Bibliographic record
Abstract
David Rapos, M.A.Sc. Candidate Mechanical and Materials Engineering, Queen’s University, Kingston, Ontario, Canada Synopsis Approximately half of all operating costs in most industrial facilities are as a result of maintenance. With this in mind, providing appropriate maintenance to a machine, at the correct time, is vital to avoid unnecessary costs. Therefore, a cost effective method of diagnosing gear faults using non-contact magnetic shaft rotational position sensors is proposed. This method records and measures the dynamic transmission error over the course of each full gear rotation and allows for the identification of various gear fault types with just as much accuracy as other options (like encoders for example). Introduction ‘Dynamic Transmission Error’ is defined as the difference between the input and output shaft rotational positions as a function of time. A dynamic transmission error with a large and variable amplitude is the result of the output shaft lagging the input shaft to a larger or lesser extent as a function of rotational position. This variable relative rotational position may represent the presence of a gear fault. Traditional methods of fault diagnostics on gearboxes are conducted through vibration analysis of signals recorded using accelerometers. However, vibration analysis at times can prove to be a challenge due to sensor mounting difficulty, extremely complex raw signals, and interference due to high levels of noise in the measured signal. Methods and Results The testing was done on a 1:1 ratio steel gear set with the number of teeth being 32 or 16. The fault cases were intended to simulate radial (towards the shaft center) and tangential (across the tooth profile) root cracks similar to a previous student’s work which proved to have promising results on a plastic gear set [1]. For each fault case, the cracks were created on three separate gears to simulate progressive crack growth. Each of these gears experienced the same speed ramp profile for three separate loads. The non-contact magnetic rotational position sensors were placed parallel and in line with the input and output shafts. These sensors recorded both a cosine and sine signal (representing the rotational position of each shaft, but 90 degrees out of phase) for the input and output shaft. Using a two-input arctangent function found in MATLAB, both the cosine and sine signal were combined into one signal. This function yielded a result over the domain (-π, π] for both the input and output shaft position and ultimately allowed for the calculation of the dynamic transmission error. The units for dynamic transmission error are in millimeters and are a measure of the experienced pitch line delay. Figure 1 is an example of some of the preliminary results. The large dynamic transmission error spike located 0.05 seconds into the shaft cycle corresponds to the point in time when the faulted tooth was in the gear mesh.
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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".