In the traditional preventive maintenance policy, the periodic maintenance activities are scheduled on the basisof the a-priori information about the failure behaviour of the population which the component belongs to, byassuming a probability distribution function and by estimating the involved statistical parameters. On thecontrary, with the predictive approach, the maintenance activity is scheduled on the basis of the real degradationlevel of the component. So, it is possible to reduce the failure probability and, at the same time, to use thecomponent for almost all its useful life. For this reason, the predictive maintenance policy makes possible thereduction of the maintenance costs with respect to the preventive approach and it is particularly effective forthose components that must work with a high required degree of reliability in systems where failures can producedramatic consequences. To apply the predictive approach, it is necessary to monitor the component degradationbehaviour by using sensors. Nevertheless, before implementing a predictive policy, it is necessary to take intoaccount the costs and the uncertainty of the monitoring system. In this paper we compare by simulation theeffectiveness of the predictive maintenance policy with the traditional preventive one when the component mustoperate with a fixed reliability level. It is shown how the convenience of the predictive maintenance approachdepends both on the parameters characterizing the stochastic degradation process and on the uncertainty of themonitoring system. For the preventive policy the a-priori information on the population is considered while, forthe predictive one, this information is updated by a Bayesian approach using the data coming from themonitoring system.
|Numero di pagine||6|
|Stato di pubblicazione||Published - 2009|