Reliability and Resilience of Systems of Systems
Bibliographic record
Abstract
Systems of systems (SoSs), which are complex structures characterized by many interconnected entities performing different functions, pose new challenges to reliability engineers. Emergent behavior, coupling between different time scales, operation near instability limits, occurrence of extreme events, and dynamic failure propagation are characteristics that are not easily described with classical reliability engineering methods. The IEEE Reliability Society has, therefore, created a technical committee on systems of systems in order to advance the state of the art. The goals, results to date, and future plans of that committee are presented in this article. The case is made that beyond reliability, resilience should be a key framework for SoS assessment. Two use cases are described: a dual-function power station example from Hydro-Québec and a multimodal urban transit system example from the Washington Metropolitan Area Transit Authority (WMATA). Enhancing the resilience of critical infrastructures is also discussed. This article concludes with future directions, which include artificial intelligence (AI)/machine learning (ML), specifically graph neural networks (GNNs), as well as aggregation methods inspired by statistical physics.
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How this classification was reachedexpand
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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".