Rotating machinery fault diagnosis using dimension expansion and AntisymNet lightweight convolutional neural network
Bibliographic record
Abstract
Abstract Deep learning-based methods have made remarkable progress in the field of fault diagnosis for rotating machinery. However, convolutional neural networks are not suitable for industrial applications due to their large model size and high computational complexity. To address this limitation, this paper proposes the Antisym module and constructs AntisymNet, which is combined with dimension expansion algorithms for fault diagnosis of rotating machinery. To begin with, the original vibration signal of the rolling machinery is subjected to time-frequency transformations using the discrete Fourier transform and discrete wavelet transform. Subsequently, each transformed time-frequency signal is expanded in dimensions, resulting in two-dimensional matrix single channel images. These single channel images are then fused into RGB images to enhance the sample features. Finally, the proposed AntisymNet is utilized for recognizing and classifying the expanded signals. To evaluate the performance of AntisymNet, the MiniImageNet image dataset is employed as a benchmark, and a comparison is made with other state-of-the-art lightweight convolutional neural networks. Additionally, the effectiveness of the proposed fault diagnosis model is validated using the CWRU bearing dataset, Ottawa bearing dataset, and the hob dataset. The model achieves an impressive accuracy rate of 99.70% in the CWRU dataset, 99.26% in the Ottawa dataset, and an error rate of only 0.66% in the hob dataset. These results demonstrate the strong performance of the proposed fault diagnosis model.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".