Research on a rapid pre-delivery inspection strategy for harmonic reducers
Bibliographic record
Abstract
Harmonic reducers, which serve as integral components in the kinetic transmission of industrial robots, significantly influence the comprehensive performance of the equipment. Before integration into the distribution network, meticulous quality assurance is imperative. A novel strategy for rapid pre-delivery inspection (PDI) of harmonic reducers is proposed to address the inefficiency in detection caused by the complexity of the assembly process. The core concept centers around integrating variational mode decomposition (VMD) and multi-scale one-dimensional convolutional neural network (MS-1DCNN) based on acoustic pressure signals. Initially, a test rig is devised to capture one-dimensional raw acoustic pressure signals from harmonic reducers. Furthermore, sparrow search algorithm is employed to determine the optimal parameter combination [ K, α] for VMD. The acoustic pressure signals can be decomposed into intrinsic mode functions (IMFs) of diverse characteristic scales. Subsequently, effective IMFs are selected by Pearson correlation coefficient, and all such components are superimposed to form the reconstructed signals. Finally, the MS-1DCNN discriminant model is established for rapid PDI of harmonic reducers. The experimental results show that VMD-MS-1DCNN achieves an outstanding recognition accuracy of 99.45% in identifying harmonic reducers, offering a unique perspective and promising industrial applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".