How to stretch system reliability exploiting mission constraints: A practical roadmap for industries

Reliability analysis can be committed to companies by customers willing to verify whether their products comply with the major international standards or simply to verify the design prior of market deployment. Nevertheless, these analyses may be required at the very preliminary stages of design or when the design is already in progress due to low organizational capabilities or simple delay in the project implementation process. The results sometime maybe be far from the market or customer target with a subsequent need to redesign the whole asset. Of course, not all the cases fall in the worst scenario and maybe with some additional consideration on mission definition it is possible to comply with the proposed reliability targets. In this paper the author will provide an overview on the approach which could be followed to achieve the reliability target even when the project is still on-going providing a practical case study

Component Maintenance Strategies and Risk Analysis for Random Shock Effects Considering Maintenance Costs

Maintenance can improve a system’s reliability in a long operation period or when a component has failed. The reliability modeling method that uses the stochastic process degradation model to describe the system degradation process has been widely used. However, the existing reliability models established using stochastic processes only consider the internal degradation process, and do not fully consider the impact of external random shocks on their reliability modeling. Furthermore, the existing theory of importance does not consider the actual factors of maintenance cost. In this paper, based on the reliability modeling of random processes, the degradation rate under the influence of random shocks is introduced into the time scale function to solve the impact of random shocks on product reliability, and two cost importance measures are proposed to guide the maintenance selection of the components under limited resources in the system.Finally, a subsystem of an aircraft hydraulic system is analyzed to verify the proposed method’s performance

Availability modeling and optimization of dynamic multi-state series-parallel systems with random reconfiguration

International audienceMost studies on multi-state series-parallel systems focus on the static type of system architecture. However, it is insufficient to model many complex industrial systems having several operation phases and each requires a subset of the subsystems combined together to perform certain tasks. To bridge this gap, this study takes into account this type of dynamic behavior in the multi-state series-parallel system and proposes an analytical approach to calculate the system availability and the operation cost. In this approach, Markov process is used to model the dynamics of system phase changing and component state changing, Markov reward model is used to calculate the operation cost associated with the dynamics, and universal generating function (UGF) is used to build system availability function from the system phase model and the component models. Based upon these models, an optimization problem is formulated to minimize the total system cost with the constraint that system availability is greater than a desired level. The genetic algorithm is then applied to solve the optimization problem. The proposed modeling and solution procedures are illustrated on a system design problem modified from a real-world maritime oil transportation system