Rotary-percussive drill bit condition prediction using traditional feature engineering and neural network-based feature extraction
Bibliographic record
Abstract
Condition monitoring of replaceable components in underground drill rigs using machine learning is a difficult task, as the operating conditions may vary considerably between each hole. To model this nuanced data with acceptable performance, feature extraction must be performed, either by field experts or using automated machine learning architectures. This work compares the use of traditional feature extraction techniques to neural network-based automatic feature extraction for a rotary-percussive underground hydraulic drill rig. A dataset was created for the purpose of predicting the condition of drill bits with tungsten carbide button inserts, and consists of both operational pressures, as well as signals from an accelerometer and a microphone. Two feature extraction approaches are compared using data collected under controlled operating conditions. The first approach uses traditional features including kurtosis, FFT features, and wavelet features. Feature selection and bit condition prediction are performed using a Random Forest model. The second approach uses neural networks to automatically extract features from raw data. Convolutional neural networks and long short-term memory networks are used in the automatic feature extraction approach. The traditional feature extraction approach is sufficient for binary classification of bit condition, while the automatic neural network-based feature extraction approach is superior when prediction is scaled up to a more complex multi-class problem.
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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 | 0.000 | 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) | 0.000 | 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".