Sensor Placement and Diagnosability Analysis at Design Stage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Adequate sensors are a necessary condition for fault diagnosability. Sensor placement for diagnosis task is to study where to put the sensors so that they are the minimal set to diagnose certain faults. This paper presents a method of sensor placement based on diagnosability analysis using the simulation model in the CAD environment. The fault signature matrix is determined by the projections of different operation modes on observable variables. The minimal sensor set for detecting faults and for discriminating the faults can be computed from the fault signature matrix. We also consider that values of exogenous variables are a condition for diagnosability. By introducing the concept of virtual sensors, faults can be detectable/ discriminable based on their signatures on virtual sensors. The advantages of this approach are that not only the minimal sensor set but also the conditions of causal scopes are obtained and the procedure is fully automated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it