Dynamic Modelling and Diagnosis of Transverse Crack and Rotor/Stator Rub in a Flexible Rotor System
Bibliographic record
Abstract
Abstract Due to unbalance present in a rotating machinery, fluctuating stresses are generated leading to the formation of transverse cracks in rotors. The cracks propagate with the passage of time and increase the amplitude of vibration. High vibration amplitudes can give rise to rotor/stator rubs. During the rubbing phase, the crack propagation gets enhanced due to inter-connected nature of these faults. If left unattended, these faults can cause the premature failure of machine components. Hence, there is a need to develop fault detection mechanisms based on the vibration response so that these faults can be diagnosed during initial stages. The effect of gravity and the presence of cracks significantly changes vibration characteristics of the rotor, which is thoroughly investigated in this research for a two-degrees-of-freedom Jeffcott rotor. It has been observed that during rubbing, high harmonics are excited. These harmonics are integer multiple of the rotor spin frequency. Similar type of the response is also observed due to the presence of a transverse rotor crack. It is difficult to distinguish the type of faults based on the steady state dynamic response only. Instead of working only on a steady state vibration response, the transient vibration response during coasting up of the rotor is considered. During coasting up of the rotor, high harmonics are excited for both the crack as well as rotor/stator rubbing. The excitation of higher harmonics starts at much earlier in the spectrogram of the vertical response for the cracked rotor compared to that of rubbing. This fact is used in the development of a fault diagnosis technique based on Short Time Fourier Transform of the vibration response. The proposed technique can efficiently distinguish different types of faults even if multiple faults coexist.
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 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".