Hilbert-Huang Transform-Based Time-Frequency Analysis for Bearing Fault Diagnosis in Rotating Machinery
Bibliographic record
Abstract
Bearing faults are a leading cause of failures in industrial rotating machinery, making their early and reliable detection essential to reduce downtime and maintenance costs. A Hilbert-Huang Transform (HHT) based methodology is proposed for early bearing fault detection. The Empirical Mode Decomposition (EMD) isolates fault-related Intrinsic Mode Functions (IMFs), which are selected automatically using the Kurtosis-RMS (KR) criterion. Envelope Spectrum analysis is then applied to the reconstructed signal for diagnosis. The proposed method achieved 94% accurate IMF selection and over 85% fault identification accuracy across 18 signals from the CWRU, TianYau Wu, and MFPT databases. Compared with conventional FFT and Envelope Spectrum techniques, the approach improves early fault detection under noise and variable load conditions while reducing manual analysis time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".