Detrended fluctuation analysis of vibration signals for bearing fault detection
Why this work is in the frame
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Bibliographic record
Abstract
Rolling element bearings are widely used in various rotary machines. Accordingly, a reliable bearing fault detection technique is critically needed in industries to prevent these machines' performance degradation, malfunction, or even catastrophic failures. Although a number of approaches have been reported in the literature, bearing fault detection, however, still remains a very challenging task because most of the bearing fault related signatures are nonstationary. This paper presents the detrended fluctuation analysis (DFA) of vibration signals to tackle this technical challenge in bearing fault detection. The DFA offers the advantage over the traditional spectral analysis methods in that it can deal with nonstationary signals, and also its application does not rely on the selection of mother functions as the wavelet transform does. The effectiveness of the proposed technique is examined through a series of experimental tests, and the investigation results demonstrate that the faulty bearing conditions can be well detected by analyzing the power-law characteristics of the DFA.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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