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首页> 外文期刊>Applied Soft Computing >Predicting uncertain behavior of press unit in a paper industry using artificial bee colony and fuzzy Lambda-Tau methodology
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Predicting uncertain behavior of press unit in a paper industry using artificial bee colony and fuzzy Lambda-Tau methodology

机译：使用人工蜂群和模糊Lambda-Tau方法预测造纸行业压榨单元的不确定行为

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摘要

As the industrial systems are growing complex these-days and data related to the system performance are recorded/collected from various resources under various practical constraints. If the collected data are used as such in the analysis, then they have high range of uncertainties occurred in the analysis and hence performance of the system cannot be done up to desired levels. Thus the main objective of the present work is to remove the uncertainties in the data up to a desired degree of accuracy by utilizing the uncertain, vague and limited data. For analysis of this, an artificial bee colony based Lambda-Tau (ABCBLT) methodology has been used in which expression of the reliability parameters are computed by using Lambda-Tau methodology and their membership functions are formulated by solving a nonlinear optimization problem with artificial bee colony (ABC) algorithm. A time varying failure rate has been used in the analysis instead of constant failure rate. A new RAM-Index has been proposed for ranking the systems' components based on its performance. The technique has been demonstrated through a case study of press unit of a paper industry, situated in Northern part of India, producing 200 tons of paper per day. The results computed by the proposed approach are compared with the Lambda-Tau methodology and concluded that they have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties.
机译：如今，随着工业系统日趋复杂，在各种实际限制下，从各种资源中记录/收集与系统性能相关的数据。如果收集的数据按原样用于分析，则它们在分析中会出现很大范围的不确定性，因此无法将系统性能提高到所需水平。因此，本发明的主要目的是通过利用不确定，模糊且有限的数据来将数据中的不确定性去除至期望的准确度。为了对此进行分析，使用了基于人工蜂群的Lambda-Tau（ABCBLT）方法，其中使用Lambda-Tau方法计算可靠性参数的表达式，并通过解决人工蜂的非线性优化问题来制定其隶属函数殖民地（ABC）算法。分析中使用了时变故障率，而不是恒定故障率。已经提出了一种新的RAM索引，用于根据其性能对系统组件进行排名。该技术已通过位于印度北部的一家造纸工业印刷厂的案例研究得到了证明，该印刷厂每天生产200吨纸。将所提出的方法计算出的结果与Lambda-Tau方法进行比较，得出的结论是，与现有技术区域相比，它们的预测区域减少了，即，减少了分析中的不确定性。因此，它可能是评估当前系统状况和所涉及不确定性的更有用的分析工具。

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