Detection of Bearing Fault Detection From Heavily Contaminated Signals: A Higher-Order Analytic Energy Operator Method
Bibliographic record
Abstract
This paper reports a higher-order analytic energy operator (HO-AEO) approach to monitoring bearing health conditions from vibrations signals that are polluted by strong noise and multiple interferences. The proposed analytic energy operator (AEO) is formed using the raw signal, its Hilbert transform, and their derivatives. In analogy to the conventional energy operator (EO), it represents an alternative energy transformation. However, unlike the conventional EO, it exploits the information from both the real and imaginary parts of the analytic signal. It can also extract both the amplitude and frequency modulations and is thus well suited for detecting impulsive fault signature. The joint use of multiple higher-order AEOs can further offset noise effect. The built-in amplitude demodulation (AD) capability of the proposed HO-AEO eliminates the enveloping step required by most high-frequency resonance (HFR) methods. The method is simple and easy to implement. Our simulation and experimental results have demonstrated that the proposed method can effectively extract bearing fault signature in the presence of heavy noise and multiple vibration interferences. It has also been shown mathematically that the HO-AEO processed signal yields higher signal-to-interference ratio (SIR) than the conventional EO does. The simulation and experimental comparisons also indicate that the proposed method has much better noise and interference handling capabilities than the conventional EO.
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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".