Fuzzy Bayesian reliability and availability analysis of production systems

IF 6.7 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Industrial Engineering Pub Date : 2010-11-01 DOI:10.1016/j.cie.2010.07.020
Latife Görkemli, Selda Kapan Ulusoy
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引用次数: 36

Abstract

To have effective production planning and control, it is necessary to calculate the reliability and availability of a production system as a whole. Considering only machine reliability in the calculations would most likely result unmet due dates. In this study, a new modelling approach for determining the reliability and availability of a production system is proposed by considering all the components of the system and their hierarchy in the system structure. Components of a production system are defined as production processes; components of the processes are defined as sub-processes. In this hierarchical structure we could model all kinds of failures such as material and supply, management and personnel, and machine and equipment. In the analysis, a fuzzy Bayesian method is used to quantify the uncertainties in the production environment. The suggested modelling approach is illustrated on an example. In the example, also a separate reliability and availability analysis is conducted which only considered machine failures, and the results of both analyses are compared.

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生产系统的模糊贝叶斯可靠性和可用性分析
为了进行有效的生产计划和控制,有必要对整个生产系统的可靠性和可用性进行计算。在计算中只考虑机器的可靠性,很可能导致无法如期完成。在本研究中,提出了一种新的建模方法,通过考虑系统的所有组件及其在系统结构中的层次结构来确定生产系统的可靠性和可用性。生产系统的组成部分被定义为生产过程;流程的组件被定义为子流程。在这个层次结构中,我们可以模拟各种故障,如材料和供应、管理和人员、机器和设备。在分析中,采用模糊贝叶斯方法对生产环境中的不确定性进行量化。通过一个实例说明了所建议的建模方法。在算例中,还单独进行了仅考虑机器故障的可靠性和可用性分析,并对两种分析结果进行了比较。
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来源期刊
Computers & Industrial Engineering
Computers & Industrial Engineering 工程技术-工程:工业
CiteScore
12.70
自引率
12.70%
发文量
794
审稿时长
10.6 months
期刊介绍: Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.
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