Feature Extraction Method of Ball Mill Load Based on Adaptive Variational Mode Decomposition and Improved Power Spectrum Analysis
Bibliographic record
Abstract
Abstract The real-time working conditions of the ball mill in the grinding process are complicated, which makes it difficult to accurately obtain the internal load status of the ball mill. In this paper, the energy difference between the original cylinder vibration signal and the intrinsic mode function is proposed as the evaluation parameter of the adaptive variational mode decomposition (VMD) layer number, and a new autocorrelation function is constructed. The intrinsic mode function is processed by introducing the energy centrobaric method of the Nuttall self-convolution window. Accordingly, a ball mill load feature extraction method based on adaptive VMD and improved power spectrum estimation is proposed, and the ball mill load identification system based on LabVIEW is developed. The number of layers of intrinsic mode function could be adaptively determined. And the algorithm’s ability to resist mode aliasing and false components of this method is improved, which improves the accuracy of ball mill load detection. The measured results show that internal load features of ball mill during the grinding process are effectively extracted, and the mill load status is accurately identified, which provide an accurate and reliable basis for grinding optimization control and efficiency.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".