# Řešení vybraných modelů s obnovou Radim Briš VŠB - Technical University of Ostrava (TUO), Ostrava, The Czech Republic

## Presentation on theme: "Řešení vybraných modelů s obnovou Radim Briš VŠB - Technical University of Ostrava (TUO), Ostrava, The Czech Republic"— Presentation transcript:

Řešení vybraných modelů s obnovou Radim Briš VŠB - Technical University of Ostrava (TUO), Ostrava, The Czech Republic radim.bris@vsb.cz

Contents Introduction Renewal process Alternating renewal process Models with periodical preventive maintenance Models with a negligible renewal period Alternating renewal models Conclusions Alternating renewal models with two types of failures

Introduction This paper mainly concentrates on the modeling of various types of renewal processes and on the computation of principal characteristics of these processes – the coefficient of availability, resp.unavailability. The aim is to generate stochastic ageing models, most often found in practice, which describe the occurrence of dormant failures that are eliminated by periodical inspections as well as monitored failures which are detectable immediately after their occurrence. Mostly numerical mathematical skills were applied in the cases when analytical solutions were not feasible.

Renewal process Random process is called renewal process. Let we call N t a number of renewals in the interval [0, t] for a firm t 0, it means From this we also get that S Nt t < S Nt+1 is called renewal function

Renewal process Renewal equation An asymptotic behaviour of a renewal of renewal function: function h(t) that is defined as renewal density. is renewal equation for a renewal density

Alternating renewal process A random process {S 1, T 1, S 2, T 2 …..} is then an alternating renewal process. X 1,X 2 …resp. Y 1,Y 2 …are independent non-negative random variables with a distr. function F(t) resp. G(t). Coefficient of availability K(t) (or also A(t) - availability) is h(x) is a renewal process density of a renewal {Tn} n=0, F(t) is a distribution function of the time to a failure, resp. 1 – F(t) = R(t) is reliability function. and asymptotic coefficient of availability is

Models with a negligible renewal period Poisson process: Gamma distribution of a time to failure: Using Laplace integral transformation we obtain: is k th nonzero root of the equation (s + λ) a = λ a For example for a = 4 nonzero roots are equal to: and a renewal density For example for a = 4 nonzero roots are equal to:

Models with a negligible renewal period Weibull distribution of time to failure: Using discrete Fourier transformation: where μ is an expected value of a time to failure: We can estimate in this way an error of a finite sum α > 0 is a shape parameter, λ > 0 is a scale parameter because a remainder is limited

Models with a negligible renewal period Weibull distribution:

Models with periodical preventive maintenance The probability P(t) (coefficient of unavailability) for May a device goes through a periodical maintenance. Interval of the operation τ C, (detection and elimination of possible dormant flaws). The period of a device maintenance … τ d F(t) is here a time distribution to a failure X. In the interval [0, τc+ τd) there is a probability that the device appears in the not operating state

Models with periodical preventive maintenance Exponential distribution of time to failure, τ d = 0: Coefficient of unavailability for Exponential distribution.

Models with periodical preventive maintenance Weibull distribution of time to failure, τ d = 0: Coefficient of unavailability for Weibull distribution. It is necessary for the given t and n, related with it which sets a number of done inspections to solve above mentioned system of n equations and the solution of the given system is not eliminated.

Alternating renewal models Lognormal distribution of a time to failure: We use discrete Fourier transformation for: 1. pdf of a sum X f + X r (X r is an exponential time to a repair), as well as for 2. convolution in the following equation: renewal density can be estimated by a finite sum

Alternating renewal models An example: In the following example a calculation for parameter values σ=1/4, λ=8σ, τ =1/2, is done. A renewal density for lognormal distribution Coefficient of availability for lognormal distribution

Alternating renewal models with two types of failures Two different independent failures. These failures can be described by an equal distribution with different parameters or by different distributions. Common repair: A time to a renewal is common for both the failures and begins immediately after one of them. It is described by an exp.distribution, with a mean 1/ τ. For a renewal density we have In case of non-exponential distribution we use and we estimate the function by a sum of the finite number of elements with a fault stated above. f n (t) is a probability density of time to n-th failure. For the calculation of convolutions we can use a quick discrete Fouriers transformation.

Alternating renewal models with two types of failures have Weibull distributions Example: Coefficient of availability for Weibull distribution

Conclusions Selected ageing processes were mathematically modelled by the means of a renewal theory and these models were subsequently solved. Mostly in ageing models the solving of integral equations was not analytically feasible. In this case numerical computations were successfully applied. It was known from the theory that the cases with the exponential probability distribution are analytically easy to solve. With the gained results and gathered experience it would be possible to continue in modelling and solving more complex mathematical models which would precisely describe real problems. For example by the involvement of certain relations which would specify the occurance, or a possible renewal of individual types of failures which in reality do not have to be independent. Equally, it would be practically efficient to continue towards the calculation of optimal maintenance strategies with the set costs connected with failures, exchanges and inspections of individual components of the system and determination of the expected number of these events at a given time interval.