Bearing system health condition monitoring using a wavelet cross-spectrum analysis technique
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rolling-element bearings are widely used in rotary machinery systems. Accordingly, a reliable bearing fault detection technique is critically needed in industries to prevent the machinery system's performance degradation, malfunction, or even catastrophic failures. Bearing fault detection, however, still remains a very challenging task because most of the bearing fault related signatures are non-stationary. In this paper, a wavelet cross-spectrum (WCS) technique is proposed to tackle the challenge of feature extraction from these non-stationary signatures for bearing fault detection. The vibration signals are first analyzed by a wavelet transform to demodulate primary representative features; the periodic features are then enhanced by cross-correlating the resulting wavelet coefficient functions over several contributive neighboring wavelet bands. A Jarque-Bera statistic index is suggested for the bandwidth selection. The effectiveness ofthe proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that the developed WCS technique is an effective signal processing approach for not only stationary but also non-stationary feature extraction and analysis, and it can be applied effectively for bearing fault detection.
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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 it