2011
DOI: 10.2478/v10006-011-0036-8
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Adaptive modeling of reliability properties for control and supervision purposes

Abstract: Modeling of reliability characteristics typically assumes that components and systems fail if a certain individual damage level is exceeded. Every (mechanical) system damage increases irreversibly due to employed loading and (mechanical) stress, respectively. The main issue of damage estimation is adequate determination of the actual state-of-damage. Several mathematical modeling approaches are known in the literature, focusing on the task of how loading effects damage progression (e.g., Wöhler, 1870) for wear… Show more

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Cited by 5 publications
(2 citation statements)
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“…Usually, the extent of damage and damage location are ascertained simultaneously. Stage 4 is more complex and is often connected with fracture mechanics, structural assessment, and fatigue-life analysis, which demands probabilistic failure models based on the history of component failure (Dettmann and Soeffker 2011).…”
Section: Introductionmentioning
confidence: 99%
“…Usually, the extent of damage and damage location are ascertained simultaneously. Stage 4 is more complex and is often connected with fracture mechanics, structural assessment, and fatigue-life analysis, which demands probabilistic failure models based on the history of component failure (Dettmann and Soeffker 2011).…”
Section: Introductionmentioning
confidence: 99%
“…The most sophisticated level is Level 4, i.e., predicting the structural remaining lifetime and making decisions based on damage diagnosis results. Level 4 is generally associated with the fields of fracture mechanics, fatigue-life analysis, and structural assessment, which requires the combination of global structural models with local damage models to predict the evolution of damage, or the probabilistic failure models based on previously failed or damaged components [14], etc. Figure 1 demonstrates the procedure of damage identification for the four levels.…”
Section: Framework Of Vibration-based Damage Identificationmentioning
confidence: 99%