An improved informative frequency band selection method for early fault detection of rolling element bearings
Bibliographic record
Abstract
Detecting rolling element bearing (REB) fault symptoms in real-world industrial settings is quite challenging due to external noise and interference from other components, especially in the early stages of fault. Therefore, enhancing the fault symptoms is crucial in practical applications. REBs usually exhibit two key characteristics: impulsiveness and cyclostationarity. However, most traditional band selection methods, which focus on maximizing these factors, are often ineffective due to impulsive noise and cyclostationary-type signal from other components, such as gears. This limitation arises from two sources: the demodulation technique and the chosen indicator for band selection. To address this limitation, first, an improved envelope spectrum with adaptive thresholding strategy is developed to extract the most pertinent bearing fault signatures. Building on this improved demodulation approach, a novel Multi-Harmonics Energy Index (MHEI) is then introduced to quantify the fault harmonic energy relative to background noise. This index facilitates the identification of the optimal demodulation band containing the most relevant fault information. A genetic algorithm optimization with a gridline-based initial population selection scheme is employed to select this optimal band automatically. The robustness and accuracy of the proposed method are validated through experimental tests incorporating inner race, outer race, roller, and compound faults in the presence of various noise types. The results demonstrate the method’s effectiveness even under challenging conditions with significant non-related impulsive and cyclostationary noise.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".