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Audiencia preliminar de allanamiento a la imputación (antes de la presentación

6. CAPÍTULO QUINTO

6.2 Audiencia preliminar de allanamiento a la imputación (antes de la presentación

An analytic prognostic model is introduced in this chapter that permits to predict the Remaining Useful Lifetime (RUL) of dynamic systems. This model considers the fatigue as a damage parameter and hence it is based on well known laws of damage like Paris' and Miner's laws. An index of degradation was derived that varies from zero to one. Our proposed model is based on the link between this index D and the crack length a. Failure is produced when a reaches a critical length aC. Hence, our model is given by a simple function

relating the instantaneous degradation to actual crack length as a measurement of actual damage.

Our aim is to evaluate the evolution of the system lifetime at each instant. For this purpose the degradation trajectories have been used in terms of cycle numbers or the time of operation. From these degradation trajectories, the RULs variations are deduced. The prognostic of a complex system can be deduced from the prognostic of its sub-systems when their damage laws are available.

To demonstrate the effectiveness of our model, two industrial examples have been considered in simulation in this chapter. These systems are the vehicle suspension systems and the petrochemical pipelines. For the vehicle suspension, three modes of road profiles are simulated. For the pipes, three types of pipes have been considered: unburied, buried, and offshore, and three modes of internal pressure are examined.

In such industrial systems, this model proves that it is very convenient and it provides a useful tool for a prognostic analysis. Moreover, it is less expensive than other models that need a large number of data and measurements.

In the following chapters we will enlarge this study by considering the nonlinear case of cumulated damage and the probabilistic influence of the basic parameters on degradation and on RULs evolution.

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CHAPTER III

ANALYTIC NONLINEAR PROGNOSTIC