Impulsive Vibrations Detection for Manufacturing Machines Using Machine Learning
Bibliographic record
Abstract
Manufacturing machines, especially those without computational capabilities, often require technicians to calibrate the machines to ensure the production line operates optimally. During the calibration process, there are times when the machine experiences transient faults, whose manual monitoring becomes unpractical and cost ineffective. In some cases, the machine’s vibrations have regular beats, but when faults occur, there will be impulsive vibrations. This work addresses these cases and proposes a novel approach to detect packaging faults through monitoring the vibration of the machine by detecting impulsive vibrations from a series of beat vibrations. The novelty of the proposed solution is that it does not require calibration or fine-tuning. It uses Neural Basis Expansion Analysis for Time Series (N-BEATS) to learn and predict the beat vibration tempo. It detects impulsive vibrations by comparing incoming vibrations with the predicted beat using Spearman’s rank correlation coefficient. Experimental results are promising, showing that the approach can achieve 97% accuracy in detecting impulsive vibrations.
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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".