SpecCSAM: a prior-knowledge embedding deep learning fault diagnosis method for unknown working conditions
Bibliographic record
Abstract
Although deep learning has exhibited promising performance in the field of fault diagnosis, most current methods suffer from performance degradation under variable working conditions. Transfer learning is commonly used to address this problem, which assumes that a plenty of unlabeled data or a few labeled data can be obtained in target domains. However, in industrial scenarios, there are a number of unknown working conditions with the lack of data, leading to the failure of transfer learning. Therefore, this paper presents a prior-knowledge embedding deep learning fault diagnosis method, SpecCSAM to overcome this issue. Only the samples under the source working condition are utilized for model training, without using any data under unknown working conditions. Firstly, based on the bearing fault mechanism and spectrum analysis, a novel data preprocessing method is introduced to construct a spectrum feature matrix (SFM). This method highlights the features that are insensitive to the domain shift, which can effectively enhance the fault diagnosis performance under unknown working conditions. Secondly, in the feature extraction module, a fusion-encoder based on the multi-branch self-attention mechanism (SAM) is employed to capture the high-dimensional fault features in SFM. Based on this innovative SAM, it is capable of mining multi-scale correlation features. To demonstrate the superiority of the proposed method, experiments were carried out on the Case Western Reserve University dataset and the self-built dataset. The average accuracies under unknown working conditions reached 99.50 and 99.28%, respectively. Combined with other experimental results, the proposed method demonstrated excellent performance in multiple tasks under unknown working conditions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".