A Condition Monitoring Method via a New Signal Expansion Strategy for the Crystal Lifting and Rotating Mechanism
Bibliographic record
Abstract
The crystal lifting and rotating mechanism (CLRM) is the key motion device during the growth process of monocrystalline silicon. The operation state of CLRM has a direct influence on the quality of the monocrystalline silicon. Typically, the CLRM operates at a slow speed with subtle changes in state and inconspicuous signal features, which makes it hard to effective diagnosis the working condition. In this paper, a vibration-signal-based diagnosis method is proposed to monitor the operation status of the CLRM. Firstly, the vibration signals are collected by the sensor installed on the certain location of the CLRM. A signal expansion strategy is then designed to extent the original signal by integration of variational mode decomposition and canonical polyadic decomposition. The characteristic of the signal is enriched. After that, the features of the expanded signals are extracted using permutation entropy, followed by the K-nearest neighbor classification. Three representative experiments are conducted to verify the performance of the proposed method using different datasets, including the benchmark vibration signal dataset, signals acquired from the experimental platform established by our laboratory, and the signals acquired during the actual growth process of monocrystalline silicon.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".