Identification of time-frequency maps of bearing faults based on hyperparameter optimization SSA-GoogleNet
Why this work is in the frame
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Bibliographic record
Abstract
In order to solve the problem of poor noise immunity of neural network in bearing fault detection and to meet the high demand for adaptive extraction ability of network features in the industrial field, this paper proposes a hyper-parameter optimization method to eliminate the error of human-set parameters. Aiming at the non-smoothness and non-linear characteristics of the bearing fault signal, the two-dimensional wavelet transform (2DWT) is used to extract the feature values of the vibration signal of the bearing fault, and it is proposed to use the CNN convolutional neural network (CNN) to train the fault model. Convolutional Neural Network (CNN) is proposed to be used for fault model training. Firstly, the 2D wavelet transform is applied to the bearing vibration signal to extract the time-frequency map of the vibration signal; secondly, the extracted time-frequency map is used as the training object of the convolutional neural network to train the model, and then multiple convolutional neural network frameworks are used for noise immunity test by adding Gaussian white noise to the original signal to construct the test framework, and then the framework with the best noise immunity is used as the base network; and then the CNN is used as the base network. Sparrow Search Algorithm (SSA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (GWO) to find the best parameters. Validated by the public dataset of Western Reserve University, the method can effectively improve the fault identification accuracy of the model and can get rid of the problem of poor network robustness.
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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 it