A New Multisensor Partial Domain Adaptation Method for Machinery Fault Diagnosis Under Different Working Conditions
Bibliographic record
Abstract
Cross-domain fault diagnosis methods based on domain adaptation (DA) have been developed for single-sensor monitoring scenarios, in which the source and target domain fall into the same categories. However, in real-world situations, faults are usually mixed with each other, and the target health category is a subspace of the source health category, posing challenges to the current cross-domain fault diagnosis approaches. Additionally, with the increasing complexity of modern industrial systems, less attention has been paid to multisensor cross-domain diagnosis. To address this research gap, this paper proposes a new method of the multisensor partial DA fault diagnosis. First, the frequency information of multisensor measurements is obtained to fully utilize the fault information. Then, an improved partial DA method based on weighted domain adversarial network is used to distinguish the label space of the data samples. Finally, a joint optimization objective is constructed under the framework of partial transfer fault diagnosis, where two terms, namely, conditional entropy and adaptive uncertainty suppression, are further added to regularize the optimization objective. Through the proposed method, the positive transfer between shared common classes is guaranteed, and additionally, the passive influence resulted by outlier classes is prevented. Experimental results show that the proposed approach surpasses other popular methods for partial transfer fault diagnosis.
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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".