Fault Diagnostics with Legacy Power Line Modems
Bibliographic record
Abstract
We evaluate the use of legacy power line modems (PLMs) for fault diagnostics, and in particular, focus on short-circuit faults in underground power cables. Prior works have shown that broadband power line communication channel estimates that are computed within the PLMs can be used to gain insight into the health of underground cables. However, several legacy PLM chip-set implementations do not provide access to the estimated channel frequency response in its entirety. Therefore, to facilitate and accelerate a practical roll-out of a PLM-based diagnostics solution, we investigate if readily extractable parameters, such as the estimated signal-to-noise ratio values and/or the computed precoding matrices in case of multiple-input multiple-output (MIMO) transmission, provide sufficient indication into the cable health status. By extracting suitable features from this raw data, we show through simulations that our machine learning based automated cable diagnostics solution achieves satisfactory results in predicting faults, and near-perfect performance in fault identification.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".