J.Soszyska, P.Dziula, M.Jurdziski, K.KoowrockiOn multi-state safety analysis <strong>in</strong> shipp<strong>in</strong>g - RTA # 3-4, 2007, December - Special Issuebasic systems it is possible to determ<strong>in</strong>e their safetyfunctions. Further, as an example, we will consider aseries system.Def<strong>in</strong>ition 4. A multi-state system is called a seriessystem if it is <strong>in</strong> the safety state subset { u , u + 1,..., z}ifand only if all its components are <strong>in</strong> this subset ofsafety states.Corollary 1. The lifetime T(u) of a multi-state seriessystem <strong>in</strong> the state subset { u , u + 1,..., z}is given byT(u) = m<strong>in</strong>{ ( u)}, u = 1,2,...,z.T i1≤i≤nThe scheme of a series system is given <strong>in</strong> Figure 2.Figure 2. The scheme of a series systemIt is easy to work out the follow<strong>in</strong>g result.Corollary 2. The safety function of the multi-stateseries system is given by⎺s n (t , ⋅ ) = [1,⎺s n (t,1),...,⎺s n (t,z)], t ∈< 0,∞),(8)wheren⎺s n (t,u) = ∏ s ( t,u), t ∈< 0,∞),u = 1,2,...,z. (9)i=1iCorollary 3. If components of the multi-state seriessystem have exponential safety functions, i.e., ifs i (t , ⋅ ) = [1, s i (t,1),..., s i (t,z)], t ∈< 0,∞),wheresi( t,u)= exp[ −λi( u)t]for t ∈< 0,∞),λi( u)> 0 ,u = 1,2,...,z, i = 1,2,...,n,then its safety function is given by⎺s n (t , ⋅ ) = [1,⎺s n (t,1),...,⎺s n (t,z)], (10)whereE 1 E 2 . . . E nn⎺s n (t,u) = exp[ −∑λ ( u)t]for t ∈< 0,∞),(11)u = 1,2,...,z.i=1i4. Basic system safety structures <strong>in</strong> variableoperation conditionsWe assume that the system dur<strong>in</strong>g its operation processhas v different operation states. Thus we can def<strong>in</strong>eZ (t), t ∈< 0 , +∞ > , as the process with discreteoperation states from the setZ = { z1 , z2, . . ., zv},In practice a convenient assumption is that Z(t) is asemi-markov process [3] with its conditional lifetimesθ at the operation state z when its next operationblstate is zl, b , l = 1,2,..., v,b ≠ l.In this case theprocess Z(t) may be described by:- the vector of probabilities of the process <strong>in</strong>itialoperation states [ p b(0)]1xν,- the matrix of the probabilities of the processtransitions between the operation states [ p ] ,bbl νxνwhere p bb( t)= 0 for b = 1,2,...,v.- the matrix of the conditional distribution functions[ H ( t)]of the process lifetimes θ , b ≠ l,<strong>in</strong> theblνxνoperation state zbwhen the next operation state iszl, where Hbl( t)= P(θbl< t)for b , l = 1,2,..., v,b ≠ l,and H bb( t)= 0 for b = 1,2,...,v.Under these assumptions, the lifetimes θblmeanvalues are given byM= θ ] ∫ tdH bl(t), b , l = 1,2,..., v,b ≠ l.(12)blE[ bl= ∞ 0The unconditional distribution functions of thelifetimes θbof the process Z (t)at the operation statesz b = 1,2,...,v,are given byb ,H b(t) = ∑ pvl = 1blHbl( t),b = 1,2,...,v.The mean values E[ θb] of the unconditional lifetimesθbare given byM= θ ] = ∑ p blM blbE[ bvl=1, b = 1,2,...,v,where Mblare def<strong>in</strong>ed by (12).Limit values of the transient probabilities at theoperation statesp b(t) = P(Z(t) = z b) , t ∈< 0 , +∞),b = 1,2,...,v,are given bybl- 42 -
J.Soszyska, P.Dziula, M.Jurdziski, K.KoowrockiOn multi-state safety analysis <strong>in</strong> shipp<strong>in</strong>g - RTA # 3-4, 2007, December - Special IssueπbMbpb= lim p b( t)= ,vt→∞∑πMl=1llb = 1,2,...,v,(13)where the probabilities πbof the vector [ πb ] satisfy1xνthe system of equations⎧[π b ] = [ π⎪⎨ v⎪∑ π l = 1.⎩l=1b][ pbl]We assume that the system is composed of ncomponents E , i = 1,2,...,n,the changes of theiprocess Z(t) operation states have an <strong>in</strong>fluence on thesystem components E safety and on the system safetyistructure as well. Thus, we denote the conditionalsafety function of the system component E while thesystem is at the operational state zb, b = 1,2,...,v,bys b( )( )( t,⋅)i = [1, s b( )( t,1),s b( )( t,2),i i ..., s b( t,z)i ],where( b)i( b)is ( t,u)= P(T ( u)> t Z(t)= zfor t ∈< 0,∞),b = 1,2,...,v,u = 1,2,...,z,and theconditional safety function of the system while thesystem is at the operational state zb, b = 1,2,...,v,by( b)n b( b)n b( b)n bs ( t,⋅)= [1, s ( t,1),s ( t,2),..., s ( t,z)],n b∈{ 1,2,..., n},b)( b)n bwhere nbare numbers of components <strong>in</strong> the operationstates z and( b)n bb( b)s ( t,u),= P ( T ( u)> t Z(t)= z )for t ∈< 0,∞),n b∈ { 1,2,..., n},b = 1,2,...,ν ,u = 1,2,...,z.The safety function( )s b( t,u)is the conditionali( )probability that the component E lifetimeiT bi( u)<strong>in</strong>the state subset { u , u + 1,..., z}is not less than t, whilethe process Z(t) is at the operation state zb. Similarly,( b)n bthe safety function s ( t,u)is the conditional( )probability that the system lifetime T b ( u)<strong>in</strong> the statebisubset { u , u + 1,..., z}is not less than t, while theprocess Z(t) is at the operation state z b.In the case when the system operation time is largeenough, the unconditional safety function of the systemis given bys ( t,⋅)= [1, s (t,1),s (t, 2),..., s ( t,z)], t ≥ 0,nwherens ( t,u)= P ( T(u)> t)≅ ∑ pbs( t,u)nnνb=1( b)nbn(14)for t ≥ 0,n b∈ { 1,2,..., n},u = 1,2,...,z,and T (u)is theunconditional lifetime of the system <strong>in</strong> the safety statesubset { u , u + 1,..., z}.The mean values and variances of the system lifetimes<strong>in</strong> the safety state subset { u , u + 1,..., z}areν( b)m ( u)= E[T(u)]≅ ∑ p m ( u),u = 1,2,...,z,(15)where [2]m( b)b=1b( u)= ∞ ∫ s ( t,u)dt,n b∈ { 1,2,..., n},(16)0u = 1,2,...,z,and( b)( b)n b2= ∞ 0( b)[ σ ( u)]2∫ts( t,u)dt −[m ( u)], (17)u = 1,2,...,z,nbfor b =1,2,...,ν , and2= ∞ 0[ σ ( u)]2∫ts( t,u)dt −[m(u)], u = 1,2,...,z.(16)nThe mean values of the system lifetimes <strong>in</strong> theparticular safety states u , are [2]m ( u)= m(u)− m(u + 1), u = 1,2,...,z −1,m ( z)= m(z).(19)5. Ship safety Model <strong>in</strong> constant operationconditionsWe prelim<strong>in</strong>arily assume that the ship is composed of anumber of ma<strong>in</strong> technical subsystems hav<strong>in</strong>g anessential <strong>in</strong>fluence on its safety. There aredist<strong>in</strong>guished her follow<strong>in</strong>g technical subsystems:22- 43 -
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R.GuoAn univariate DEMR modelling o
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R.GuoAn univariate DEMR modelling o
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R.GuoAn univariate DEMR modelling o
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S.Guze Numerical approach to reliab
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S.Guze Numerical approach to reliab
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S.Guze Numerical approach to reliab
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S.Guze, K.Koowrocki Reliability ana
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S.Guze, K.Koowrocki Reliability ana
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S.Guze, K.Koowrocki Reliability ana
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L.Knopik Some remarks on mean time
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L.Knopik Some remarks on mean time
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K.Koowrocki Reliability modelling o
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A. Kudzys Transformed conditional p
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J. Soszyska Systems reliability ana
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NOTES