Machine Status Tracking Using Vibration via Sparse Sampling and Without Reconstruction
Bibliographic record
Abstract
One of the challenges in monitoring machinery vibration is handling the huge amount of data that must be transmitted, stored, and analyzed before transforming it into useful information. Many existing works aim at devising efficient and effective data reduction schemes to minimize this data load. Unfortunately, many of these techniques require the reconstruction of data, even in its reduced form, before proceeding with analysis. This is true even for compressive sampling (CS), the current state-of-the-art method for reducing sampling and data transfer volumes, as the vibration signal must be reconstructed before analysis can be applied. In this work, we capitalize on the nature of manufacturing machines, where vibrations often remain stationary over time and only change when the machine status changes. As such, we propose the use of a multilayer neural network and k-nearest neighbor (kNN) method to analyze the sparsely and randomly sampled data and subsequently identify the machine status without the need for data reconstruction. Experimental results demonstrate that the proposed solution can track a machine's status with an accuracy of 99.88%, using only 15% of the vibration data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".