An explainable framework for partial discharge detection in power cables through the integration of rough set theory and deep learning
Bibliographic record
Abstract
Recognition of partial discharge (PD) in power cables' insulation during the early stage of PD progression can trigger a set of preventive actions which significantly reduce power systems breakdowns and service interruptions. Thus, developing effective identification tools is of great importance to prevent such unwanted breakdowns. This paper proposes a hybrid framework based on a knowledge-based classifier and machine-driven feature extraction for PD identification in power cables. The classifier in this framework relies on rough set theory (RST) and a novel feature analysis algorithm (FAA) for PD and noise patterns discrimination. Unlike the ANN-based classifiers (black-box classifiers) that might generate untruthful outputs in case of the input sample and estimated function inconsistency, the RST as the prime classifier, will prevent any classification in the case of non-compliance with the classification rules and activate the FAA for classification using shallow and deep analyses. As a result, by overcoming black-box classifiers' limitations, a meager false detection rate and highly reliable outputs guided by the proposed white-box classifier can be attributed to this framework's main advantages. Also, to enhance classification performance, an improved denoising technique based on morphological filters incorporates a fully-connected autoencoder (FCA) to provide an abstract version of signals (synthetic features) for the classifier. Finally, the effectiveness of the proposed framework is demonstrated through extensive simulations and experimental validation, achieving higher accuracy than existing state-of-the-art approaches (99.42 %).
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 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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".