From performance to prediction: extracting aging data from the effects of base load aging on washing machines for a machine learning model
Bibliographic record
Abstract
The standard testing of washing machine performance faces reliability challenges, which stem from the uncontrollable degradation of base load. This study provided a quantified standard for base load age to enhance the reliability and stability of the washing machine performance testing process. First, the impact of base load aging on the cleaning performance and water extraction performance was investigated. Simultaneously, the changes in reflectance of the base load were recorded. Then, MLR and ANN models were developed using the remaining moisture content and reflectance of the base load to predict base load age. It was found that the water extraction performance is more easily affected by the aging of the base load than the cleaning performance. In addition, ANN has better performance in predicting base load age, resulting in an R c 2 of 0.978, RMSEC of 9.295 h, and R cv 2 of 0.972 h, RMSECV of 10.639 h for the front-loading washing machine, and R c 2 of 0.958, RMSEC of 12.948 h, and R cv 2 of 0.940, RMSECV of 15.572 h for the top-loading washing machine. This study establishes a theoretical foundation for optimizing base load age regulation. Further study could expand the sample size to enhance the robustness and generalizability of the proposed method.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".