Fault diagnosis for rotor based on multi-sensor information and progressive strategies
Bibliographic record
Abstract
Abstract Fault diagnosis is an effective tool to ensure safe operation of machinery and avoid serious accidents. As most currently used fault diagnosis methods usually employ mapping relationship established by training samples and their labels to achieve classification of testing samples, it is difficult for them to achieve fault diagnosis under the condition of incomplete training sample types. In addition, previous studies usually focus on feature extraction of single-channel vibration signal, which cannot get complete fault feature information. To solve the above problems, a progressive fault diagnosis method is investigated in this paper. First, the preliminary fault detection for the rotor is performed by studying reconstruction error of a sparse auto-encoder. Second, if a fault exists in the rotor, the outlier detection is implemented by the support vector data description method. Finally, if there are no outlier samples, the well -trained support vector machine is used to confirm the type of fault samples and complete the diagnosis. The performance of the proposed method was verified using the data obtained from a rotor laboratory bench. The types of rotor states investigated include normal, contact-rubbing, unbalance and misalignment. The experimental results verify the effectiveness and superiority of the proposed method in reducing the incidents of fault omission and fault misunderstanding.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".