A mixture of experts approach for SHM measurement processing
Bibliographic record
Abstract
One of the greatest challenges in deploying structural health monitoring (SHM) systems is the need to manage the continuous stream of measurements obtained from tens or hundreds of installed sensors. In a practical system the analysis of these measurements must be performed in an automated and robust manner and be completed in real-time. As the first stage in this process, a neural computing based novelty detection system has been developed which is capable of modelling the basic behaviour of a structure and subsequently isolating noteworthy measurements. In this article we examine the trade-off between the system's need to adapt to normal changes in a structures behaviour over the long-term, with the need to maintain a reliable reference model so as to identify important events when they occur. It is demonstrated that extending the existing basic neural processing system, by introducing a 'mixture of experts' approach, can address the contradictory needs of adaptability and model stability. In addition, it is shown that this approach provides a means of incorporating detection of both short-term and long-term phenomena into a single integrated processing system.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".