A rolling bearing fault diagnosis method based on the improved sparrow search algorithm optimized VMD and multi-scale convolutional neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To address the issues of low diagnostic accuracy in traditional rolling bearing fault diagnosis models and the ineffective extraction of spatial and temporal features from vibration signals, this paper proposes a rolling bearing fault diagnosis method based on the improved sparrow search algorithm optimized VMD and multi-scale convolutional neural networks. First, the improved sparrow search algorithm is employed to adaptively optimize the penalty parameter and mode count in variational modal decomposition (VMD). This achieves finer frequency band segmentation and effectively suppresses energy leakage, thereby yielding high quality frequency domain representations. Second, a multi-scale convolutional neural networks (MSCNN) is constructed, with feature level fusion implemented. Subsequently, a bidirectional long short-term memory networks (BiLSTM) is introduced to model the temporal dependencies of the fused features, enabling fault mode learning. A softmax layer is employed to achieve multi-class classification. Finally experimental results and comparisons based on the CWRU bearing dataset demonstrate the effectiveness of the proposed method in the rolling bearing fault classification task, providing significant application value for achieving efficient and reliable bearing fault detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it