48 research outputs found
Assessment of Resilience in Desalination Infrastructure Using Semi-Markov Models
As the supply of desalinated water becomes significant in many countries, the reliable long-term operation of desalination infrastructure becomes paramount. As it is not realistic to build desalination systems with components that never fail, instead the system should be designed with more resilience. To answer the question how resilient the system should be, we present in this paper a quantitative approach to measure system resilience using semi-Markov models. This approach allows to probabilistically represent the resilience of a desalination system, considering the functional or failed states of its components, as well as the probability of failure and repair rates. As the desalination plants are connected with the end-user through water transportation and distribution networks, this approach also enables an evaluation of various network configurations and resilience strategies. A case study addressing a segment of the water system in Saudi Arabia is given with the results, benefits, and limitations of the technique discussed.Center for Complex Engineering Systems at MIT and KACSTUnited States. National Aeronautics and Space Administration (Space Technology Research Fellowship, grant number NNX14AM42H
A discrete MMAP for analysing the behaviour of a multi-state complex dynamic system subject to multiple events.
A complex multi-state system subject to different types of failures, repairable and/or nonrepairable, external shocks and preventive maintenance is modelled by considering a discrete
Markovian arrival process with marked arrivals (D-MMAP). The internal performance of the
system is composed of several degradation states partitioned into minor and major damage
states according to the risk of failure. Random external events can produce failures throughout
the system. If an external shock occurs, there may be an aggravation of the internal degradation, cumulative external damage or extreme external failure. The internal performance and the
cumulative external damage are observed by random inspection. If major degradation is
observed, the unit goes to the repair facility for preventive maintenance. If a repairable failure
occurs then the system goes to corrective repair with different time distributions depending on
the failure state. Time distributions for corrective repair and preventive maintenance depend on
the failure state. Rewards and costs depending on the state at which the device failed or was
inspected are introduced. The system is modelled and several measures of interest are built into
transient and stationary regimes. A preventive maintenance policy is shown to determine the
effectiveness of preventive maintenance and the optimum state of internal and cumulative
external damage at which preventive maintenance should be taken into account. A numerical
example is presented, revealing the efficacy of the model. Correlations between the numbers of
different events over time and in non-overlapping intervals are calculated. The results are
expressed in algorithmic-matrix form and are implemented computationally with Matlab.Junta de Andalucía, Spain, under the grant FQM307Ministerio de Economía y Competitividad, España, MTM2017-88708-PEuropean Regional Development Fund (ERDF
Management decision making based on Markov reward models for refrigeration system
This paper presents a method for calculation the reliability measures of multi-state supermarket refrigeration system for decision making of system structure, where the system and its components can have different performance levels ranging from perfect functioning to complete failure. The suggested approach presents the Markov reward models for computation of average availability, total number of system’s elements failures and mean time to system failure for multi-state system. Corresponding procedures for reward matrix definition is suggested. A numerical example is presented in order to illustrate the approach