Cross-condition fault diagnosis method for hydraulic systems based on domain adaptation and ensemble learning
Bibliographic record
Abstract
In response to the challenge of low fault diagnosis accuracy for hydraulic components in cross-condition fault diagnosis of multi-sensor fused hydraulic system using deep domain adaptation method, a method based on domain adaptation and ensemble learning is proposed. This method employs convolutional neural network as the primary architecture to recognize hydraulic signals collected by each sensor. It aligns the features of samples from source domain and target domain using kernel maximum mean discrepancy and central moment discrepancy. Additionally, a dual-attention mechanism is applied to select features, and the domain adaptation models trained for each sensor are integrated. This ensemble learning approach aims to diagnose faults in target domain hydraulic components across varying working conditions. Experimental validation is conducted using data from a hydraulic cooling system testbed and simulated data from an underwater robot hydraulic system. The method demonstrates higher accuracy in cross-condition fault diagnosis of hydraulic systems compared to other domain adaptation methods.
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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".