Metodologia para predição de tempo de falha de máquinas e equipamentos baseada no monitoramento de vibração
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.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".