Fault Diagnosis of Rolling Bearings Under Variable Working Conditions Based on EOS-CNN
Bibliographic record
Abstract
Under time-varying working conditions, the vibration signals of rolling bearings have problems such as amplitude changes, pulsating impact intervals, non-constant sampling phase, and signal noise pollution. These factors making it difficult for data-driven methods to learn the feature information of the signals and achieve intelligent diagnosis of rolling bearings under variable working conditions. To address this issue, a rolling bearing fault diagnosis method based on envelope order spectrum and convolutional neural network (EOS-CNN) is proposed in this paper. First, an improved variational mode decomposition (VMD) method is used to decompose the original signal to obtain the optimal Intrinsic mode functions (IMFs). Then, the EOS features of IMFs are extracted using the Hilbert order transform (HOT). Finally, a one-dimensional convolutional neural network (1D CNN)-based classifier is employed to classify the extracted features and achieve intelligent diagnosis of rolling bearing faults. The proposed method is verified by publicly available variable speed bearing dataset from Ottawa University. The experiments results show that the diagnostic accuracy of the proposed method with envelope order analysis-based fault features as input is much higher than that of the original signal as input, demonstrating the effectiveness of the proposed method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".