A Cumulative Shock Model with Random Failure Threshold and a Change Point
Notice bibliographique
Résumé
Summary & Conclusions Reliability evaluation plays a pivotal role in the field of shock models. In such models, system failure occurs either when the damage inflicted by shocks surpasses their respective failure thresholds or when the time interval between shocks falls below a critical threshold. In the existing literature, the focus has often been solely on the magnitude of shocks, disregarding their sources. However, it is important to acknowledge that this approach may not always be suitable in real-world scenarios. Systems can experience random shocks originating from various sources, each with different probabilities. Different sources of shocks can have varying implications for a system. Therefore, it is more appropriate to consider the sources of shocks when modeling system reliability. Moreover, most research in the field of shock models utilizes fixed failure thresholds for systems. While fixed failure threshold models can provide a fundamental understanding of system reliability and performance, they may not always accurately reflect real-world conditions. Often, the designer and producer of a part or a system have many diverse users of their products. In practice, the critical threshold value can vary appreciably among users. In this case, a probabilistic, rather than a deterministic threshold value is more appropriate. On the other hand, in practical applications, a system may experience a shock with a stronger or weaker impact due to sudden changes in system behavior or environmental conditions. This represents a point in the data where there is a shift in the underlying distribution or generating process. This research has focused on the investigation of the reliability of a system characterized by random failure thresholds and a change point, which is exposed to cumulative shocks emanating from various sources. Our approach employs Phase-type (PH) distribution and its properties for reliability modeling. To demonstrate the efficiency and accuracy of the proposed model, we presented an illustrative example and conducted a comparative analysis with Monte Carlo simulations. It is imperative to note that accounting for real-world conditions, such as random failure thresholds, change points, and multiple shock sources, can significantly impact the reliability assessment. Engineers and designers stand to gain valuable insights from this model, which can aid in enhancing system reliability and safety and reducing costs throughout the system's lifetime.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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