Enhanced Random Forest With Concurrent Analysis of Static and Dynamic Nodes for Industrial Fault Classification
Enhanced Random Forest With Concurrent Analysis of Static and Dynamic Nodes for Industrial Fault Classification
复制标题
工业故障分类静态和动态节点并行分析的增强型随机森林
DOI：
10.1109/tii.2019.2915559
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发表时间：
2020-01
影响因子：
12.3
通讯作者：
Zhao Chunhui
中科院分区：
文献类型：
--
作者：
Chai Zheng;Zhao Chunhui
近年来，机器学习算法已成功应用于工业过程。然而，静态和动态表示的并发分析还没有得到全面解决的工业过程故障分类。针对这一问题，提出了一种同时分析静态和动态节点的增强型随机森林故障分类算法。首先，通过设计一个新的慢度指标，更适合于监督故障分类问题的标准慢特征分析进行修改。其次，进行特征排序过程以确定显著特征。这些特征替代了节点中的原始变量，用于构建增强的随机森林。该方案通过选取重要的静态和动态节点，增强了对节点的区分能力和解释能力。此外，不相关的慢特征比初始相关变量更适合于训练森林，从而全面解决了工业过程的动态特性。田纳西州伊士曼基准和现实世界的三相流过程中的故障分类所提出的方法的应用进行评估。实验结果表明，该方法对16类Tennessee Eastman过程和4类三相流过程的辨识精度分别超过70%和99%，优于传统的学习算法。所选的重要特征表明，静态和动态信息在故障分类中起着重要作用。
In recent years, machine learning algorithms have been successfully applied to industrial processes. However, the concurrent analysis of static and dynamic representations has not been comprehensively addressed for industrial process fault classification. In this paper, an enhanced random forest algorithm with a concurrent analysis of static and dynamic nodes is proposed to address this issue for fault classification. First, the standard slow feature analysis is modified by designing a new slowness index that is more suitable for a supervised fault classification problem. Second, a feature ranking process is conducted to determine the significant features. These features, which substitute the raw variables in the nodes, are used to build the enhanced random forest. Using this scheme, the significant static and dynamic nodes are selected to enhance the discriminative ability and interpretation. Additionally, the slow features that are uncorrelated are more suitable for training the forest than the initial correlated variables, and the dynamic characteristics of industrial processes are thus comprehensively addressed. The application of the proposed method to fault classification is evaluated by both the Tennessee Eastman benchmark and a real-world three-phase flow process. The experimental results show that the proposed method outperforms the traditional learning algorithms with remarkable accuracy and F1 score that both exceed 70% for the 16-class Tennessee Eastman process and exceed 99% for the 4-class three-phase flow process. The selected significant features reveal that both the static and dynamic information play important roles in fault classification.
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