Combine harvester assembly fault diagnosis based on optimized multi-scale reverse discrete entropy
Bibliographic record
Abstract
An optimized multi-scale reverse discrete entropy (RDE, OMRDE) method for feature extraction is proposed to address the lack of effective feature extraction and detection methods for combine harvester assembly fault inspection. This method was used to extract the vibration signal features from the combine. A fault diagnostic method is designed to verify the efficiency of the associated methods. First, a comparative study of the RDE, multi-scale inverse DE (MRDE), and OMRDE methods was performed using simulated signals to verify the effectiveness of OMRDE. Second, the FSTPSO–VMD method was used to decompose the vibration signal of the combine assembly fault, and the OMRDE, MRDE, and fuzzy entropy were compared and analyzed. The actual feature extraction effect of the three entropy functions reached the highest classification accuracy (88.5%) after using OMRDE to extract features. Finally, a fusion feature set is constructed to further improve the classification accuracy, and the LSSVM classifier is optimized using FSTPSO. The analytical results show that the FSTPSO–LSSVM classifier constructed in this study adopts the fused feature with an accuracy of 93%, which is better than that of other common methods, and verifies the validity of the fault diagnostic model. Therefore, the performance of the OMRDE method proposed in this study is better than that of the MRDE. The proposed fault diagnostic model can accurately classify the fault detection of a combine harvester assembly.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".