Vibration Signal Features Prediction of GIS Equipment Based on Improved Slime Mold Optimization Algorithm Optimizing CNN-BiLSTM
Bibliographic record
Abstract
The change of vibration signal of GIS equipment can reflect the internal mechanical state.In order to improve the prediction accuracy of vibration signal characteristics of GIS equipment, this paper proposes an improved slime mold algorithm to optimize CNN-BiLSTM GIS vibration characteristics prediction method.First, the vibration characteristic parameters are extracted in the frequency domain based on the GIS historical vibration signal through Fourier transformation.Secondly, in order to enhance the feature utilization ability of BiLSTM model, 1D CNN is used to extract feature parameters; the differential evolution strategy is integrated into the slime mold optimization algorithm and the parameters such as the number of hidden layer neurons and learning rate of CNN-BiLSTM are optimized.Finally, an improved slime mold algorithm is established to optimize the GIS feature prediction model of CNN-BiLSTM.The experimental results show that the root mean square error and mean absolute percentage error of the DESMA-CNN-BiLSTM model are 1.7915 and 0.1317%, respectively, which are better than other methods in prediction accuracy.The improved algorithm proposed in this paper has strong global search ability and fast convergence speed.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".