Clustering analysis and recognition of acoustic emission signals as failure precursors in coal samples
Bibliographic record
Abstract
Real-time monitoring and precursor identification of coal failure are crucial for providing early warnings of coal and rock dynamic disasters in coal mines. In this study, the acoustic emission (AE) response characteristics and crack evolution of coal failure precursors were analyzed under laboratory conditions. The AE signals of coal failure were classified using a two-step clustering method. Precursory AE signals were extracted and analyzed, followed by their identification using a support vector machine (SVM) algorithm. The results showed that Type V AE signals first appeared near the peak stress and exhibited distinctive features: low amplitude (hundreds of mV), long duration (hundreds of thousands of μs), large counts (tens of thousands), and high energy (tens of thousands of mV * ms). Type V AE signals were selected as the precursory signals of coal failure. An SVM model was subsequently trained to learn and identify these signals, achieving an average precision of 95.83%. The times when the SVM model first recognized the failure precursor AE signals of coal samples 1 and 2 were 19.442 s and 11.5686 s earlier than their peak failure times, respectively. The research results provide a valuable reference for using AE to identify coal failure precursors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".