Gear Fault Detection With the Energy Operator and its Variants
Bibliographic record
Abstract
Vibration analysis is currently the most efficient, non-invasive way to monitor the condition of the gears. Faults in gears can be of two distinct types, distributed or local. Many fault detection methods are effective for one type of fault or the other but not both. Also, many methods have the inconvenience that they are not simple and/or require initial information about the state of gear. In this paper, several methods are proposed with the objective of finding a filter-free, simple and efficient method for the detection of both types of faults. The calculus enhanced energy operator (CEEO), previously designed for fault detection in bearings, is proposed here for the first time on gears. Two new methods, the EO123 and EO23, based on the original energy operator are also proposed and evaluated. All the proposed methods are filter free, computationally simple and can handle a certain level of noise and interference. With the exception of low rotational frequencies of the gears, it is demonstrated via simulated and experimentally obtained signals that the CEEO method can handle noise better than the other proposed methods and that the EO23 method can handle interference better than the others. Different conditions determine the effectiveness of the methods.
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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".