Synchro-Reassigned Extracting Transform: An Effective Tool for Rotating Machinery Fault Diagnosis Under Varying Speed Condition
Bibliographic record
Abstract
Time-frequency analysis techniques offer valuable insights into the dynamic characteristics of non-stationary signals, making them suitable for diagnosing faults in rotating machinery operating under variable speed conditions. However, extracting meaningful features from time-frequency representations (TFRs) faces challenges due to energy spreading caused by complex modes and background noise. To address this issue, this paper introduces a novel technique called the Synchro-Reassigned Extracting Transform (SRET). The SRET uses instantaneous frequency and group delay operators to extract and reassign energy coefficients simultaneously in both the frequency and time directions, enhancing the sharpness of TFRs. Theoretical analysis reveals limitations of the synchroextracting transform (SET) when analyzing signals with both slowly and rapidly varying features, which the proposed SRET effectively overcomes. To optimize computational efficiency, the paper presents a discrete implementation algorithm for SRET. The effectiveness of SRET in analyzing time-varying signals and diagnosing bearing faults is demonstrated through simulations and two sets of bearing vibration data. Additionally, the application of SRET in processing vibration signals from a wind turbine gearbox highlights its potential for fault diagnosis in rotating machinery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".