Metodologia para predição de tempo de falha de máquinas e equipamentos baseada no monitoramento de vibração
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
The purpose of this work is to propose a methodology for predicting machine and equipment failure time based on vibration monitoring. Several measurements have been made in an experimental workbench, which was composed of a motor and five bearings. The objective of these measurements was to analyze the vibration data collected by applying specific signal analysis tools. Those specific tools consist on calculating vibroacoustic symptoms, whose temporal evolution has been evaluated. The objective was to verify which symptom (s) could be modeled according to the Weibull distribution, which is widely used to evaluate equipment lifetime. Among the symptoms evaluated, the following symptoms could be modeled according to the Weibull distribution: kurtosis, skewness, K4, TDA with high pass filter of 3000 Hz, energy level of the envelope with low pass filter in 100 Hz, energy level of the filtered envelope with bandpass filter between 1000 and 2000 Hz, filtered envelope energy level with bandpass filter between 2000 and 4000 Hz.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it