Fault Diagnosis of Gear Pump Based on Cyclic Impulse Characteristic Decomposition
Bibliographic record
Abstract
The information of gear fault features is contained in vibration signals with the form of periodic impulse. Traditional signal decomposition methods mostly are suitable for AM-FM signals. However, when dealing with gear fault vibration signals, the gear impulse feature information is difficult to be accurately extracted from the noise by these methods. Therefore, the cyclic impulse characteristic decomposition (CICD) method is proposed in this article. Firstly, based on the advantage of the periodicity performance of discrete Fourier transform (DFT) matrix, the impulse cycle period is estimated by constructing the cyclic impulse dictionary matrix. Second, as Goertzel filtering method can parallely extract the amplitude information contained in scattered frequency point within different frequency band, and the bilateral spectral amplitudes obtained by this method are reconstructed to get the cyclic pulse characteristic components (CPCCs). The analyzing results of simulation and experiment signals demonstrate that CICD can accurately separate the fault impulse information of gear, as well as has better antinoise ability compared with other methods.
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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 | 0.001 |
| 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".