Fault Diagnosis in Industrial Processes by Maximizing Pairwise Kullback-Leibler Divergence
Fault Diagnosis in Industrial Processes by Maximizing Pairwise Kullback-Leibler Divergence
复制标题
通过最大化成对 Kullback-Leibler 散度来诊断工业过程中的故障
DOI：
10.1109/tcst.2019.2950403
复制
发表时间：
2021-03-01
影响因子：
4.8
通讯作者：
Harinath, Eranda
中科院分区：
文献类型：
--
作者：
Lu, Qiugang;Jiang, Benben;Harinath, Eranda
故障诊断因其提高过程安全性和效率的能力而受到越来越多的关注。本文提出了一种用于故障诊断的最大比率散度分析（MRDA）方法，该方法在降维步骤中最大化每对类别之间的成对比率库尔贝克 - 莱布勒（KL）散度。此外，还提出了一种基于收缩技术的迭代算法来学习MRDA的载荷向量。与传统监测方法相比，所提出的基于MRDA的方法具有以下优势，从而提高了故障诊断能力。首先，MRDA最大化每对类别之间的成对比率散度，这直接提高了低维空间中的分类性能。此外，MRDA不太可能受“异常值”类别的主导，因为它的目标是比率散度的平均值，从而使所提出的方法有利于不平衡故障类别的分类。基于MRDA的故障诊断方法的有效性通过田纳西 - 伊斯曼过程（TEP）得到了验证。
Fault diagnosis gains increasing attention for its ability to enhance process safety and efficiency. This brief proposes a maximized ratio divergence analysis (MRDA) approach for fault diagnosis, which maximizes the pairwise ratio Kullback-Leibler (KL) divergence between each pair of classes during the dimensionality reduction step. In addition, an iterative algorithm based on deflation techniques is put forward for learning the loading vectors of MRDA. The proposed MRDA-based approach allows for improved power of fault diagnosis because of the following advantages over classical monitoring methods. First, MRDA maximizes the pairwise ratio divergence between each pair of classes, which directly leads to enhanced classification performance in the low-dimensional space. Moreover, MRDA is less likely to be dominated by "outlier" classes, since its objective is an average of ratio divergence, thereby facilitating the proposed method to be beneficial to the classification of imbalanced faulty classes. The effectiveness of the MRDA-based approach for fault diagnosis is verified by the Tennessee Eastman process (TEP).