A New Method of Multi-sensor Vibration Signals Data Fusion Based on Correlation Function
Bibliographic record
Abstract
In this paper to further enhance the measuring precision of vibration signal in the process of rotary machine fault diagnosis, a new data fusion method is presented on the basis of correlation function to determine the weighted value. The method doesn't require knowing the prior knowledge about sensors, and the weighted value of sensors can be determined on the basis of the correlation measure of real-time data tested in the fusion process. It gives greater weighted value to the greater correlation measure of sensor signals, and vice versa. Through simulation analysis, the dynamic adaptability, precision, and fault-tolerance of this method are better than the traditional weighted averaging method. Furthermore, this method has good performance of anti-jamming due to the correlation measures between noises and effective signals are usually small. Finally, rotor dynamics and integrated fault diagnosis system is taken as an example to verify the feasibility and advantages of this method with respect to multi-sensor vibration signal processing.
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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".