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随着系统结构变得更加复杂,可用性分析成为工业系统设计领域中的一个重要问题。此外,系统的可用性受诸多因素影响,如设计、制造、安装等,所以针对系统的故障行为建模、分析和预测是非常困难的。本文目的是开发一种新的方法,用于计算任何工业系统中的各种性能指标,即可靠性、可用性、MTBF(平均故障间隔时间),ENOF(预期的故障次数),故障率和维修时间。在该方法中,采用遗传算法获得所有组成部件的故障率和维修时间,然后使用模糊λ-tau技术计算各种性能指标。本研究涉及的清洗系统是造纸工业的主要组成部分。运用Petri网络对工作部件之间的相互作用进行建模。使用三角模糊数表示故障率和修复频率,因为他们允许将可靠信息中的专家观点、语言变量、操作条件、不确定性和不精确性纳入系统模型中。基于计算的可靠性参数形成一个结构框架,可帮助维护工程师分析和预测系统行为。
As the system structure becomes more complex, usability analysis becomes an important issue in the field of industrial system design. In addition, the availability of the system is affected by many factors, such as design, manufacture, installation, etc. Therefore, it is very difficult to model, analyze and predict the fault behavior of the system. The purpose of this paper is to develop a new method for calculating various performance metrics in any industrial system, namely reliability, availability, MTBF (Mean Time Between Failure), ENOF (expected number of failures), failure rate and repair time . In this method, genetic algorithms are used to obtain the failure rates and maintenance times of all the components, and then the fuzzy λ-tau technique is used to calculate various performance indexes. The cleaning system involved in this study is a major component of the paper industry. Petri nets are used to model the interaction between working parts. Triangular fuzzy numbers are used to represent the failure rate and repair frequency because they allow the inclusion of expert opinions, linguistic variables, operating conditions, uncertainties and inaccuracies in reliable information into the system model. The calculated reliability parameters form a structural framework that helps maintenance engineers analyze and predict system behavior.