Bibliographic record
Abstract
One of the inherent functions of an integrated vehicle health management (IVHM) system is the reasoning capability that is built on the knowledge of how the individual line replaceable units (LRU) and subsystems are functionally interconnected across the vehicle. Once known and mathematically represented, the IVHM system has the ability to utilize knowledge obtained from the individual LRU/subsystems to determine the overall health state and functional capabilities of the vehicle. This process must go beyond the basic diagnoses of the observed health condition of the isolated subsystems and their remaining functionality. The IVHM reasoning process described herein employs a hierarchical structure that accounts for the failure modes at the LRU level and can also determine the functional impact of those LRUs in terms of remaining functional/operational availability at the subsystem and vehicle levels. This type of architecture is also consistent with an enhanced fault isolation process in cases when fault indications result in ambiguous outcomes, providing additional information to help isolate the root cause and reduce the ambiguity group size. In addition, the IVHM reasoner must also attempt to provide estimates of the severity of the underlying fault/failure modes and the remaining useful life of the integrated systems when prognostic information is available. Thus, providing real-time vehicle health and remaining functionality information will be useful to operations and maintenance personnel for decision support. The following paper will present a framework and an associated aircraft system use case for performing hierarchical reasoning at the vehicle level based on individual subsystem health indicators. Within the presented architecture, a low level diagnostic reasoning engine seeks to classify fault/failure mode indications from raw sensor data or feature data processed by the subsystem specific modules within the monitoring system. Mid-level reasoning is employed to determine the overall functional capability of the constituent subsystems, specifically what are the implications of the detected failure modes on the functional availability of the subsystem. The vehicle-level reasoning will then “roll up” and quantify the true capability of the vehicle based on the health assessments from all underlying subsystems, taking into account their interconnections (failure mode propagation), criticality and redundancies.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".