基于模糊粗糙集的电厂设备性能特征还原与权重分配

Xiao-feng Dong, Y. Gu, Kun Yang
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引用次数: 0

摘要

在RCM分析过程中引入基于案例的推理,简化了RCM分析的任务,缩短了分析时间。然而,冗余特征不仅会增加案例的内存,而且会使案例检索算法变得更加复杂。此外,传统的权重分配方法增加了人对案例检索准确性的主观影响。本文将模糊粗糙集算法应用于RCM分析案例推理中相似设备案例检索的特征约简和权值分配。该方法有效地避免了案例中连续特征值离散造成的信息丢失。最后,对蒸汽给水泵的性能特点进行了实例分析。
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Power Plant Equipment Performance Feature Reduction and Weight Allocation Based on Fuzzy Rough Set
Case-based reasoning introduced into RCM analysis process simplifies the tasks of RCM analysis and shortens time. However, redundant features may not only increase the case memory, but also make the case retrieval algorithm more complicated. Additionally, traditional methods of weight allocation increase the human subjective influence on the accuracy of case retrieval. This paper applies fuzzy rough set algorithm in feature reduction and weight allocation which is used for case retrieval of similar equipment in RCM analysis case-based reasoning. This method effectively avoids information loss caused by discretizing continuous feature value in cases. Finally, a case study is implemented to steam feed pump performance features.
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