Online Unbalanced Rotor Fault Detection of an IM Drive Based on Both Time and Frequency Domain Analyses
Bibliographic record
Abstract
An 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 an early stage. Therefore, this paper presents an online condition monitoring based fault detection of an unbalanced rotor 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 signals are analyzed based on fast Fourier transform (FFT), Hilbert transform, envelope detection, and discrete wavelet transform (DWT) to detect the severity of the fault and its possible location under different load conditions. The DWT is used to extract the information from a signal over a wide range of frequencies. The Daubechies wavelet is selected for the healthy and faulty condition analysis of IM. It is found that the DWT can more precisely identify the fault as compared to the conventional FFT for a three-phase, two-pole, 0.246 kW, 60 Hz, 2950 r/min IM drive.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".