Low-Complexity Vibration-Based Condition Monitoring for Rolling Bearings: A Novel Instantaneous Amplitude–Frequency Approach
Bibliographic record
Abstract
In vibration-based condition monitoring (VBCM) of rotating machinery, bearing defects typically induce amplitude and frequency modulations, making joint instantaneous amplitude–frequency analysis effective for distinguishing healthy from abnormal vibration patterns. Recent advances in sensor technology have enabled the deployment of smart transducers for VBCM, allowing vibration signals to be processed directly at the measurement point. However, the limited processing capabilities of these transducers necessitate efficient methods for signal analysis and feature extraction. To address this need, we propose a low-complexity, parameterless method for VBCM of rolling bearings based on joint instantaneous amplitude–frequency analysis. The method leverages the instantaneous amplitude (envelope) and instantaneous frequency of the vibration signal to construct two novel representations: 1) instantaneous amplitude–frequency mapping (IAFM) and 2) instantaneous amplitude–frequency correlation (IAFC). These representations preserve temporal information and capture condition-specific energy–frequency variations. Consequently, five new defect-sensitive features are extracted to characterize these variations. Experimental results demonstrate excellent performance in detecting and diagnosing various fault types across different motor speeds, confirming the method’s effectiveness in distinguishing healthy and faulty states. Additionally, its moderate computational complexity makes the method well suited for real-time monitoring using smart transducers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".