Fault feature extraction for centrifugal pump impellers via EMD and cyclic bispectral slicing
Bibliographic record
Abstract
The vibration signal of early centrifugal pump impeller faults is a nonlinear, non-Gaussian, non-steady-state signal with inherent periodicity. These characteristics complicate the accurate extraction of fault features. This study aims to explore a novel feature extraction method for centrifugal pump impeller fault vibration signals. This method leverages the adaptive characteristics of empirical mode decomposition (EMD) for multi-scale decomposition of the original vibration signal and separation of each intrinsic mode function (IMF). The cyclic bispectrum secondary slicing technique is introduced to perform high-order statistical purification of the noisy IMF, and the modulation frequency characterizing the fault is accurately isolated through optimized slicing parameters. In the analysis of actual centrifugal pump impeller vibration signals, this method effectively enhances the separability and anti-noise robustness of the modulation component. Furthermore, the extracted features are input into SVM, XGBoost, and 1D-CNN classification models, with test accuracies of 85.7%, 92.1%, and 95.7%, respectively, significantly outperforming the single-feature method.
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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 | 0.001 | 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".