Recent developments in the Logistic Engineering field are expanding the range of analysis for Operational Availability (Ao) from purely statistical models to include prognostic models. A necessary step in order to perform prognostic analysis is the study of system's components degradations. The degradation of a component is a case by case study, which characteristics that change significantly even inside the same component macro area (software, composites, mechanical parts etc.). The degradation models project over time the key factors of each component, eventually ending with the component failure (exceptions can be made for software). For those models the initial degradation state is an heavily influencing factor. Assuming that a component starts it's degradation process from a pristine state, relative to the value for which it is tested and conformed, introduces an error in the Remaining Useful Life assessment. Components used in systems have varying degrees of degradation at the t0 of the system, which may be greater or lesser than the assumed value. This happens cause conformity tests do have tolerances in the accepted values. Using the effective measurements derived from the conformity tests can improve the accuracy of the Remaining Useful Life (RUL) evaluation.
|Titolo:||Improving autonomic logistic analysis by including the production compliancy status as initial degradation state|
|Data di pubblicazione:||2016|
|Citazione:||De Francesco, E., De Francesco, E., De Francesco, R., Leccese, F., & Cagnetti, M. (2016). Improving autonomic logistic analysis by including the production compliancy status as initial degradation state. In 3rd IEEE International Workshop on Metrology for Aerospace, MetroAeroSpace 2016 - Proceedings (pp.371-375). New York : Institute of Electrical and Electronics Engineers Inc..|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|