Ensemble Empirical Mode Decomposition-Based Teager Energy Spectrum for Bearing Fault Diagnosis
Bibliographic record
Abstract
Periodic impulses in vibration signals and its repeating frequency are the key indicators for diagnosing the local damage of rolling element bearings. A new method based on ensemble empirical mode decomposition (EEMD) and the Teager energy operator is proposed to extract the characteristic frequency of bearing fault. The signal is firstly decomposed into monocomponents by means of EEMD to satisfy the monocomponent requirement by the Teager energy operator. Then, the intrinsic mode function (IMF) of interest is selected according to its correlation with the original signal and its kurtosis. Next, the Teager energy operator is applied to the selected IMF to detect fault-induced impulses. Finally, Fourier transform is applied to the obtained Teager energy series to identify the repeating frequency of fault-induced periodic impulses and thereby to diagnose bearing faults. The principle of the method is illustrated by the analyses of simulated bearing vibration signals. Its effectiveness in extracting the characteristic frequency of bearing faults, and especially its performance in identifying the symptoms of weak and compound faults, are validated by the experimental signal analyses of both seeded fault experiments and a run-to-failure test. Comparison studies show its better performance than, or complements to, the traditional spectral analysis and the squared envelope spectral analysis methods.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".