Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN
Bibliographic record
Abstract
Owing to the symmetry of the rolling bearing structure and the rotating operation mode, the rolling bearing works in a complex environment. It is very easy to be submerged by noise and misdiagnosis. For the non-stationary signal in variable speed state, this paper presents a condition monitoring method based on deep belief network (DBN) optimized by multi-order fractional Fourier transform (FRFT) and sparrow search algorithm (SSA). Firstly, the fractional Fourier transform based on curve feature segmentation is used to filter the fault vibration signal and extract the fault feature frequency. Then, the fault features are input into the SSA-DBN model for training, and the bearing fault features are classified, identified, and diagnosed. Finally, the rotating machinery fault simulator in the laboratory of Ottawa University is taken as the practical application object to verify the effectiveness of the method. The experimental results show that the proposed method has higher recognition accuracy and stronger stability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".