Fault Diagnostics with Legacy Power Line Modems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it