A Joint Kurtosis-Based Adaptive Bandstop Filtering and Iterative Autocorrelation Approach to Bearing Fault Detection
Bibliographic record
Abstract
This paper reports a bearing fault detection method based on kurtosis-based adaptive bandstop filtering (KABS) and iterative autocorrelation (IAC). The interferences in the bearing signal can be removed by KABS filtering, whereas IAC is employed for noise reduction and signal enhancement. In the KABS method, two window-merging schemes are proposed to identify the frequency bands potentially containing interferences and to preserve those covering fault frequencies. Issues related to the selection of the number of autocorrection iterations are also discussed. The proposed method can be used for bearing fault detection in a low signal-to-noise ratio (SNR) and low signal-to-interference ratio (SIR) environment. The implementation of the proposed method does not require prior knowledge of the fault-excited resonant frequency. The performance of the proposed method has been examined by simulation analysis, with favorable comparisons to the Hilbert enveloping, energy operator, and spectrum kurtosis methods. Its effectiveness in bearing fault detection has also been demonstrated using experimental data.
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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".