Online unbalanced rotor fault detection of an IM drive based on both time and frequency domain analyses
Bibliographic record
Abstract
Effective maintenance program provides incipient fault detection of rotating machines which reduces interim, unscheduled and excessive maintenance actions. By applying suitable online condition monitoring accompanied with signal processing techniques, machines irregularity can be detected at early stage. Therefore, this paper presents an online unbalanced rotor fault detection of an induction motor (IM). Characteristic features of motor current and vibration signals are analyzed in time domain as a fault diagnosis technique which is a key parameter to the fault threshold. Motor current and vibration signal analyses are also done based on Fast Fourier Transform (FFT), Hilbert Transform (HT) and Envelope Detection (ED) with low pass filter method to detect the severity of the fault and its possible location under different load conditions. The effectiveness of the electrical and mechanical fault detections based on FFT, HT and ED analyses are verified using the captured experimental data while the motor is running under different load conditions. The magnitudes of the spectral components are extracted for the pattern reorganization of the fault. Techniques are used under normal and unbalanced rotor conditions to a 3-phase, 2 pole, 0.246 KW, 60 Hz, 2950 rpm IM drive.
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".