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http://dspace.lib.cranfield.ac.uk/handle/1826/4078

Document Type:  Thesis or dissertation 
Title:  Riskbased reliability allocation at component level in nonrepairable systems by using evolutionary algorithm 
Authors:  Hussain, Syed Adeel 
Supervisors:  Todinov, M. T. Allwood, R. L. 
Issue Date:  Apr2007 
Abstract:  The approach for setting system reliability in the riskbased reliability allocation
(RBRA) method is driven solely by the amount of ‘total losses’ (sum of reliability
investment and risk of failure) associated with a nonrepairable system failure. For a
system consisting of many components, reliability allocation by RBRA
method becomes a very complex combinatorial optimisation problem particularly if
large numbers of alternatives, with different levels of reliability and associated cost,
are considered for each component. Furthermore, the complexity of this problem is
magnified when the relationship between cost and reliability assumed to be nonlinear
and nonmonotone. An optimisation algorithm (OA) is therefore developed in
this research to demonstrate the solution for such difficult problems.
The core design of the OA originates from the fundamental concepts of
basic Evolutionary Algorithms which are well known for emulating Natural process
of evolution in solving complex optimisation problems through computer simulations
of the key genetic operations such as 'reproduction', ‘crossover’ and ‘mutation’.
However, the OA has been designed with significantly different model of evolution
(for identifying valuable parent solutions and subsequently turning them into even
better child solutions) compared to the classical genetic model for ensuring rapid and
efficient convergence of the search process towards an optimum solution. The vital
features of this OA model are 'generation of all populations (samples) with unique
chromosomes (solutions)', 'working exclusively with the elite chromosomes in each
iteration' and 'application of prudently designed genetic operators on the elite
chromosomes with extra emphasis on mutation operation'. For each possible
combination of alternatives, both system reliability and cost of failure is computed by
means of MonteCarlo simulation technique.
For validation purposes, the optimisation algorithm is first applied to
solve an already published reliability optimisation problem with constraint on some
target level of system reliability, which is required to be achieved at a minimum
system cost. After successful validation, the viability of the OA is demonstrated by
showing its application in optimising four different nonrepairable sample systems in view of the risk based reliability allocation method. Each system is assumed to have
discrete choice of component data set, showing monotonically increasing cost and
reliability relationship among the alternatives, and a fixed amount associated with
cost of failure. While this optimisation process is the main objective of the research
study, two variations are also introduced in this process for the purpose of
undertaking parametric studies. To study the effects of changes in the reliability
investment on system reliability and total loss, the first variation involves using a
different choice of discrete data set exhibiting a nonmonotonically increasing
relationship between cost and reliability among the alternatives. To study the effects
of risk of failure, the second variation in the optimisation process is introduced by
means of a different cost of failure amount, associated with a given nonrepairable
system failure.
The optimisation processes show very interesting results between system
reliability and total loss. For instance, it is observed that while maximum reliability
can generally be associated with high total loss and low risk of failure, the minimum
observed value of the total loss is not always associated with minimum system
reliability. Therefore, the results exhibit various levels of system reliability and total
loss with both values showing strong sensitivity towards the selected combination of
component alternatives. The first parametric study shows that second data set (nonmonotone)
creates more opportunities for the optimisation process for producing
better values of the loss function since cheaper components with higher reliabilities
can be selected with higher probabilities. In the second parametric study, it can be
seen that the reduction in the cost of failure amount reduces the size of risk of failure
which also increases the chances of using cheaper components with lower levels of
reliability hence producing lower values of the loss functions.
The research study concludes that the riskbased reliability allocation
method together with the optimisation algorithm can be used as a powerful tool for
highlighting various levels of system reliabilities with associated total losses for any
given system in consideration. This notion can be further extended in selecting
optimal system configuration from various competing topologies. With such
information to hand, reliability engineers can streamline complicated system designs
in view of the required level of system reliability with minimum associated total cost of premature failure. In all cases studied, the run time of the optimisation algorithm
increases linearly with the complexity of the algorithm and due to its unique model
of evolution, it appears to conduct very detailed multidirectional search across the
solution space in fewer generations  a very important attribute for solving the kind
of problem studied in this research. Consequently, it converges rapidly towards
optimum solution unlike the classical genetic algorithm which gradually reaches the
optimum, when successful. The research also identifies key areas for future
development with the scope to expand in various other dimensions due to its
interdisciplinary applications. 
URI:  http://hdl.handle.net/1826/4078 
Appears in Collections:  PhD, EngD and MSc by research theses (School of Applied Sciences)

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