Independent component analysis as applied to vibration source separation and fault diagnosis
Bibliographic record
Abstract
In health monitoring of complex mechanical systems such as aircraft engines there are many components whose diagnosis is of great interest for the industry. A conventional way to monitor these components is to collect vibration signals using accelerometers placed in their closest vicinity. However, due to some restrictions such as inaccessibility, it is not always practical to place the accelerometers as such. In many cases, pre-installed instrumentations are used, which are usually inadequate and placed on the carcass of the structure. Nevertheless, even if the accelerometers are positioned very close to the components, they would collect signals not just from one specific component but from other components as well. In this study, we sought to employ frequency-based independent component analysis (ICA) to recover the signals produced by components within a single complex system. In such a case, differences between “blind source separation” and vibration source separation are discussed. A new workaround for the permutation ambiguity encountered in the implication of ICA is proposed. Finally, in order to demonstrate the applicability of the new proposed approach, experimental results carried out on a test bed are presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".