An application of the modulus maxima distribution in machinery condition monitoring
Why this work is in the frame
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Bibliographic record
Abstract
Purpose To investigate wavelet modulus maxima distribution (MMD) in machinery condition monitoring and extract a parameter that can give a quantitative description of machinery‐operating status. Design/methodology/approach Signal decomposition technique is applied to extract gear motion signal and then wavelet transform modulus maxima are utilized to define fault growth parameter (FGP). Findings MMD were proposed and the distribution used to derive an EWMA statistic representing machinery fault growth. A comparison with other research works indicates better performance of this parameter. Practical implications This paper presents an innovative scheme for the machinery condition monitoring, on the basis of wavelet modulus maxima representation. The definition of MMD can be utilized to derive a parameter that describes the operating status of machinery. This parameter is load‐independent so that it demonstrates better performance when compared with other research works. Further, the MMD may be treated as input of condition classification system in the future work. Originality/value The idea for this paper stems from wavelet modulus maxima representation, whilst the application in vibration signal analysis is new. It was found that, by applying this approach, the occurrence of failure is correctly identified and the proposed EWMA FGP is independent of the load applied, which is a very important property in machinery condition monitoring and fault detection.
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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 | 0.001 | 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 it