An application of the modulus maxima distribution in machinery condition monitoring
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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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".