Sensor Measurements Only With Transmissibility Operators for Fault Detection in Flexible Beams
Bibliographic record
Abstract
In this work, we extend transmissibility-based fault detection to flexible structures and then use transmissibilities to estimate the system’s total number of independent excitations. Transmissibilities are mathematical representations of the relationship between the outputs of an underlying system. The input and output of transmissibility are both outputs of the underlying system. The article then examines a class of flexible structures for which neither the excitation signal nor the structural dynamics are known. We begin by examining a simulation model for testing a class of faults that are notoriously difficult to replicate experimentally, such as fatigue cracks. Following that, we will examine an experimental setup comprised of a flexible cantilever beam and multiple accelerometers. To illustrate the proposed algorithm, we consider a flexible beam with a change-of-stiffness fault. The transmissibility operators between the accelerometers are identified under normal beam conditions and then used for fault detection and estimating the number of excitation signals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".