Fault Detection of DCS Central Control Hardware Devices Based on RTE-PCA

Qing-hui Sun
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Abstract

DCS systems have been widely used in industrial production, but often the fault data of their central control systems are very complex and not conducive to troubleshooting and monitoring. Therefore, an excellent data dimensionality reduction method can accelerate the efficiency of fault diagnosis without losing accuracy. In this paper, we use Random Trees Embedding (RTE) algorithm to firstly expand the data with features to make the highly coupled data linearly separable, and then use PCA to reduce the dimensionality of the data with excellent results. Then, the effect of RTE-PCA algorithm with different parameters is discussed.
基于RTE-PCA的DCS中控硬件设备故障检测
DCS系统在工业生产中得到了广泛的应用,但其中央控制系统的故障数据往往非常复杂,不利于故障排除和监控。因此,一种优秀的数据降维方法可以在不损失准确性的前提下提高故障诊断的效率。本文首先利用随机树嵌入(RTE)算法对数据进行特征扩展,使高耦合数据线性可分,然后利用主成分分析法对数据进行降维,取得了很好的效果。然后,讨论了不同参数下RTE-PCA算法的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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