National Pollutant Release Inventory. Output of the proposed expert system is the spatial distribution of conductor deterioration grades that can be used to predict the remaining life of the energized conductor. Moreover, the spatial nature of this nondestructive diagnostic tool allows its use for better scheduling of line inspections, maintenance, and refurbishment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".