Integrated Hilbert Huang technique for bearing defects detection
Bibliographic record
Abstract
Nowadays, the modern rotating machinery industries, such as automotive industries, aerospace turbo machinery, chemical plants, and power stations, are rapidly increasing in complexity and in their everyday operations, which demand the system to operate in higher reliability, extreme safety, and with lower cost of production and maintenance. Therefore accurate fault diagnosis of machine failure is vital to the operation and production departments. The majority of Machine imperfections and malfunctions have been related to bearings faults. Many researchers are still exploring to find suitable diagnosis strategies and techniques to detect incipient bearing faults. A new integrated Hilbert-Huang technique (iHT) is proposed in this paper for bearing fault detection. The iHT takes two processes; firstly: representative signatures are extracted and secondly the resulting selected features are employed to highlight defect-related impulses for incipient bearing fault detection. A novel Jarque-Bera analysis method is suggested to select most prominent characteristic feature functions and the signals are integrated to enhance the features of the condition related function. The effectiveness of the proposed iHT technique is verified by a series of experimental tests corresponding to different bearing health conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".