3,134 research outputs found

    Component redundancy allocation in optimal cost preventive maintenance scheduling

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    This work presents a methodology to assist maintenance teams in defining the maintenance schedule and redundancy allocation that minimise the life-cycle average cost of a system. The minimal data required are three average costs and one reliability function. This methodology is useful in a system design phase, since in this situation data is usually scarce or inaccurate, but can also be applied in the exploration phase. It consists of an adaptation of the classical optimal age replacement method, combined with a redundancy allocation problem. A set of simple illustrative examples covering a variety of operating conditions is presented, demonstrating quantitatively the applicability of the methodology to a range of maintenance optimisation decisions

    Estimation of component redundancy in optimal age maintenance

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    The classical Optimal Age-Replacement defines the maintenance strategy based on the equipment failure consequences. For severe consequences an early equipment replacement is recommended. For minor consequences the repair after failure is proposed. One way of reducing the failure consequences is the use of redundancies, especially if the equipment failure rate is decreasing over time, since in this case the preventive replacement does not reduce the risk of failure. The estimation of an active component redundancy degree is very important in order to minimize the life-cycle cost. If it is possible to make these estimations in the early phase of system design, the implementation is easier and the amortization faster. This work proposes an adaptation of the Optimal Age-Replacement method in order to simultaneously optimize the equipment redundancy allocation and the maintenance plan. The main goal is to provide a simple methodology, requiring the fewer data possible. A set of examples are presented illustrating that this methodology covers a wide variety of operating conditions. The optimization of the number of repairs between each replacement, in the cases of imperfect repairs, is another feature of this methodology

    A condition-based opportunistic maintenance policy integrated with energy efficiency for two-component parallel systems

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    Purpose: In order to improve the energy utilization and achieve sustainable development, this paper integrates energy efficiency into condition-based maintenance(CBM) decision-making for two-component parallel systems. The objective is to obtain the optimal maintenance policy by minimizing total cost. Design/methodology/approach: Based on energy efficiency, the paper considers the economic dependence between the two components to take opportunistic maintenance. Specifically, the objective function consists of traditional maintenance cost and energy cost incurred by energy consumption of components. In order to assess the performance of the proposed new maintenance policy, the paper uses Monte-Carlo method to evaluate the total cost and find the optimal maintenance policy. Findings: Simulation results indicate that the new maintenance policy is superior to the classical condition-based opportunistic maintenance policy in terms of total economic costs. Originality/value: For two-component parallel systems, previous researches usually simply establish a condition-based opportunistic maintenance model based on real deterioration data, but ignore energy consumption, energy efficiency (EE) and their contributions of sustainable development. This paper creatively takes energy efficiency into condition-based maintenance(CBM) decision-making process, and proposes a new condition-based opportunistic maintenance policy by using energy efficiency indicator(EEI).Peer Reviewe

    Multi-State System Reliability: A New and Systematic Review

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    AbstractReliability analysis considering multiple possible states is known as multi-state (MS) reliability analysis. Multi-state system reliability models allow both the system and its components to assume more than two levels of performance. Through multi-state reliability models provide more realistic and more precise representations of engineering systems, they are much more complex and present major difficulties in system definition and performance evaluation. MSS reliability has received a substantial amount of attention in the past four decades. This article presents a new and systematic review about multi-state system reliability. A timely review is an effective work related to improving the development of MSS theory. The review about the latest studies and advances about multi-state system reliability evaluation, multi-state systems optimization and multi-state systems maintenance is summarized in this paper

    The safety case and the lessons learned for the reliability and maintainability case

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    This paper examine the safety case and the lessons learned for the reliability and maintainability case

    Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

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    This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme

    Space station advanced automation

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    In the development of a safe, productive and maintainable space station, Automation and Robotics (A and R) has been identified as an enabling technology which will allow efficient operation at a reasonable cost. The Space Station Freedom's (SSF) systems are very complex, and interdependent. The usage of Advanced Automation (AA) will help restructure, and integrate system status so that station and ground personnel can operate more efficiently. To use AA technology for the augmentation of system management functions requires a development model which consists of well defined phases of: evaluation, development, integration, and maintenance. The evaluation phase will consider system management functions against traditional solutions, implementation techniques and requirements; the end result of this phase should be a well developed concept along with a feasibility analysis. In the development phase the AA system will be developed in accordance with a traditional Life Cycle Model (LCM) modified for Knowledge Based System (KBS) applications. A way by which both knowledge bases and reasoning techniques can be reused to control costs is explained. During the integration phase the KBS software must be integrated with conventional software, and verified and validated. The Verification and Validation (V and V) techniques applicable to these KBS are based on the ideas of consistency, minimal competency, and graph theory. The maintenance phase will be aided by having well designed and documented KBS software
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