Simultaneous Fault Detection and Multivariable Adaptive Hybrid Control for Unbalanced Vibrations of Cracked Flexible Rotor Systems
Bibliographic record
Abstract
Abstract For the reduction of unbalanced vibrations in a multi-input and multi-output flexible rotor system with electromagnetic actuators (EAs), conventional adaptive feedforward controllers (AFFCs) are very sensitive for changes in rotor spin frequencies. Although frequency updating is used in these controllers, a small variation in the rotor spin frequency can completely reduce their effectiveness. An adaptive notch filter is used in this research for the frequency estimation. By using this external frequency estimation, the performance of the conventional AFFCs can be enhanced. During changes in the rotor spin frequency, fundamental harmonics of the flexible rotor are also excited. Their amplitude is much higher compared to steady-state unbalanced vibrations, which can accelerate the wear and tear of components of EAs. By using feedback controllers, the amplitude of these fundamental harmonics can be reduced significantly. In real rotors with flexible bearing supports, any looseness of bolts and presence of transverse cracks can change system parameters significantly. Multiple harmonics are generated corresponding to even single spinning speed of the rotor. Robust stability as well as performance can be achieved in the presence of uncertainty and rotor crack nonlinearities using feedback controllers designed by mu-synthesis. By using the multiharmonic hybrid control, the higher harmonics can be compensated efficiently in case of a crack in rotor systems. The fast Fourier transform of the control signal can indicate the presence of a transverse crack in an online manner. In this way, active vibration control as well as rotor crack fault detection can be done simultaneously.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".